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Roland Siegwart

239 accepted papers

2026

BIEVR-LIO: Robust LiDAR-Inertial Odometry through Bump-Image-Enhanced Voxel Maps

Patrick Pfreundschuh, Turcan Tuna, Cedric Le Gentil, Roland Siegwart, Cesar Cadena, Helen Oleynikova
RSS 2026poster

Reliable odometry is essential for mobile robots as they increasingly enter more challenging environments, which often contain little information to constrain point cloud registration, resulting in degraded LiDAR–Inertial Odometry (LIO) accuracy or even divergence. To address this, we present BIEVR-…

Cited by 0Source ↗Scholar ↗
2026

Depth Completion in Unseen Field Robotics Environments Using Extremely Sparse Depth Measurements

Marco Job, Thomas Stastny, Eleni Kelasidi, Roland Siegwart, Michael Pantic
ICRA 2026poster

Autonomous field robots operating in unstructured environments require robust perception to ensure safe and reliable operations. Recent advances in monocular depth estimation have demonstrated the potential of low-cost cameras as depth sensors; however, their adoption in field robotics remains limit…

Cited by 0Source ↗PDF ↗Scholar ↗
2026

How to Shake Trees with Aerial Manipulators? a Theoretical and Experimental Study

Antonio Gonzalez-Morgado, Eugenio Cuniato, Guillermo Heredia, Anibal Ollero, Roland Siegwart, Marco Tognon
ICRA 2026poster

Aerial manipulators are advancing beyond traditional inspection roles to enable complex interactions with flexible structures. Applications such as structural health monitoring, and especially agricultural tasks like fruit harvesting or environmental monitoring, require inducing controlled vibration…

Cited by 0Source ↗Scholar ↗
2026

Pushing the Limits of Reactive Navigation: Learning to Escape Local Minima

Isar Meijer, Michael Pantic, Helen Oleynikova, Roland Siegwart
ICRA 2026poster

Can a robot navigate a cluttered environment without an explicit map? Reactive methods that use only the robot’s current sensor data and local information are fast and flexible, but prone to getting stuck in local minima. Is there a middle-ground between reactive methods and map-based path planners?…

Cited by 0Source ↗Scholar ↗
2026

Toward Open-Source and Modular Space Systems with ATMOS (I)

Pedro Roque, Sujet Phodapol, Elias Krantz, Jaeyoung Lim, Joris Petrus Martinus Verhagen, Frank J. Jiang +8
ICRA 2026poster

In the near future, most deployed spacecraft will be autonomous. Their tasks will involve autonomous rendezvous and proximity operations (RPOs) with large structures, such as inspection, assembly, and maintenance of orbiting space stations, as well as human-assistance tasks over shared workspaces. Y…

Cited by 0Scholar ↗
2026

Towards Robust Optimization-Based Autonomous Dynamic Soaring With a Fixed-Wing UAV

Marvin Harms, Jaeyoung Lim, David Rohr, Friedrich M. Rockenbauer, Nicholas R. J. Lawrance, Roland Siegwart
RA-L 2026

Dynamic soaring is a flying technique to exploit the energy available in wind shear layers, enabling potentially unlimited flight without the need for internal energy sources. We propose a framework for autonomous dynamic soaring with a fixed-wing uncrewed aerial vehicle (UAV). The framework makes u

Cited by 0Source ↗Scholar ↗
2025

CueLearner: Bootstrapping and local policy adaptation from relative feedback

Giulio Schiavi, Andrei Cramariuc, Lionel Ott, Roland Siegwart
IROS 2025

Human guidance has emerged as a powerful tool for enhancing reinforcement learning (RL). However, conventional forms of guidance such as demonstrations or binary scalar feedback can be challenging to collect or have low information content, motivating the exploration of other forms of human input. A

Cited by 0Source ↗Scholar ↗
2025

Discontinuity-aware Normal Integration for Generic Central Camera Models

Francesco Milano, Manuel López-Antequera, Naina Dhingra, Roland Siegwart, Robert Thiel
ICCV 2025poster

Recovering a 3D surface from its surface normal map, a problem known as normal integration, is a key component for photometric shape reconstruction techniques such as shape-from-shading and photometric stereo. The vast majority of existing approaches for normal integration handle only implicitly the…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Efficient Hierarchical Any-Angle Path Planning on Multi-Resolution 3D Grids

Victor Reijgwart, Cesar Cadena, Roland Siegwart, Lionel Ott
RSS 2025poster

Hierarchical, multi-resolution volumetric mapping approaches are widely used to represent large and complex environments as they can efficiently capture their occupancy and connectivity information. Yet widely used path planning methods such as sampling and trajectory optimization do not exploit thi…

Cited by 0PDF ↗Scholar ↗
2025

Geometric Tracking Control of Omnidirectional Multirotors for Aggressive Maneuvers

Hyungyu Lee, Sheng Cheng, Zhuohuan Wu, Jaeyoung Lim, Roland Siegwart, Naira Hovakimyan
RA-L 2025

An omnidirectional multirotor has the maneuverability of decoupled translational and rotational motions, superseding the traditional multirotors' motion capability. Such maneuverability is achieved due to the ability of the omnidirectional multirotor to frequently alter the thrust amplitude and dire

Cited by 10Source ↗Scholar ↗
2025

How to Shake Trees With Aerial Manipulators? A Theoretical and Experimental Study

Antonio González-Morgado, Eugenio Cuniato, Guillermo Heredia, Aníbal Ollero, Roland Siegwart, Marco Tognon
RA-L 2025

Aerial manipulators are advancing beyond traditional inspection roles to enable complex interactions with flexible structures. Applications such as structural health monitoring, and especially agricultural tasks like fruit harvesting or environmental monitoring, require inducing controlled vibration

Cited by 0Source ↗Scholar ↗
2025

Obstacle-Avoidant Leader Following with a Quadruped Robot

Carmen Scheidemann, Lennart Werner, Victor Reijgwart, Andrei Cramariuc, Joris Chomarat, Jia-Ruei Chiu +2
ICRA 2025

Personal mobile robotic assistants are expected to find wide applications in industry and healthcare. For example, people with limited mobility can benefit from robots helping with daily tasks, or construction workers can have robots perform precision monitoring tasks on-site. However, manually stee

Cited by 12Source ↗Scholar ↗
2025

Pushing the Limits of Reactive Navigation: Learning to Escape Local Minima

Isar Meijer, Michael Pantic, Helen Oleynikova, Roland Siegwart
RA-L 2025

Can a robot navigate a cluttered environment without an explicit map? Reactive methods that use only the robot's current sensor data and local information are fast and flexible, but prone to getting stuck in local minima. Is there a middle-ground between reactive methods and map-based path planners?

Cited by 4Source ↗code ↗Scholar ↗
2025

Traversing Mars: Cooperative Informative Path Planning to Efficiently Navigate Unknown Scenes

Friedrich M. Rockenbauer, Jaeyoung Lim, Marcus Gerhard Müller, Roland Siegwart, Lukas Schmid
RA-L 2025

The ability to traverse an unknown environment is crucial for autonomous robot operations. However, due to the limited sensing capabilities and system constraints, approaching this problem with a single robot agent can be slow, costly, and unsafe. For example, in planetary exploration missions, the

Cited by 7Source ↗code ↗Scholar ↗
2024

COIN-LIO: Complementary Intensity-Augmented LiDAR Inertial Odometry

Patrick Pfreundschuh, Helen Oleynikova, Cesar Cadena, Roland Siegwart, Olov Andersson
ICRA 2024poster

We present COIN-LIO, a LiDAR Inertial Odometry pipeline that tightly couples information from LiDAR intensity with geometry-based point cloud registration. The focus of our work is to improve the robustness of LiDAR-inertial odometry in geometrically degenerate scenarios, like tunnels or flat fields…

Cited by 23Source ↗code ↗Scholar ↗
2024

Energy-Optimized Planning in Non-Uniform Wind Fields with Fixed-Wing Aerial Vehicles

Yufei Duan, Florian Achermann, Jaeyoung Lim, Roland Siegwart
IROS 2024poster

Fixed-wing small uncrewed aerial vehicles (sUAVs) possess the capability to remain airborne for extended durations and traverse vast distances. However, their operation is susceptible to wind conditions, particularly in regions of complex terrain where high wind speeds may push the aircraft beyond i…

Cited by 4Source ↗Scholar ↗
2024

NeuSurfEmb: A Complete Pipeline for Dense Correspondence-based 6D Object Pose Estimation without CAD Models

Francesco Milano, Jen Jen Chung, Hermann Blum, Roland Siegwart, Lionel Ott
IROS 2024poster

State-of-the-art approaches for 6D object pose estimation assume the availability of CAD models and require the user to manually set up physically-based rendering (PBR) pipelines for synthetic training data generation. Both factors limit the application of these methods in real-world scenarios. In t…

Cited by 2Source ↗code ↗Scholar ↗
2024

On Learning Scene-aware Generative State Abstractions for Task-level Mobile Manipulation Planning

Julian Förster, Jen Jen Chung, Lionel Ott, Roland Siegwart
IROS 2024

Task and motion planning (TAMP) is a promising approach for efficient long-horizon manipulation planning, which is a prerequisite for being able to deploy manipulation systems in human-centered environments at scale. TAMP systems often rely on so-called predicates to abstractly describe the world. T

Cited by 1Source ↗code ↗Scholar ↗
2024

Passive Aligning Physical Interaction of Fully-Actuated Aerial Vehicles for Pushing Tasks

Tong Hui, Eugenio Cuniato, Michael Pantic, Marco Tognon, Matteo Fumagalli, Roland Siegwart
ICRA 2024poster

Recently, the utilization of aerial manipulators for performing pushing tasks in non-destructive testing (NDT) applications has seen significant growth. Such operations entail physical interactions between the aerial robotic system and the environment. End-effectors with multiple contact points are…

Cited by 6Source ↗Scholar ↗
2024

Reinforcement Learning for Blind Stair Climbing with Legged and Wheeled-Legged Robots

Simon Chamorro, Victor Klemm, Miguel de La Iglesia Valls, Christopher Pal, Roland Siegwart
ICRA 2024poster

In recent years, legged and wheeled-legged robots have gained prominence for tasks in environments predominantly created for humans across various domains. One significant challenge faced by many of these robots is their limited capability to navigate stairs, which hampers their functionality in mul…

Cited by 5Source ↗Scholar ↗
2024

Safe Low-Altitude Navigation in Steep Terrain With Fixed-Wing Aerial Vehicles

Jaeyoung Lim, Florian Achermann, Rik Girod, Nicholas R. J. Lawrance, Roland Siegwart
RA-L 2024

Fixed-wing aerial vehicles provide an efficient way to navigate long distances or cover large areas for environmental monitoring applications. By design, they also require large open spaces due to limited maneuverability. However, strict regulatory and safety altitude limits constrain the available

Cited by 9Source ↗code ↗Scholar ↗
2024

TULIP: Transformer for Upsampling of LiDAR Point Clouds

Bin Yang, Patrick Pfreundschuh, Roland Siegwart, Marco Hutter, Peyman Moghadam, Vaishakh Patil
CVPR 2024poster

LiDAR Upsampling is a challenging task for the perception systems of robots and autonomous vehicles due to the sparse and irregular structure of large-scale scene contexts. Recent works propose to solve this problem by converting LiDAR data from 3D Euclidean space into an image super-resolution prob…

Cited by 6Source ↗PDF ↗code ↗Scholar ↗
2024

Task Adaptation in Industrial Human-Robot Interaction: Leveraging Riemannian Motion Policies

Mike Allenspach, Michael Pantic, Rik Girod, Lionel Ott, Roland Siegwart
RSS 2024poster

In real-world industrial environments, modern robots often rely on human operators for crucial decision-making and mission synthesis from individual tasks. Effective and safe collaboration between humans and robots requires systems that can adjust their motion to human intentions, enabling dynamic t…

Cited by 0Source ↗PDF ↗Scholar ↗
2024

Under pressure: learning-based analog gauge reading in the wild

Maurits Reitsma, Julian Keller, Kenneth Blomqvist, Roland Siegwart
ICRA 2024poster

We propose an interpretable framework for reading analog gauges that is deployable on real world robotic systems. Our framework splits the reading task into distinct steps, such that we can detect potential failures at each step. Our system needs no prior knowledge of the type of gauge or the range…

Cited by 1Source ↗code ↗Scholar ↗
2024

VIRUS-NeRF - Vision, InfraRed and UltraSonic based Neural Radiance Fields

Nicolaj Schmid, Cornelius Von Einem, Cesar Cadena, Roland Siegwart, Lorenz Hruby, Florian Tschopp
IROS 2024poster

Autonomous mobile robots are an increasingly integral part of modern factory and warehouse operations. Obstacle detection, avoidance and path planning are critical safety-relevant tasks, which are often solved using expensive LiDAR sensors and depth cameras. We propose to use cost-effective low-reso…

Cited by 2Source ↗code ↗Scholar ↗
2024

Watching the Air Rise: Learning-Based Single-Frame Schlieren Detection

Florian Achermann, Julian Andreas Haug, Tobias Zumsteg, Nicholas Lawrance, Jen Jen Chung, Andrey Kolobov +1
ICRA 2024poster

Detecting air flows caused by phenomena such as heat convection is valuable in multiple scenarios, including leak identification and locating thermal updrafts for extending UAV flight duration. Unfortunately, the heat signature of these flows is often too subtle to be seen by a thermal camera. While…

Cited by 0Source ↗Scholar ↗
2024

Waverider: Leveraging Hierarchical, Multi-Resolution Maps for Efficient and Reactive Obstacle Avoidance

Victor Reijgwart, Michael Pantic, Roland Siegwart, Lionel Ott
ICRA 2024

Fast and reliable obstacle avoidance is an important task for mobile robots. In this work, we propose an efficient reactive system that provides high-quality obstacle avoidance while running at hundreds of hertz with minimal resource usage. Our approach combines wavemap, a hierarchical volumetric ma

Cited by 8Source ↗Scholar ↗
2024

Zero123-6D: Zero-shot Novel View Synthesis for RGB Category-level 6D Pose Estimation

Francesco Di Felice, Alberto Remus, Stefano Gasperini, Benjamin Busam, Lionel Ott, Federico Tombari +2
IROS 2024

Estimating the pose of objects through vision is essential to make robotic platforms interact with the environment. Yet, it presents many challenges, often related to the lack of flexibility and generalizability of state-of-the-art solutions. Diffusion models are a cutting-edge neural architecture t

Cited by 13Source ↗Scholar ↗
2024

nvblox: GPU-Accelerated Incremental Signed Distance Field Mapping

Alexander Millane, Helen Oleynikova, Emilie Wirbel, Remo Steiner, Vikram Ramasamy, David Tingdahl +1
ICRA 2024poster

Dense, volumetric maps are essential to enable robot navigation and interaction with the environment. To achieve low latency, dense maps are typically computed onboard the robot, often on computationally constrained hardware. Previous works leave a gap between CPU-based systems for robotic mapping w…

Cited by 28Source ↗Scholar ↗
2023

3D VSG: Long-term Semantic Scene Change Prediction through 3D Variable Scene Graphs

Samuel Looper, Javier Rodriguez-Puigvert, Roland Siegwart, Cesar Cadena, Lukas Schmid
ICRA 2023poster

Numerous applications require robots to operate in environments shared with other agents, such as humans or other robots. However, such shared scenes are typically subject to different kinds of long-term semantic scene changes. The ability to model and predict such changes is thus crucial for robot…

Cited by 27Source ↗code ↗Scholar ↗
2023

A Perching and Tilting Aerial Robot for Precise and Versatile Power Tool Work on Vertical Walls

Roman Dautzenberg, Timo Küster, Timon Mathis, Yann Roth, Curdin Steinauer, Gabriel Käppeli +8
IROS 2023poster

Drilling, grinding, and setting anchors on vertical walls are fundamental processes in everyday construction work. Manually doing these works is error-prone, potentially dangerous, and elaborate at height. Today, heavy mobile ground robots can perform automatic power tool work. However, aerial vehic…

Cited by 14Source ↗Scholar ↗
2023

Baking in the Feature: Accelerating Volumetric Segmentation by Rendering Feature Maps

Kenneth Blomqvist, Lionel Ott, Jen Jen Chung, Roland Siegwart
IROS 2023poster

Methods have recently been proposed that densely segment 3D volumes into classes using only color images and expert supervision in the form of sparse semantically annotated pixels. While impressive, these methods still require a relatively large amount of supervision and segmenting an object can tak…

Cited by 9Source ↗Scholar ↗
2023

Design and Control of a Micro Overactuated Aerial Robot with an Origami Delta Manipulator

Eugenio Cuniato, Christian Geckeler, Maximilian Brunner, Dario Strübin, Elia Bähler, Fabian Ospelt +3
ICRA 2023poster

This work presents the mechanical design and control of a novel small-size and lightweight Micro Aerial Vehicle (MAV) for aerial manipulation. To our knowledge, with a total take-off mass of only 2.0 kg, the proposed system is the most lightweight Aerial Manipulator (AM) that has 8-DOF independently…

Cited by 8Source ↗Scholar ↗
2023

Dynablox: Real-Time Detection of Diverse Dynamic Objects in Complex Environments

Lukas Schmid, Olov Andersson, Aurelio L. Sulser, Patrick Pfreundschuh, Roland Siegwart
RA-L 2023

Real-time detection of moving objects is an essential capability for robots acting autonomously in dynamic environments. We thus propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dynablox</i> , a novel online mapping-based approach for robust m

Cited by 82Source ↗code ↗Scholar ↗
2023

Efficient volumetric mapping of multi-scale environments using wavelet-based compression

Victor Reijgwart, Cesar Cadena, Roland Siegwart, Lionel Ott
RSS 2023poster

Volumetric maps are widely used in robotics due to their desirable properties in applications such as path planning, exploration, and manipulation. Constant advances in mapping technologies are needed to keep up with the improvements in sensor technology, generating increasingly vast amounts of prec…

Cited by 15Source ↗PDF ↗code ↗Scholar ↗
2023

Fisher Information Based Active Planning for Aerial Photogrammetry

Jaeyoung Lim, Nicholas Lawrance, Florian Achermann, Thomas Stastny, Rik Bähnemann, Roland Siegwart
ICRA 2023poster

Small uncrewed aerial systems (sUASs) are useful tools for 3D reconstruction due to their speed, ease of use, and ability to access high-utility viewpoints. Today, most aerial survey approaches generate a preplanned coverage pattern assuming a planar target region. However, this is inefficient since…

Cited by 10Source ↗Scholar ↗
2023

Learning Agent-Aware Affordances for Closed-Loop Interaction with Articulated Objects

Giulio Schiavi, Paula Wulkop, Giuseppe Rizzi, Lionel Ott, Roland Siegwart, Jen Jen Chung
ICRA 2023poster

Interactions with articulated objects are a challenging but important task for mobile robots. To tackle this challenge, we propose a novel closed-loop control pipeline, which integrates manipulation priors from affordance estimation with sampling-based whole-body control. We introduce the concept of…

Cited by 22Source ↗code ↗Scholar ↗
2023

Learning to Open Doors with an Aerial Manipulator

Eugenio Cuniato, Ismail Geles, Weixuan Zhang, Olov Andersson, Marco Tognon, Roland Siegwart
IROS 2023poster

The field of aerial manipulation has seen rapid advances, transitioning from push-and-slide tasks to interaction with articulated objects. The motion trajectory of these complex actions is usually hand-crafted or a result of online optimization methods like Model Predictive Control (MPC) or Model Pr…

Cited by 4Source ↗Scholar ↗
2023

Local and Global Information in Obstacle Detection on Railway Tracks

Matthias Brucker, Andrei Cramariuc, Cornelius Von Einem, Roland Siegwart, Cesar Cadena
IROS 2023poster

Reliable obstacle detection on railways could help prevent collisions that result in injuries and potentially damage or derail the train. Unfortunately, generic object detectors do not have enough classes to account for all possible scenarios, and datasets featuring objects on railways are challengi…

Cited by 11Source ↗Scholar ↗
2023

Material-Agnostic Shaping of Granular Materials with Optimal Transport

Nikhilesh Alatur, Olov Andersson, Roland Siegwart, Lionel Ott
IROS 2023poster

From construction materials, such as sand or asphalt, to kitchen ingredients, like rice, sugar, or salt; the world is full of granular materials. Despite impressive progress in robotic manipulation of single objects, granular materials remain a challenge due to difficulties in modelling these highly…

Cited by 0Source ↗Scholar ↗
2023

Multi-Agent Path Integral Control for Interaction-Aware Motion Planning in Urban Canals

Lucas Streichenberg, Elia Trevisan, Jen Jen Chung, Roland Siegwart, Javier Alonso-Mora
ICRA 2023poster

Autonomous vehicles that operate in urban environments shall comply with existing rules and reason about the interactions with other decision-making agents. In this paper, we introduce a decentralized and communication-free interaction-aware motion planner and apply it to Autonomous Surface Vessels…

Cited by 18Source ↗code ↗Scholar ↗
2023

NeRFing it: Offline Object Segmentation Through Implicit Modeling

Kenneth Blomqvist, Jen Jen Chung, Lionel Ott, Roland Siegwart
ICRA 2023poster

Most recently proposed methods for robotic per-ception are based on deep learning, which require very large datasets to perform well. The accuracy of a learned model is mainly dependent on the data distribution it was trained on. Thus for deploying such models, it is crucial to use training data bel…

Cited by 1Source ↗Scholar ↗
2023

Neural Implicit Vision-Language Feature Fields

Kenneth Blomqvist, Francesco Milano, Jen Jen Chung, Lionel Ott, Roland Siegwart
IROS 2023poster

Recently, groundbreaking results have been presented on open-vocabulary semantic image segmentation. Such methods segment each pixel in an image into arbitrary categories provided at run-time in the form of text prompts, as opposed to a fixed set of classes defined at training time. In this work, we…

Cited by 12Source ↗code ↗Scholar ↗
2023

Obstacle avoidance using Raycasting and Riemannian Motion Policies at kHz rates for MAVs

Michael Pantic, Isar Meijer, Rik Bähnemann, Nikhilesh Alatur, Olov Andersson, Cesar Cadena +2
ICRA 2023poster

This paper presents a novel method for using Riemannian Motion Policies on volumetric maps, shown in the example of obstacle avoidance for Micro Aerial Vehicles (MAVs), Today, most robotic obstacle avoidance algorithms rely on sampling or optimization-based planners with volumetric maps. However, th…

Cited by 16Source ↗code ↗Scholar ↗
2023

On the programming effort required to generate Behavior Trees and Finite State Machines for robotic applications

Matteo Iovino, Julian Förster, Pietro Falco, Jen Jen Chung, Roland Siegwart, Christian Smith
ICRA 2023poster

In this paper we provide a practical demonstration of how the modularity in a Behavior Tree (BT) decreases the effort in programming a robot task when compared to a Finite State Machine (FSM). In recent years the way to represent a task plan to control an autonomous agent has been shifting from the…

Cited by 36Source ↗Scholar ↗
2023

Resilient Terrain Navigation with a 5 DOF Metal Detector Drone

Patrick Pfreundschuh, Rik Bähnemann, Tim Kazik, Thomas Mantel, Roland Siegwart, Olov Andersson
ICRA 2023poster

Micro aerial vehicles (MAVs) hold the potential for performing autonomous and contactless land surveys for the detection of landmines and explosive remnants of war (ERW). Metal detectors are the standard detection tool but must be operated close to and parallel to the terrain. A successful combinati…

Cited by 3Source ↗Scholar ↗
2023

SphNet: A Spherical Network for Semantic Pointcloud Segmentation

Lukas Bernreiter, Lionel Ott, Roland Siegwart, Cesar Cadena
ICRA 2023poster

Semantic segmentation for robotic systems can enable a wide range of applications, from self-driving cars and augmented reality systems to domestic robots. We argue that a spherical representation is a natural one for egocentric pointclouds. Thus, in this work, we present a novel framework exploitin…

Cited by 2Source ↗Scholar ↗
2023

Unsupervised Continual Semantic Adaptation Through Neural Rendering

Zhizheng Liu, Francesco Milano, Jonas Frey, Roland Siegwart, Hermann Blum, Cesar Cadena
CVPR 2023poster

An increasing amount of applications rely on data-driven models that are deployed for perception tasks across a sequence of scenes. Due to the mismatch between training and deployment data, adapting the model on the new scenes is often crucial to obtain good performance. In this work, we study conti…

Cited by 17Source ↗PDF ↗code ↗Scholar ↗
2023

maplab 2.0 - A Modular and Multi-Modal Mapping Framework

Andrei Cramariuc, Lukas Bernreiter, Florian Tschopp, Marius Fehr, Victor Reijgwart, Juan I. Nieto +2
RA-L 2023

Integration of multiple sensor modalities and deep learning into Simultaneous Localization And Mapping (SLAM) systems are areas of significant interest in current research. Multi-modality is a stepping stone towards achieving robustness in challenging environments and interoperability of heterogeneo

Cited by 77Source ↗code ↗Scholar ↗
2022

A Planning-and-Control Framework for Aerial Manipulation of Articulated Objects

Maximilian Brunner, Giuseppe Rizzi, Matthias Studiger, Roland Siegwart, Marco Tognon
RA-L 2022

While the variety of applications for Aerial Manipulators (AMs) has increased over the last years, they are mostly limited to push-and-slide tasks. More complex manipulations of dynamic environments are poorly addressed and still require handcrafted designs of hardware, control, and trajectory plann

Cited by 45Source ↗Scholar ↗
2022

Adaptive Tank-based Control for Aerial Physical Interaction with Uncertain Dynamic Environments Using Energy-Task Estimation

Federico Benzi, Maximilian Brunner, Marco Tognon, Cristian Secchi, Roland Siegwart
RA-L 2022

While aerial manipulation has witnessed noticeable growth as a field in the last decade, most works investigated forms of interaction with static and rigid environments only. Whenever dynamic environments were considered, the employed methods often relied on the knowledge of the model of the environ

Cited by 33Source ↗Scholar ↗
2022

Aerial Layouting: Design and Control of a Compliant and Actuated End-Effector for Precise In-flight Marking on Ceilings

Christian Lanegger, Marco Ruggia, Marco Tognon, Lionel Ott, Roland Siegwart
RSS 2022poster

Aerial robots have demonstrated impressive feats of precise control, such as dynamic flight through openings or highly complex choreographies. Despite the accuracy needed for these tasks, there are problems that require levels of precision that are challenging to achieve today. One such problem is a…

Cited by 12Source ↗PDF ↗Scholar ↗
2022

Autonomous Teamed Exploration of Subterranean Environments using Legged and Aerial Robots

Mihir Kulkarni, Mihir Dharmadhikari, Marco Tranzatto, Samuel Zimmermann, Victor Reijgwart, Paolo De Petris +7
ICRA 2022poster

This paper presents a novel strategy for autonomous teamed exploration of subterranean environments using legged and aerial robots. Tailored to the fact that subterranean settings, such as cave networks and underground mines, often involve complex, large-scale and multi-branched topologies, while wi…

Cited by 112Source ↗code ↗Scholar ↗
2022

Closed-Loop Next-Best-View Planning for Target-Driven Grasping

Michel Breyer, Lionel Ott, Roland Siegwart, Jen Jen Chung
IROS 2022poster

Picking a specific object from clutter is an essential component of many manipulation tasks. Partial observations often require the robot to collect additional views of the scene before attempting a grasp. This paper proposes a closed-loop next-best-view planner that drives exploration based on occl…

Cited by 29Source ↗code ↗Scholar ↗
2022

Collaborative Robot Mapping using Spectral Graph Analysis

Lukas Bernreiter, Shehryar Khattak, Lionel Ott, Roland Siegwart, Marco Hutter, Cesar Cadena
ICRA 2022poster

In this paper, we deal with the problem of creating globally consistent pose graphs in a centralized multi-robot SLAM framework. For each robot to act autonomously, individual onboard pose estimates and maps are maintained, which are then communicated to a central server to build an optimized global…

Cited by 15Source ↗Scholar ↗
2022

Continual Adaptation of Semantic Segmentation Using Complementary 2D-3D Data Representations

Jonas Frey, Hermann Blum, Francesco Milano, Roland Siegwart, Cesar Cadena
RA-L 2022

Semantic segmentation networks are usually pre-trained once and not updated during deployment. As a consequence, misclassifications commonly occur if the distribution of the training data deviates from the one encountered during the robot's operation. We propose to mitigate this problem by adapting

Cited by 16Source ↗Scholar ↗
2022

Don't Share My Face: Privacy Preserving Inpainting for Visual Localization

Saad Himmi, Oguzhan Ilter, François Pailleau, Roland Siegwart, Berta Bescos, Cesar Cadena
IROS 2022poster

Visual localization is an important task for many robotic and augmented reality applications. As localizing within large scale maps can be memory and computationally de-manding, cloud-based localization services are appealing for developers. However, such services raise important privacy concerns fo…

Cited by 4Source ↗Scholar ↗
2022

Embodied Active Domain Adaptation for Semantic Segmentation via Informative Path Planning

René Zurbrügg, Hermann Blum, Cesar Cadena, Roland Siegwart, Lukas Schmid
RA-L 2022

This work presents an embodied agent that can adapt its semantic segmentation network to new indoor environments in a fully autonomous way. Because semantic segmentation networks fail to generalize well to unseen environments, the agent collects images of the new environment which are then used for

Cited by 23Source ↗code ↗Scholar ↗
2022

Energy Tank-Based Policies for Robust Aerial Physical Interaction with Moving Objects

Maximilian Brunner, Livio Giacomini, Roland Siegwart, Marco Tognon
ICRA 2022poster

Although manipulation capabilities of aerial robots greatly improved in the last decade, only few works addressed the problem of aerial physical interaction with dynamic environments, proposing strongly model-based approaches. However, in real scenarios, modeling the environment with high accuracy i…

Cited by 27Source ↗Scholar ↗
2022

Fast and Compute-Efficient Sampling-Based Local Exploration Planning via Distribution Learning

Lukas Schmid, Chao Ni, Yuliang Zhong, Roland Siegwart, Olov Andersson
RA-L 2022

Exploration is a fundamental problem in robotics. While sampling-based planners have shown high performance and robustness, they are oftentimes compute intensive and can exhibit high variance. To this end, we propose to learn both components of sampling-based exploration. We present a method to dire

Cited by 22Source ↗code ↗Scholar ↗
2022

FlowBot: Flow-based Modeling for Robot Navigation

Daniel Dugas, Kuanqi Cai, Olov Andersson, Nicholas Lawrance, Roland Siegwart, Jen Jen Chung
IROS 2022poster

Autonomous navigation among people is a com-plex problem that also exhibits considerable variation depending on the type of environment and people involved. Here we consider navigation among crowds that exhibit flow-like behavior like people moving through a train station. We propose a novel pseudo-…

Cited by 4Source ↗Scholar ↗
2022

Human-State-Aware Controller for a Tethered Aerial Robot Guiding a Human by Physical Interaction

Mike Allenspach, Yash Vyas, Matthias Rubio, Roland Siegwart, Marco Tognon
RA-L 2022

With the rapid development of Aerial Physical Interaction, the possibility to have aerial robots physically interacting with humans is attracting a growing interest. In one of our previous works [1], we considered one of the first systems in which a human is physically connected to an aerial vehicle

Cited by 19Source ↗Scholar ↗
2022

Learning Variable Impedance Control for Aerial Sliding on Uneven Heterogeneous Surfaces by Proprioceptive and Tactile Sensing

Weixuan Zhang, Lionel Ott, Marco Tognon, Roland Siegwart
RA-L 2022

The recent development of novel aerial vehicles capable of physically interacting with the environment leads to new applications such as contact-based inspection. These tasks require the robotic system to exchange forces with partially-known environments, which may contain uncertainties including un

Cited by 28Source ↗Scholar ↗
2022

NavDreams: Towards Camera-Only RL Navigation Among Humans

Daniel Dugas, Olov Andersson, Roland Siegwart, Jen Jen Chung
IROS 2022poster

Autonomously navigating a robot in everyday crowded spaces requires solving complex perception and planning challenges. When using only monocular image sensor data as input, classical two-dimensional planning approaches cannot be used. While images present a significant challenge when it comes to pe…

Cited by 17Source ↗code ↗Scholar ↗
2022

Panoptic Multi-TSDFs: a Flexible Representation for Online Multi-resolution Volumetric Mapping and Long-term Dynamic Scene Consistency

Lukas Schmid, Jeffrey Delmerico, Johannes L. Schönberger, Juan Nieto, Marc Pollefeys, Roland Siegwart +1
ICRA 2022poster

For robotic interaction in environments shared with other agents, access to volumetric and semantic maps of the scene is crucial. However, such environments are inevitably subject to long-term changes, which the map needs to account for. We thus propose panoptic multi-TSDFs as a novel representation…

Cited by 74Source ↗code ↗Scholar ↗
2022

Power-Based Safety Layer for Aerial Vehicles in Physical Interaction Using Lyapunov Exponents

Eugenio Cuniato, Nicholas R. J. Lawrance, Marco Tognon, Roland Siegwart
RA-L 2022

As the performance of autonomous systems increases, safety concerns arise, especially when operating in non-structured environments. To deal with these concerns, this work presents a safety layer for mechanical systems that detects and responds to unstable dynamics caused by external disturbances. T

Cited by 22Source ↗Scholar ↗
2022

Reactive Motion Planning for Rope Manipulation and Collision Avoidance using Aerial Robots

Liping Shi, Michael Pantic, Olov Andersson, Marco Tognon, Roland Siegwart, Rune Hylsberg Jacobsen
IROS 2022poster

In this work we address the challenging problem of manipulating a flexible link, like a rope, with an aerial robot. Inspired by spraying tasks in construction and maintenance scenarios, we consider the case in which an autonomous end-effector (e.g., a spray nozzle moved by a robot or a human operato…

Cited by 6Source ↗Scholar ↗
2022

See Yourself in Others: Attending Multiple Tasks for Own Failure Detection

Boyang Sun, Jiaxu Xing, Hermann Blum, Roland Siegwart, Cesar Cadena
ICRA 2022poster

Autonomous robots deal with unexpected scenarios in real environments. Given input images, various visual perception tasks can be performed, e.g., semantic segmentation, depth estimation and normal estimation. These different tasks provide rich information for the whole robotic perception system. Al…

Cited by 12Source ↗code ↗Scholar ↗
2022

Towards 6DoF Bilateral Teleoperation of an Omnidirectional Aerial Vehicle for Aerial Physical Interaction

Mike Allenspach, Nicholas Lawrance, Marco Tognon, Roland Siegwart
ICRA 2022poster

Bilateral teleoperation offers an intriguing solution towards shared autonomy with aerial vehicles in contact-based inspection and manipulation tasks. Omnidirectional aerial robots allow for full pose operations, making them particularly attractive in such tasks. Naturally, the question arises wheth…

Cited by 17Source ↗Scholar ↗
2022

Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning

Yunke Ao, Le Chen, Florian Tschopp, Michel Breyer, Roland Siegwart, Andrei Cramariuc
ICRA 2022poster

Visual-inertial sensors have a wide range of applications in robotics. However, good performance often requires different sophisticated motion routines to accurately calibrate camera intrinsics and inter-sensor extrinsics. This work presents a novel formulation to learn a motion policy to be execute…

Cited by 4Source ↗code ↗Scholar ↗
2022

Visual Loop Closure Detection for a Future Mars Science Helicopter

Alexander Dietsche, Lionel Ott, Roland Siegwart, Roland Brockers
RA-L 2022

Future Mars Rotorcrafts will require the ability to precisely navigate to previously visited locations in order to return to a safe landing site or execute precise scientific measurements, such as sample acquisition or targeted sensing. To enable a future Mars Science Helicopter to perform in-flight

Cited by 0Source ↗Scholar ↗
2021

3D3L: Deep Learned 3D Keypoint Detection and Description for LiDARs

Dominc Streiff, Lukas Bernreiter, Florian Tschopp, Marius Fehr, Roland Siegwart
ICRA 2021poster

With the advent of powerful, light-weight 3D LiDARs, they have become the hearth of many navigation and SLAM algorithms on various autonomous systems. Pointcloud registration methods working with unstructured pointclouds such as ICP are often computationally expensive or require a good initial guess…

Cited by 19Source ↗code ↗Scholar ↗
2021

A Unified Approach for Autonomous Volumetric Exploration of Large Scale Environments Under Severe Odometry Drift

Lukas Schmid, Victor Reijgwart, Lionel Ott, Juan I. Nieto, Roland Siegwart, César Cadena
RA-L 2021

Exploration is a fundamental problem in robot autonomy. A major limitation, however, is that during exploration robots oftentimes have to rely on on-board systems alone for state estimation, accumulating significant drift over time in large environments. Drift can be detrimental to robot safety and

Cited by 36Source ↗code ↗Scholar ↗
2021

Active Model Learning using Informative Trajectories for Improved Closed-Loop Control on Real Robots

Weixuan Zhang, Marco Tognon, Lionel Ott, Roland Siegwart, Juan Nieto
ICRA 2021poster

Model-based controllers on real robots require accurate knowledge of the system dynamics to perform optimally. For complex dynamics, first-principles modeling is not sufficiently precise, and data-driven approaches can be leveraged to learn a statistical model from real experiments. However, the eff…

Cited by 11Source ↗Scholar ↗
2021

Crowd against the machine: A simulation-based benchmark tool to evaluate and compare robot capabilities to navigate a human crowd

Fabien Grzeskowiak, David Gonon, Daniel Dugas, Diego Paez-Granados, Jen Jen Chung, Juan Nieto +4
ICRA 2021poster

The evaluation of robot capabilities to navigate human crowds is essential to conceive new robots intended to operate in public spaces. This paper initiates the development of a benchmark tool to evaluate such capabilities; our long term vision is to provide the community with a simulation tool that…

Cited by 23Source ↗Scholar ↗
2021

Direct Force and Pose NMPC with Multiple Interaction Modes for Aerial Push-and-Slide Operations

Lazar Peric, Maximilian Brunner, Karen Bodie, Marco Tognon, Roland Siegwart
ICRA 2021poster

In this paper, we present a model predictive controller for a fully actuated aerial manipulator to track a hybrid force and pose trajectory at the end-effector in an aerial interaction task. A force sensor at the end-effector is used to detect contact and to directly control the interaction force. W…

Cited by 29Source ↗Scholar ↗
2021

Distributed PDOP Coverage Control: Providing Large-Scale Positioning Service Using a Multi-Robot System

Liang Zhang, Zexu Zhang, Roland Siegwart, Jen Jen Chung
RA-L 2021

This manuscript addresses the active positioning service using a multi-robot system (MRS) for providing large-scale coverage and scalability in terms of MRS size. Inspired by the coverage control problems from Wireless Sensor Network (WSN) literature, we propose a gradient-based control method where

Cited by 22Source ↗Scholar ↗
2021

Dynamic Object Aware LiDAR SLAM based on Automatic Generation of Training Data

Patrick Pfreundschuh, Hubertus F.C. Hendrikx, Victor Reijgwart, Renaud Dubé, Roland Siegwart, Andrei Cramariuc
ICRA 2021poster

Highly dynamic environments, with moving objects such as cars or humans, can pose a performance challenge for LiDAR SLAM systems that assume largely static scenes. To overcome this challenge and support the deployment of robots in real world scenarios, we propose a complete solution for a dynamic ob…

Cited by 99Source ↗Scholar ↗
2021

Dynamic-Aware Autonomous Exploration in Populated Environments

Valentina Cavinato, Thomas Eppenberger, Dina Youakim, Roland Siegwart, Renaud Dubé
ICRA 2021poster

Autonomous exploration allows mobile robots to navigate in initially unknown territories in order to build complete representations of the environments. In many real-life applications, environments often contain dynamic obstacles which can compromise the exploration process by temporarily blocking p…

Cited by 11Source ↗Scholar ↗
2021

Efficient Multi-scale POMDPs for Robotic Object Search and Delivery

Luc Holzherr, Julian Förster, Michel Breyer, Juan Nieto, Roland Siegwart, Jen Jen Chung
ICRA 2021poster

We present a novel hierarchical POMDP framework to solve an object search and delivery task where the agent is given a prior belief about the possible item locations. Solving POMDPs is computationally demanding and, as such, applications have typically been limited to small environments. The propose…

Cited by 9Source ↗Scholar ↗
2021

Fast Image-Anomaly Mitigation for Autonomous Mobile Robots

Gianmario Fumagalli, Yannick Huber, Marcin Dymczyk, Roland Siegwart, Renaud Dubé
IROS 2021poster

Camera anomalies like rain or dust can severely degrade image quality and its related tasks, such as localization and segmentation. In this work we address this important issue by implementing a pre-processing step that can effectively mitigate such artifacts in a real-time fashion, thus supporting…

Cited by 2Source ↗Scholar ↗
2021

Hough$2$Map - Iterative Event-Based Hough Transform for High-Speed Railway Mapping

Florian Tschopp, Cornelius von Einem, Andrei Cramariuc, David Hug, Andrew William Palmer, Roland Siegwart +2
RA-L 2021

To cope with the growing demand for transportation on the railway system, accurate, robust, and high-frequency positioning is required to enable a safe and efficient utilization of the existing railway infrastructure. As a basis for a localization system we propose a complete on-board mapping pipeli

Cited by 22Source ↗code ↗Scholar ↗
2021

Learn to Path: Using neural networks to predict Dubins path characteristics for aerial vehicles in wind

Trevor Phillips, Maximilian Stölzle, Erick Turricelli, Florian Achermann, Nicholas Lawrance, Roland Siegwart +1
ICRA 2021poster

For asymptotically optimal sampling-based path planners such as RRT*, path quality improves as the number of samples added to the motion tree increases. However, each additional sample requires a nearest-neighbor search. Calculating state transition costs can be particularly difficult in cases with…

Cited by 2Source ↗Scholar ↗
2021

Mesh Manifold Based Riemannian Motion Planning for Omnidirectional Micro Aerial Vehicles

Michael Pantic, Lionel Ott, Cesar Cadena, Roland Siegwart, Juan I. Nieto
RA-L 2021

This letter presents a novel on-line path planning method that enables aerial robots to interact with surfaces. We present a solution to the problem of finding trajectories that drive a robot towards a surface and move along it. Triangular meshes are used as a surface map representation that is free

Cited by 14Source ↗Scholar ↗
2021

Multi-Resolution Elevation Mapping and Safe Landing Site Detection with Applications to Planetary Rotorcraft

Pascal Schoppmann, Pedro F. Proença, Jeff Delaune, Michael Pantic, Timo Hinzmann, Larry Matthies +2
IROS 2021poster

In this paper, we propose a resource-efficient approach to provide an autonomous UAV with an on-board perception method to detect safe, hazard-free landing sites during flights over complex 3D terrain. We aggregate 3D measurements acquired from a sequence of monocular images by a Structure-from-Moti…

Cited by 17Source ↗Scholar ↗
2021

NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human Environments

Daniel Dugas, Juan Nieto, Roland Siegwart, Jen Jen Chung
ICRA 2021poster

Robot navigation is a task where reinforcement learning approaches are still unable to compete with traditional path planning. State-of-the-art methods differ in small ways, and do not all provide reproducible, openly available implementations. This makes comparing methods a challenge. Recent resear…

Cited by 71Source ↗code ↗Scholar ↗
2021

Nonlinear Model Predictive Velocity Control of a VTOL Tiltwing UAV

David Rohr, Matthias Studiger, Thomas Stastny, Nicholas R. J. Lawrance, Roland Siegwart
RA-L 2021

This letter presents the modeling, system identification and nonlinear model predictive control (NMPC) design for longitudinal, full envelope velocity control of a small tiltwing hybrid unmanned aerial vehicle (H-UAV). A first-principles based dynamics model is derived and identified from flight dat

Cited by 30Source ↗Scholar ↗
2021

Online Informative Path Planning for Active Information Gathering of a 3D Surface

Hai Zhu, Jen Jen Chung, Nicholas R.J. Lawrance, Roland Siegwart, Javier Alonso-Mora
ICRA 2021poster

This paper presents an online informative path planning approach for active information gathering on three-dimensional surfaces using aerial robots. Most existing works on surface inspection focus on planning a path offline that can provide full coverage of the surface, which inherently assumes the…

Cited by 58Source ↗Scholar ↗
2021

PHASER: A Robust and Correspondence-Free Global Pointcloud Registration

Lukas Bernreiter, Lionel Ott, Juan I. Nieto, Roland Siegwart, Cesar Cadena
RA-L 2021

We propose PHASER, a correspondence-free global registration of sensor-centric pointclouds that is robust to noise, sparsity, and partial overlaps. Our method can seamlessly handle multimodal information, and does not rely on keypoint nor descriptor preprocessing modules. By exploiting properties of

Cited by 37Source ↗code ↗Scholar ↗
2021

Pixel-Wise Anomaly Detection in Complex Driving Scenes

Giancarlo Di Biase, Hermann Blum, Roland Siegwart, Cesar Cadena
CVPR 2021poster

The inability of state-of-the-art semantic segmentation methods to detect anomaly instances hinders them from being deployed in safety-critical and complex applications, such as autonomous driving. Recent approaches have focused on either leveraging segmentation uncertainty to identify anomalous are…

Cited by 173PDF ↗code ↗Scholar ↗
2021

SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation

Robin Chan, Krzysztof Lis, Svenja Uhlemeyer, Hermann Blum, Sina Honari, Roland Siegwart +3
NeurIPS 2021poster

State-of-the-art semantic or instance segmentation deep neural networks (DNNs) are usually trained on a closed set of semantic classes. As such, they are ill-equipped to handle previously-unseen objects. However, detecting and localizing such objects is crucial for safety-critical applications such…

Cited by 155Source ↗code ↗Scholar ↗
2021

Self-Improving Semantic Perception for Indoor Localisation

Hermann Blum, Francesco Milano, René Zurbrügg, Roland Siegwart, Cesar Cadena, Abel Gawel
CoRL 2021poster

We propose a novel robotic system that can improve its perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fixed models, we propose a framework in which semantic models are continuously updated on the robot to adapt to the deploy…

Cited by 8Source ↗code ↗Scholar ↗
2021

SemSegMap – 3D Segment-based Semantic Localization

Andrei Cramariuc, Florian Tschopp, Nikhilesh Alatur, Stefan Benz, Tillmann Falck, Marius Brühlmeier +3
IROS 2021poster

Localization is an essential task for mobile autonomous robotic systems that want to use pre-existing maps or create new ones in the context of SLAM. Today, many robotic platforms are equipped with high-accuracy 3D LiDAR sensors, which allow a geometric mapping, and cameras able to provide semantic…

Cited by 33Source ↗Scholar ↗
2021

Spherical Multi-Modal Place Recognition for Heterogeneous Sensor Systems

Lukas Bernreiter, Lionel Ott, Juan Nieto, Roland Siegwart, Cesar Cadena
ICRA 2021poster

In this paper, we propose a robust end-to-end multi-modal pipeline for place recognition where the sensor systems can differ from the map building to the query. Our approach operates directly on images and LiDAR scans without requiring any local feature extraction modules. By projecting the sensor d…

Cited by 23Source ↗code ↗Scholar ↗
2021

TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction

Margarita Grinvald, Federico Tombari, Roland Siegwart, Juan Nieto
ICRA 2021poster

The ability to simultaneously track and reconstruct multiple objects moving in the scene is of the utmost importance for robotic tasks such as autonomous navigation and interaction. Virtually all of the previous attempts to map multiple dynamic objects have evolved to store individual objects in sep…

Cited by 35Source ↗code ↗Scholar ↗
2021

Voxplan: A 3D Global Planner using Signed Distance Function Submaps

Laura Gasser, Alexander Millane, Victor Reijgwart, Rik Bähnemann, Roland Siegwart
ICRA 2021poster

The ability to safely navigate through complex and cluttered environments is required for a wide range of robotics applications. This paper introduces a framework to compute safe global paths in maps represented as collections of 3D Signed Distance Function (SDF) submaps. Such maps are able to maint…

Cited by 3Source ↗Scholar ↗
2020

A Connectivity-Prediction Algorithm and its Application in Active Cooperative Localization for Multi-Robot Systems

Liang Zhang, Zexu Zhang, Roland Siegwart, Jen Jen Chung
ICRA 2020poster

This paper presents a method for predicting the probability of future connectivity between mobile robots with range-limited communication. In particular, we focus on its application to active motion planning for cooperative localization (CL). The probability of connection is modeled by the distribut…

Cited by 4Source ↗Scholar ↗
2020

A Data-driven Planning Framework for Robotic Texture Painting on 3D Surfaces

Anurag Sai Vempati, Roland Siegwart, Juan Nieto
ICRA 2020poster

Painting textures on 3D surfaces requires an understanding of the surface geometry, paint flow and paint mixing. This work formulates automated painting as a planning problem and proposes a solution based on a self-supervised learning framework that enables a robot to paint monochromatic non-uniform…

Cited by 9Source ↗Scholar ↗
2020

Accurate Mapping and Planning for Autonomous Racing

Leiv Andresen, Adrian Brandemuehl, Alex Honger, Benson Kuan, Niclas Vödisch, Hermann Blum +8
IROS 2020poster

This paper presents the perception, mapping, and planning pipeline implemented on an autonomous race car. It was developed by the 2019 AMZ driverless team for the Formula Student Germany (FSG) 2019 driverless competition, where it won 1st place overall. The presented solution combines early fusion o…

Cited by 31Source ↗Scholar ↗
2020

An Efficient Sampling-Based Method for Online Informative Path Planning in Unknown Environments

Lukas Schmid, Michael Pantic, Raghav Khanna, Lionel Ott, Roland Siegwart, Juan I. Nieto
RA-L 2020

The ability to plan informative paths online is essential to robot autonomy. In particular, sampling-based approaches are often used as they are capable of using arbitrary information gain formulations. However, they are prone to local minima, resulting in sub-optimal trajectories, and sometimes do

Cited by 285Source ↗code ↗Scholar ↗
2020

Depth Based Semantic Scene Completion With Position Importance Aware Loss

Jie Li, Yu Liu, Xia Yuan, Chunxia Zhao, Roland Siegwart, Ian Reid +1
RA-L 2020

Semantic scene completion (SSC) refers to the task of inferring the 3D semantic segmentation of a scene while simultaneously completing the 3D shapes. We propose PALNet, a novel hybrid network for SSC based on single depth. PALNet utilizes a two-stream network to extract both 2D and 3D features from

Cited by 71Source ↗code ↗Scholar ↗
2020

End-to-End Velocity Estimation for Autonomous Racing

Sirish Srinivasan, Inkyu Sa, Alex Zyner, Victor Reijgwart, Miguel de la Iglesia Valls, Roland Siegwart
RA-L 2020

Velocity estimation plays a central role in driverless vehicles, but standard, and affordable methods struggle to cope with extreme scenarios like aggressive maneuvers due to the presence of high sideslip. To solve this, autonomous race cars are usually equipped with expensive external velocity sens

Cited by 35Source ↗Scholar ↗
2020

Hybrid Topological and 3D Dense Mapping through Autonomous Exploration for Large Indoor Environments

Clara Gomez, Marius Fehr, Alex Millane, Alejandra C. Hernandez, Juan Nieto, Ramon Barber +1
ICRA 2020poster

Robots require a detailed understanding of the 3D structure of the environment for autonomous navigation and path planning. A popular approach is to represent the environment using metric, dense 3D maps such as 3D occupancy grids. However, in large environments the computational power required for m…

Cited by 48Source ↗Scholar ↗
2020

IAN: Multi-Behavior Navigation Planning for Robots in Real, Crowded Environments

Daniel Dugas, Juan Nieto, Roland Siegwart, Jen Jen Chung
IROS 2020poster

State-of-the-art approaches for robot navigation among humans are typically restricted to planar movement actions. This work addresses the question of whether it can be beneficial to use interaction actions, such as saying, touching, and gesturing, for the sake of allowing robots to navigate in unst…

Cited by 21Source ↗Scholar ↗
2020

IDOL: A Framework for IMU-DVS Odometry using Lines

Cedric Le Gentil, Florian Tschopp, Ignacio Alzugaray, Teresa Vidal-Calleja, Roland Siegwart, Juan Nieto
IROS 2020poster

In this paper, we introduce IDOL, an optimization-based framework for IMU-DVS Odometry using Lines. Event cameras, also called Dynamic Vision Sensors (DVSs), generate highly asynchronous streams of events triggered upon illumination changes for each individual pixel. This novel paradigm presents adv…

Cited by 52Source ↗Scholar ↗
2020

Informative Path Planning for Active Field Mapping under Localization Uncertainty

Marija Popović, Teresa Vidal-Calleja, Jen Jen Chung, Juan Nieto, Roland Siegwart
ICRA 2020poster

Information gathering algorithms play a key role in unlocking the potential of robots for efficient data collection in a wide range of applications. However, most existing strategies neglect the fundamental problem of the robot pose uncertainty, which is an implicit requirement for creating robust,…

Cited by 41Source ↗Scholar ↗
2020

LQR-Assisted Whole-Body Control of a Wheeled Bipedal Robot With Kinematic Loops

Victor Klemm, Alessandro Morra, Lionel Gulich, Dominik Mannhart, David Rohr, Mina Kamel +2
RA-L 2020

We present a hierarchical whole-body controller leveraging the full rigid body dynamics of the wheeled bipedal robot Ascento. We derive closed-form expressions for the dynamics of its kinematic loops in a way that readily generalizes to more complex systems. The rolling constraint is incorporated us

Cited by 160Source ↗Scholar ↗
2020

Learning Camera Miscalibration Detection

Andrei Cramariuc, Aleksandar Petrov, Rohit Suri, Mayank Mittal, Roland Siegwart, Cesar Cadena
ICRA 2020poster

Self-diagnosis and self-repair are some of the key challenges in deploying robotic platforms for long-term real-world applications. One of the issues that can occur to a robot is miscalibration of its sensors due to aging, environmental transients, or external disturbances. Precise calibration lies…

Cited by 20Source ↗code ↗Scholar ↗
2020

Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes

Nicolas Marchal, Charlotte Moraldo, Hermann Blum, Roland Siegwart, Cesar Cadena, Abel Gawel
RA-L 2020

Deep learning has enabled remarkable advances in scene understanding, particularly in semantic segmentation tasks. Yet, current state of the art approaches are limited to a closed set of classes, and fail when facing novel elements, also known as out of distribution (OoD) data. This is a problem as

Cited by 21Source ↗Scholar ↗
2020

Learning Dynamics for Improving Control of Overactuated Flying Systems

Weixuan Zhang, Maximilian Brunner, Lionel Ott, Mina Kamel, Roland Siegwart, Juan I. Nieto
RA-L 2020

Overactuated omnidirectional flying vehicles are capable of generating force and torque in any direction, which is important for applications such as contact-based industrial inspection. This comes at the price of an increase in model complexity. These vehicles usually have non-negligible, repetitiv

Cited by 14Source ↗Scholar ↗
2020

Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning

Le Chen, Yunke Ao, Florian Tschopp, Andrei Cramariuc, Michel Breyer, Jen Jen Chung +2
CoRL 2020

Visual-inertial systems rely on precise calibrations of both camera intrinsics and inter-sensor extrinsics, which typically require manually performing complex motions in front of a calibration target. In this work we present a novel approach to obtain favorable trajectories for visual-inertial syst

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2020

Leveraging Stereo-Camera Data for Real-Time Dynamic Obstacle Detection and Tracking

Thomas Eppenberger, Gianluca Cesari, Marcin Dymczyk, Roland Siegwart, Renaud Dubé
IROS 2020poster

Dynamic obstacle avoidance is one crucial component for compliant navigation in crowded environments. In this paper we present a system for accurate and reliable detection and tracking of dynamic objects using noisy point cloud data generated by stereo cameras. Our solution is real-time capable and…

Cited by 66Source ↗Scholar ↗
2020

MOZARD: Multi-Modal Localization for Autonomous Vehicles in Urban Outdoor Environments

Lukas Schaupp, Patrick Pfreundschuh, Mathias Bürki, Cesar Cadena, Roland Siegwart, Juan Nieto
IROS 2020poster

Visually poor scenarios are one of the main sources of failure in visual localization systems in outdoor environments. To address this challenge, we present MOZARD, a multi-modal localization system for urban outdoor environments using vision and LiDAR. By fusing key point based visual multi-session…

Cited by 2Source ↗Scholar ↗
2020

MultiPoint: Cross-spectral registration of thermal and optical aerial imagery

Florian Achermann, Andrey Kolobov, Debadeepta Dey, Timo Hinzmann, Jen Jen Chung, Roland Siegwart +1
CoRL 2020

While optical cameras are ubiquitous in robotics, some robots can sense the world in several sections of the electromagnetic spectrum simultaneously, which can extend their capabilities in fundamental ways. For instance, many fixed-wing UAVs carry both optical and thermal imaging cameras, potentiall

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2020

Object Finding in Cluttered Scenes Using Interactive Perception

Tonci Novkovic, Remi Pautrat, Fadri Furrer, Michel Breyer, Roland Siegwart, Juan Nieto
ICRA 2020poster

Object finding in clutter is a skill that requires perception of the environment and in many cases physical interaction. In robotics, interactive perception defines a set of algorithms that leverage actions to improve the perception of the environment, and vice versa use perception to guide the next…

Cited by 87Source ↗Scholar ↗
2020

OneShot Global Localization: Instant LiDAR-Visual Pose Estimation

Sebastian Ratz, Marcin Dymczyk, Roland Siegwart, Renaud Dubé
ICRA 2020poster

Globally localizing in a given map is a crucial ability for robots to perform a wide range of autonomous navigation tasks. This paper presents OneShot - a global localization algorithm that uses only a single 3D LiDAR scan at a time, while outperforming approaches based on integrating a sequence of…

Cited by 44Source ↗Scholar ↗
2020

Robot Navigation in Crowded Environments Using Deep Reinforcement Learning

Lucia Liu, Daniel Dugas, Gianluca Cesari, Roland Siegwart, Renaud Dubé
IROS 2020poster

Mobile robots operating in public environments require the ability to navigate among humans and other obstacles in a socially compliant and safe manner. This work presents a combined imitation learning and deep reinforcement learning approach for motion planning in such crowded and cluttered environ…

Cited by 142Source ↗Scholar ↗
2020

Trajectory Optimization for Wheeled-Legged Quadrupedal Robots Driving in Challenging Terrain

Vivian Suzano Medeiros, Edo Jelavic, Marko Bjelonic, Roland Siegwart, Marco A. Meggiolaro, Marco Hutter
RA-L 2020

Wheeled-legged robots are an attractive solution for versatile locomotion in challenging terrain. They combine the speed and efficiency of wheels with the ability of legs to traverse challenging terrain. In this letter, we present a trajectory optimization formulation for wheeled-legged robots that

Cited by 90Source ↗Scholar ↗
2020

Trajectory Tracking Nonlinear Model Predictive Control for an Overactuated MAV

Maximilian Brunner, Karen Bodie, Mina Kamel, Michael Pantic, Weixuan Zhang, Juan Nieto +1
ICRA 2020poster

This work presents a method to control omnidirectional micro aerial vehicles (OMAVs) for the tracking of 6-DoF trajectories in free space. A rigid body model based approach is applied in a receding horizon fashion to generate optimal wrench commands that can be constrained to meet limits given by th…

Cited by 41Source ↗Scholar ↗
2020

Volumetric Grasping Network: Real-time 6 DOF Grasp Detection in Clutter

Michel Breyer, Jen Jen Chung, Lionel Ott, Roland Siegwart, Juan Nieto
CoRL 2020

General robot grasping in clutter requires the ability to synthesize grasps that work for previously unseen objects and that are also robust to physical interactions, such as collisions with other objects in the scene. In this work, we design and train a network that predicts 6 DOF grasps from 3D sc

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2020

Voxgraph: Globally Consistent, Volumetric Mapping Using Signed Distance Function Submaps

Victor Reijgwart, Alexander Millane, Helen Oleynikova, Roland Siegwart, Cesar Cadena, Juan I. Nieto
RA-L 2020

Globally consistent dense maps are a key requirement for long-term robot navigation in complex environments. While previous works have addressed the challenges of dense mapping and global consistency, most require more computational resources than may be available on-board small robots. We propose a

Cited by 109Source ↗code ↗Scholar ↗
2019

A Fully-Integrated Sensing and Control System for High-Accuracy Mobile Robotic Building Construction

Abel Gawel, Hermann Blum, Johannes Pankert, Koen Krämer, Luca Bartolomei, Selen Ercan +6
IROS 2019poster

We present a fully-integrated sensing and control system which enables mobile manipulator robots to execute building tasks with millimeter-scale accuracy on building construction sites. The approach leverages multi-modal sensing capabilities for state estimation, tight integration with digital build…

Cited by 74Source ↗Scholar ↗
2019

A Virtual Reality Interface for an Autonomous Spray Painting UAV

Anurag Sai Vempati, Harshit Khurana, Vojtech Kabelka, Simon Flueckiger, Roland Siegwart, Paul A. Beardsley
RA-L 2019

PaintCopter is an autonomous unmanned aerial vehicle (UAV) capable of spray painting on complex three-dimensional (3D) surfaces. This letter aims to make PaintCopter more user-friendly and to enable more intuitive human–robot interaction. We propose a virtual reality interface that allows the user t

Cited by 26Source ↗Scholar ↗
2019

AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming

Ciro Potena, Raghav Khanna, Juan I. Nieto, Roland Siegwart, Daniele Nardi, Alberto Pretto
RA-L 2019

The combination of aerial survey capabilities of unmanned aerial vehicles (UAVs) with targeted intervention abilities of agricultural unmanned ground vehicles (UGVs) can significantly improve the effectiveness of robotic systems applied to precision agriculture. In this context, building and updatin

Cited by 81Source ↗Scholar ↗
2019

An Approach for Semantic Segmentation of Tree-like Vegetation

S. Tejaswi Digumarti, Lukas Maximilian Schmid, Giuseppe Maria Rizzi, Juan Nieto, Roland Siegwart, Paul Beardsley +1
ICRA 2019poster

This paper presents a pipeline for semantic segmentation of trees into their components. Given a single RGB-D image of a tree, we employ a deep network to predict labels to classify each pixel of the tree into trunk, branches, twigs and leaves. Multiple convolutional neural network architectures to…

Cited by 18Source ↗Scholar ↗
2019

An Omnidirectional Aerial Manipulation Platform for Contact-Based Inspection

Karen Bodie, Maximilian Brunner, Michael Pantic, Stefan Walser, Patrick Pfändler, Ueli Angst +2
RSS 2019poster

This paper presents an omnidirectional aerial manipulation platform for robust and responsive interaction with unstructured environments, toward the goal of contact-based inspection. The fully actuated tilt-rotor aerial system is equipped with a rigidly mounted end-effector, and is able to exert a 6…

Cited by 132Source ↗PDF ↗Scholar ↗
2019

Ascento: A Two-Wheeled Jumping Robot

Victor Klemm, Alessandro Morra, Ciro Salzmann, Florian Tschopp, Karen Bodie, Lionel Gulich +7
ICRA 2019poster

Applications of mobile ground robots demand high speed and agility while navigating in complex indoor environments. These present an ongoing challenge in mobile robotics. A system with these specifications would be of great use for a wide range of indoor inspection tasks. This paper introduces Ascen…

Cited by 257Source ↗Scholar ↗
2019

Comparing Task Simplifications to Learn Closed-Loop Object Picking Using Deep Reinforcement Learning

Michel Breyer, Fadri Furrer, Tonci Novkovic, Roland Siegwart, Juan I. Nieto
RA-L 2019

Enabling autonomous robots to interact in unstructured environments with dynamic objects requires manipulation capabilities that can deal with clutter, changes, and objects' variability. This letter presents a comparison of different reinforcement learning-based approaches for object picking with a

Cited by 52Source ↗Scholar ↗
2019

Disturbance Estimation and Rejection for High-Precision Multirotor Position Control

Daniel Hentzen, Thomas Stastny, Roland Siegwart, Roland Brockers
IROS 2019poster

Many multirotor Unmanned Aerial Systems applications have a critical need for precise position control in environments with strong dynamic external disturbances such as wind gusts or ground and wall effects. Moreover, to maximize flight time, small multirotor platforms have to operate within strict…

Cited by 42Source ↗Scholar ↗
2019

Empty Cities: Image Inpainting for a Dynamic-Object-Invariant Space

Berta Bescos, José Neira, Roland Siegwart, Cesar Cadena
ICRA 2019poster

In this paper we present an end-to-end deep learning framework to turn images that show dynamic content, such as vehicles or pedestrians, into realistic static frames. This objective encounters two main challenges: detecting all the dynamic objects, and inpainting the static occluded background with…

Cited by 38Source ↗code ↗Scholar ↗
2019

Experimental Comparison of Visual-Aided Odometry Methods for Rail Vehicles

Florian Tschopp, Thomas Schneider, Andrew W. Palmer, Navid Nourani-Vatani, Cesar Cadena, Roland Siegwart +1
RA-L 2019

Today, rail vehicle localization is based on infrastructure-side Balises (beacons) together with on-board odometry to determine whether a rail segment is occupied. Such a coarse locking leads to a sub-optimal usage of the rail networks. New railway standards propose the use of moving blocks centred

Cited by 41Source ↗Scholar ↗
2019

Fault-tolerant Flight Control of a VTOL Tailsitter UAV

Silvan Fuhrer, Sebastian Verling, Thomas Stastny, Roland Siegwart
ICRA 2019poster

Compared to other vertical take-off and landing (VTOL) systems, a tailsitter minimizes the number of actuators and moving parts necessary. The downside of having a minimalistic actuation is its inherent low fault-tolerance. The failure of an actuator usually results in a loss of controllability, res…

Cited by 16Source ↗Scholar ↗
2019

Flexible Trinocular: Non-rigid Multi-Camera-IMU Dense Reconstruction for UAV Navigation and Mapping

Timo Hinzmann, Cesar Cadena, Juan Nieto, Roland Siegwart
IROS 2019poster

In this paper, we propose a visual-inertial framework able to efficiently estimate the camera poses of a non-rigid trinocular baseline for long-range depth estimation on-board a fast moving aerial platform. The estimation of the time-varying baseline is based on relative inertial measurements, a pho…

Cited by 9Source ↗Scholar ↗
2019

Free-Space Features: Global Localization in 2D Laser SLAM Using Distance Function Maps

Alexander Millane, Helen Oleynikova, Juan Nieto, Roland Siegwart, César Cadena
IROS 2019poster

In many applications, maintaining a consistent map of the environment is key to enabling robotic platforms to perform higher-level decision making. Detection of already visited locations is one of the primary ways in which map consistency is maintained, especially in situations where external positi…

Cited by 17Source ↗Scholar ↗
2019

From Coarse to Fine: Robust Hierarchical Localization at Large Scale

Paul-Edouard Sarlin, Cesar Cadena, Roland Siegwart, Marcin Dymczyk
CVPR 2019poster

Robust and accurate visual localization is a fundamental capability for numerous applications, such as autonomous driving, mobile robotics, or augmented reality. It remains, however, a challenging task, particularly for large-scale environments and in presence of significant appearance changes. Stat…

Cited by 1086PDF ↗code ↗Scholar ↗
2019

Learning to Predict the Wind for Safe Aerial Vehicle Planning

Florian Achermann, Nicholas R. J. Lawrance, René Ranftl, Alexey Dosovitskiy, Jen Jen Chung, Roland Siegwart
ICRA 2019poster

Obtaining an accurate estimate of the local wind remains a significant challenge for small unmanned aerial vehicles (UAVs). Small UAVs often operate at low altitudes near terrain, where the wind environment can be more complex than at higher altitudes. Combined with their relatively low mass, this m…

Cited by 19Source ↗Scholar ↗
2019

Multiple Hypothesis Semantic Mapping for Robust Data Association

Lukas Bernreiter, Abel Gawel, Hannes Sommer, Juan I. Nieto, Roland Siegwart, Cesar Dario Cadena Lerma
RA-L 2019

In this letter, we present a semantic mapping approach with multiple hypothesis tracking for data association. As semantic information has the potential to overcome ambiguity in measurements and place recognition, it forms an eminent modality for autonomous systems. This is particularly evident in u

Cited by 23Source ↗Scholar ↗
2019

OREOS: Oriented Recognition of 3D Point Clouds in Outdoor Scenarios

Lukas Schaupp, Mathias Bürki, Renaud Dubé, Roland Siegwart, Cesar Cadena
IROS 2019poster

We introduce a novel method for oriented place recognition with 3D LiDAR scans. A Convolutional Neural Network is trained to extract compact descriptors from single 3D LiDAR scans. These can be used both to retrieve near-by place candidates from a map, and to estimate the yaw discrepancy needed for…

Cited by 64Source ↗Scholar ↗
2019

Object Classification Based on Unsupervised Learned Multi-Modal Features For Overcoming Sensor Failures

Julia Nitsch, Juan Nieto, Roland Siegwart, Max Schmidt, Cesar Cadena
ICRA 2019poster

For autonomous driving applications it is critical to know which type of road users and road side infrastructure are present to plan driving manoeuvres accordingly. Therefore autonomous cars are equipped with different sensor modalities to robustly perceive its environment. However, for classificati…

Cited by 4Source ↗Scholar ↗
2019

Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search

Ajith Anil Meera, Marija Popović, Alexander Millane, Roland Siegwart
ICRA 2019poster

Target search with unmanned aerial vehicles (UAVs) is relevant problem to many scenarios, e.g., search and rescue (SaR). However, a key challenge is planning paths for maximal search efficiency given flight time constraints. To address this, we propose the Obstacle-aware Adaptive Informative Path Pl…

Cited by 74Source ↗Scholar ↗
2019

On Flying Backwards: Preventing Run-away of Small, Low-speed, Fixed-wing UAVs in Strong Winds

Thomas Stastny, Roland Siegwart
IROS 2019poster

Small, low-speed fixed-wing Unmanned Aerial Vehicles (UAVs) operating autonomously, beyond-visual-line-of-sight (BVLOS) will inevitably encounter winds rising to levels near or exceeding the vehicles' nominal airspeed. In this paper, we develop a nonlinear lateral-directional path following guidance…

Cited by 13Source ↗Scholar ↗
2019

Optimization-Based Terrain Analysis and Path Planning in Unstructured Environments

Ueli Graf, Paulo Borges, Emili Hernández, Roland Siegwart, Renaud Dubé
ICRA 2019poster

Accurate environment representation is one of the key challenges in autonomous ground vehicle navigation in unstructured environments. We propose a real-time optimization-based approach to terrain modeling and path planning in off-road and rough environments. Our method uses an irregular, hierarchic…

Cited by 21Source ↗Scholar ↗
2019

Redundant Perception and State Estimation for Reliable Autonomous Racing

Nikhil Gosala, Andreas Bühler, Manish Prajapat, Claas Ehmke, Mehak Gupta, Ramya Sivanesan +6
ICRA 2019poster

In autonomous racing, vehicles operate close to the limits of handling and a sensor failure can have critical consequences. To limit the impact of such failures, this paper presents the redundant perception and state estimation approaches developed for an autonomous race car. Redundancy in perceptio…

Cited by 34Source ↗Scholar ↗
2019

Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery

Margarita Grinvald, Fadri Furrer, Tonci Novkovic, Jen Jen Chung, Cesar Cadena, Roland Siegwart +1
RA-L 2019

To autonomously navigate and plan interactions in real-world environments, robots require the ability to robustly perceive and map complex, unstructured surrounding scenes. Besides building an internal representation of the observed scene geometry, the key insight toward a truly functional understan

Cited by 255Source ↗code ↗Scholar ↗
2018

Automatic Segmentation of Tree Structure From Point Cloud Data

Sundara Tejaswi Digumarti, Juan I. Nieto, Cesar Cadena, Roland Siegwart, Paul A. Beardsley
RA-L 2018

Methods for capturing and modeling vegetation, such as trees or plants, typically distinguish between two components-branch skeleton and foliage. Current methods do not provide quantitatively accurate tree structure and foliage density needed for applications such as visualization, inspection, or to

Cited by 22Source ↗Scholar ↗
2018

C-blox: A Scalable and Consistent TSDF-based Dense Mapping Approach

Alexander Millane, Zachary Taylor, Helen Oleynikova, Juan Nieto, Roland Siegwart, César Cadena
IROS 2018poster

In many applications, maintaining a consistent dense map of the environment is key to enabling robotic platforms to perform higher level decision making. Several works have addressed the challenge of creating precise dense 3D maps from visual sensors providing depth information. However, during oper…

Cited by 68Source ↗code ↗Scholar ↗
2018

Collaborative 6DoF Relative Pose Estimation for Two UAVs with Overlapping Fields of View

Marco Karrer, Mohit Agarwal, Mina Kamel, Roland Siegwart, Margarita Chli
ICRA 2018poster

Driven by the promise of leveraging the benefits of collaborative robot operation, this paper presents an approach to estimate the relative transformation between two small Unmanned Aerial Vehicles (UAVs), each equipped with a single camera and an inertial sensor, comprising the first step of any me…

Cited by 20Source ↗Scholar ↗
2018

Cubic Range Error Model for Stereo Vision with Illuminators

Marius Huber, Timo Hinzmann, Roland Siegwart, Larry H. Matthies
ICRA 2018poster

Use of low-cost depth sensors, such as a stereo camera setup with illuminators, is of particular interest for numerous applications ranging from robotics and transportation to mixed and augmented reality. The ability to quantify noise is crucial for these applications, e.g., when the sensor is used…

Cited by 5Source ↗Scholar ↗
2018

Design of an Autonomous Racecar: Perception, State Estimation and System Integration

Miguel I. Valls, Hubertus F.C. Hendrikx, Victor J.F. Reijgwart, Fabio V. Meier, Inkyu Sa, Renaud Dubé +3
ICRA 2018poster

This paper introduces jlüela driverless: the first autonomous racecar to win a Formula Student Driverless competition. In this competition, among other challenges, an autonomous racecar is tasked to complete 10 laps of a previously unknown racetrack as fast as possible and using only onboard sensing…

Cited by 54Source ↗Scholar ↗
2018

Flexible Stereo: Constrained, Non-Rigid, Wide-Baseline Stereo Vision for Fixed-Wing Aerial Platforms

Timo Hinzmann, Tim Taubner, Roland Siegwart
ICRA 2018poster

This paper proposes a computationally efficient method to estimate the time-varying relative pose between two visual-inertial sensor rigs mounted on the flexible wings of a fixed-wing unmanned aerial vehicle (UAV). The estimated relative poses are used to generate highly accurate depth maps in real-…

Cited by 18Source ↗Scholar ↗
2018

Free LSD: Prior-Free Visual Landing Site Detection for Autonomous Planes

Timo Hinzmann, Thomas Stastny, Cesar Cadena, Roland Siegwart, Igor Gilitschenski
RA-L 2018

Full autonomy for fixed-wing unmanned aerial vehicles (UAVs) requires the capability to autonomously detect potential landing sites in unknown and unstructured terrain, allowing for self-governed mission completion or handling of emergency situations. In this letter, we propose a perception system a

Cited by 39Source ↗Scholar ↗
2018

GOMSF: Graph-Optimization Based Multi-Sensor Fusion for robust UAV Pose estimation

Ruben Mascaro, Lucas Teixeira, Timo Hinzmann, Roland Siegwart, Margarita Chli
ICRA 2018poster

Achieving accurate, high-rate pose estimates from proprioceptive and/or exteroceptive measurements is the first step in the development of navigation algorithms for agile mobile robots such as Unmanned Aerial Vehicles (UAVs). In this paper, we propose a decoupled Graph-Optimization based Multi-Senso…

Cited by 148Source ↗Scholar ↗
2018

History-Aware Autonomous Exploration in Confined Environments Using MAVs

Christian Witting, Marius Fehr, Rik Bähnemann, Helen Oleynikova, Roland Siegwart
IROS 2018poster

Many scenarios require a robot to be able to explore its 3D environment online without human supervision. This is especially relevant for inspection tasks and search and rescue missions. To solve this high-dimensional path planning problem, sampling-based exploration algorithms have proven successfu…

Cited by 109Source ↗Scholar ↗
2018

Incremental Object Database: Building 3D Models from Multiple Partial Observations

Fadri Furrer, Tonci Novkovic, Marius Fehr, Abel Gawel, Margarita Grinvald, Torsten Sattler +2
IROS 2018poster

Collecting 3D object data sets involves a large amount of manual work and is time consuming. Getting complete models of objects either requires a 3D scanner that covers all the surfaces of an object or one needs to rotate it to completely observe it. We present a system that incrementally builds a d…

Cited by 48Source ↗Scholar ↗
2018

Incremental-Segment-Based Localization in 3-D Point Clouds

Renaud Dubé, Mattia G. Gollub, Hannes Sommer, Igor Gilitschenski, Roland Siegwart, Cesar Cadena +1
RA-L 2018

Localization in 3-D point clouds is a highly challenging task due to the complexity associated with extracting information from 3-D data. This letter proposes an incremental approach addressing this problem efficiently. The presented method first accumulates the measurements in a dynamic voxel grid

Cited by 61Source ↗code ↗Scholar ↗
2018

LandmarkBoost: Efficient visualContext Classifiers for Robust Localization

Marcin Dymczyk, Igor Gilitschenski, Juan Nieto, Simon Lynen, Bernhard Zeisl, Roland Siegwart
IROS 2018poster

The growing popularity of autonomous systems creates a need for reliable and efficient metric pose retrieval algorithms. Currently used approaches tend to rely on nearest neighbor search of binary descriptors to perform the 2D-3D matching and guarantee realtime capabilities on mobile platforms. Thes…

Cited by 9Source ↗Scholar ↗
2018

Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization

Paul-Edouard Sarlin, Frederic Debraine, Marcin Dymczyk, Roland Siegwart, Cesar Cadena
CoRL 2018

Many robotics applications require precise pose estimates despite operating in large and changing environments. This can be addressed by visual localization, using a pre-computed 3D model of the surroundings. The pose estimation then amounts to finding correspondences between 2D keypoints in a query

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2018

Local Positioning System Using UWB Range Measurements for an Unmanned Blimp

Vincent Mai, Mina Kamel, Matthias Krebs, Andreas Schaffner, Daniel Meier, Liam Paull +1
RA-L 2018

Unmanned blimps are a safe and reliable alternative to conventional drones when flying above people. On-board real-time tracking of their pose and velocities is a necessary step toward autonomous navigation. There is a need for an easily deployable technology that is able to accurately and robustly

Cited by 24Source ↗Scholar ↗
2018

Maplab: An Open Framework for Research in Visual-Inertial Mapping and Localization

Thomas Schneider, Marcin Dymczyk, Marius Fehr, Kevin Egger, Simon Lynen, Igor Gilitschenski +1
RA-L 2018

Robust and accurate visual-inertial estimation is crucial to many of today's challenges in robotics. Being able to localize against a prior map and obtain accurate and drift-free pose estimates can push the applicability of such systems even further. Most of the currently available solutions, howeve

Cited by 272Source ↗code ↗Scholar ↗
2018

Multi-Agent Time-Based Decision-Making for the Search and Action Problem

Takahiro Miki, Marija Popović, Abel Gawel, Gregory Hitz, Roland Siegwart
ICRA 2018poster

Many robotic applications, such as search-and-rescue, require multiple agents to search for and perform actions on targets. However, such missions present several challenges, including cooperative exploration, task selection and allocation, time limitations, and computational complexity. To address…

Cited by 19Source ↗Scholar ↗
2018

PaintCopter: An Autonomous UAV for Spray Painting on Three-Dimensional Surfaces

Anurag Sai Vempati, Mina Kamel, Nikola Stilinovic, Qixuan Zhang, Dorothea Reusser, Inkyu Sa +3
RA-L 2018

This letter describes a system for autonomous spray painting using an unmanned aerial vehicle (UAV), suitable for industrial applications. The work is motivated by the potential for such a system to achieve accurate and fast painting results. The PaintCopter is a quadrotor that has been custom fitte

Cited by 55Source ↗Scholar ↗
2018

PoseMap: Lifelong, Multi-Environment 3D LiDAR Localization

Philipp Egger, Paulo V K Borges, Gavin Catt, Andreas Pfrunder, Roland Siegwart, Renaud Dubé
IROS 2018poster

Reliable long-term localization is key for robotic systems in dynamic environments. In this paper, we propose a novel approach for long-term localization using 3D LiDARs, coined PoseMap. In essence, we extract distinctive features from range measurements and bundle these into local views along with…

Cited by 62Source ↗Scholar ↗
2018

Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Mapless Navigation by Leveraging Prior Demonstrations

Mark Pfeiffer, Samarth Shukla, Matteo Turchetta, Cesar Cadena, Andreas Krause, Roland Siegwart +1
RA-L 2018

This letter presents a case study of a learning-based approach for target-driven mapless navigation. The underlying navigation model is an end-to-end neural network, which is trained using a combination of expert demonstrations, imitation learning (IL) and reinforcement learning (RL). While RL and I

Cited by 176Source ↗code ↗Scholar ↗
2018

Safe Local Exploration for Replanning in Cluttered Unknown Environments for Microaerial Vehicles

Helen Oleynikova, Zachary Taylor, Roland Siegwart, Juan I. Nieto
RA-L 2018

In order to enable microaerial vehicles (MAVs) to assist in complex, unknown, unstructured environments, they must be able to navigate with guaranteed safety, even when faced with a cluttered environment they have no prior knowledge of. While trajectory-optimization-based local planners have been sh

Cited by 94Source ↗Scholar ↗
2018

SegMap: 3D Segment Mapping using Data-Driven Descriptors

Renaud Dubé, Andrei Cramariuc, Daniel Dugas, Juan Nieto, Roland Siegwart, Cesar Cadena
RSS 2018poster

When performing localization and mapping, working at the level of structure can be advantageous in terms of robustness to environmental changes and differences in illumination. This paper presents SegMap: a map representation solution to the localization and mapping problem based on the extraction o…

Cited by 218Source ↗PDF ↗code ↗Scholar ↗
2018

Sparse 3D Topological Graphs for Micro-Aerial Vehicle Planning

Helen Oleynikova, Zachary Taylor, Roland Siegwart, Juan Nieto
IROS 2018poster

Micro-Aerial Vehicles (MAVs) have the advantage of moving freely in 3D space. However, creating compact and sparse map representations that can be efficiently used for planning for such robots is still an open problem. In this paper, we take maps built from noisy sensor data and construct a sparse g…

Cited by 80Source ↗Scholar ↗
2018

The Two-State Implicit Filter Recursive Estimation for Mobile Robots

Michael Bloesch, Michael Burri, Hannes Sommer, Roland Siegwart, Marco Hutter
RA-L 2018

This letter deals with recursive filtering for dynamic systems where an explicit process model is not easily devisable. Most Bayesian filters assume the availability of such an explicit process model, and thus may require additional assumptions or fail to properly leverage all available information.

Cited by 60Source ↗Scholar ↗
2018

Towards Autonomous Stratospheric Flight: A Generic Global System Identification Framework for Fixed-Wing Platforms

Jongseok Lee, Tin Muskardin, Cristina Ruiz Pacz, Philipp Oettershagen, Thomas Stastny, Inkyu Sa +2
IROS 2018poster

System identification of High Altitude Long Endurance fixed-wing aerial vehicles is challenging as its operating flight envelope covers wide ranges of altitudes and Mach numbers. We present a new global system identification framework geared towards such fixed-wing aerial platforms where the aim is…

Cited by 6Source ↗Scholar ↗
2018

weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming

Inkyu Sa, Zetao Chen, Marija Popovic, Raghav Khanna, Frank Liebisch, Juan I. Nieto +1
RA-L 2018

Selective weed treatment is a critical step in autonomous crop management as related to crop health and yield. However, a key challenge is reliable and accurate weed detection to minimize damage to surrounding plants. In this letter, we present an approach for dense semantic weed classification with

Cited by 297Source ↗Scholar ↗
2017

A low-cost system for high-rate, high-accuracy temporal calibration for LIDARs and cameras

Hannes Sommer, Raghav Khanna, Igor Gilitschenski, Zachary Taylor, Roland Siegwart, Juan Nieto
IROS 2017poster

Deployment of camera and laser based motion estimation systems for controlling platforms operating at high speeds, such as cars or trains, is posing increasingly challenging precision requirements on the temporal calibration of these sensors. In this work, we demonstrate a simple, low-cost system fo…

Cited by 23Source ↗Scholar ↗
2017

Aerial picking and delivery of magnetic objects with MAVs

Abel Gawel, Mina Kamel, Tonci Novkovic, Jakob Widauer, Dominik Schindler, Benjamin Pfyffer von Altishofen +2
ICRA 2017poster

Autonomous delivery of goods using a Micro Air Vehicle (MAV) is a difficult problem, as it poses high demand on the MAV's control, perception and manipulation capabilities. This problem is especially challenging if the exact shape, location and configuration of the objects are unknown. In this paper…

Cited by 101Source ↗Scholar ↗
2017

An online multi-robot SLAM system for 3D LiDARs

Renaud Dubé, Abel Gawel, Hannes Sommer, Juan Nieto, Roland Siegwart, Cesar Cadena
IROS 2017poster

Using multiple cooperative robots is advantageous for time critical Search and Rescue (SaR) missions as they permit rapid exploration of the environment and provide higher redundancy than using a single robot. A considerable number of applications such as autonomous driving and disaster response cou…

Cited by 176Source ↗Scholar ↗
2017

Autonomous robotic stone stacking with online next best object target pose planning

Fadri Furrer, Martin Wermelinger, Hironori Yoshida, Fabio Gramazio, Matthias Kohler, Roland Siegwart +1
ICRA 2017poster

Predominately, robotic construction is applied as prefabrication in structured indoor environments with standard building materials. Our work, on the other hand, focuses on utilizing irregular materials found on-site, such as rubble and rocks, for autonomous construction. We present a pipeline that…

Cited by 93Source ↗Scholar ↗
2017

Collaborative transportation using MAVs via passive force control

Andrea Tagliabue, Mina Kamel, Sebastian Verling, Roland Siegwart, Juan Nieto
ICRA 2017poster

This paper shows a strategy based on passive force control for collaborative object transportation using Micro Aerial Vehicles (MAVs), focusing on the transportation of a bulky object by two hexacopters. The goal is to develop a robust approach which does not rely on: (a) communication links between…

Cited by 133Source ↗Scholar ↗
2017

Efficient descriptor learning for large scale localization

Antonio Loquercio, Marcin Dymczyk, Bernhard Zeisl, Simon Lynen, Igor Gilitschenski, Roland Siegwart
ICRA 2017poster

Many robotics and Augmented Reality (AR) systems that use sparse keypoint-based visual maps operate in large and highly repetitive environments, where pose tracking and localization are challenging tasks. Additionally, these systems usually face further challenges, such as limited computational powe…

Cited by 21Source ↗Scholar ↗
2017

From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots

Mark Pfeiffer, Michael Schaeuble, Juan Nieto, Roland Siegwart, Cesar Cadena
ICRA 2017poster

Learning from demonstration for motion planning is an ongoing research topic. In this paper we present a model that is able to learn the complex mapping from raw 2D-laser range findings and a target position to the required steering commands for the robot. To our best knowledge, this work presents t…

Cited by 526Source ↗Scholar ↗
2017

Map quality evaluation for visual localization

Hamza Merzić, Elena Stumm, Marcin Dymczyk, Roland Siegwart, Igor Gilitschenski
ICRA 2017poster

A variety of end-user devices involving keypoint-based mapping systems are about to hit the market e.g. as part of smartphones, cars, robotic platforms, or virtual and augmented reality applications. Thus, the generated map data requires automated evaluation procedures that do not require experience…

Cited by 17Source ↗Scholar ↗
2017

Model-based transition optimization for a VTOL tailsitter

Sebastian Verling, Thomas Stastny, Gregory Bättig, Kostas Alexis, Roland Siegwart
ICRA 2017poster

This paper addresses the problem of trajectory optimization for the transition of a Vertical Take-off and Landing (VTOL) tailsitter Unmanned Aerial Vehicle (UAV). The proposed strategy performs a model based optimization, where the model represents the closed-loop dynamics of the UAV with low-level…

Cited by 39Source ↗Scholar ↗
2017

Multiresolution mapping and informative path planning for UAV-based terrain monitoring

Marija Popović, Teresa Vidal-Calleja, Gregory Hitz, Inkyu Sa, Roland Siegwart, Juan Nieto
IROS 2017poster

Unmanned aerial vehicles (UAVs) can offer timely and cost-effective delivery of high-quality sensing data. However, deciding when and where to take measurements in complex environments remains an open challenge. To address this issue, we introduce a new multiresolution mapping approach for informati…

Cited by 89Source ↗Scholar ↗
2017

Onboard real-time dense reconstruction of large-scale environments for UAV

Anurag Sai Vempati, Igor Gilitschenski, Juan Nieto, Paul Beardsley, Roland Siegwart
IROS 2017poster

In this paper, we propose a GPU parallelized SLAM system capable of using photometric and inertial data together with depth data from an active RGB-D sensor to build accurate dense 3D maps of indoor environments. We describe several extensions to existing dense SLAM techniques that allow us to opera…

Cited by 17Source ↗Scholar ↗
2017

Online informative path planning for active classification using UAVs

Marija Popović, Gregory Hitz, Juan Nieto, Inkyu Sa, Roland Siegwart, Enric Galceran
ICRA 2017poster

In this paper, we introduce an informative path planning (IPP) framework for active classification using unmanned aerial vehicles (UAVs). Our algorithm uses a combination of global viewpoint selection and evolutionary optimization to refine the planned trajectory in continuous 3D space while satisfy…

Cited by 120Source ↗Scholar ↗
2017

Robust collision avoidance for multiple micro aerial vehicles using nonlinear model predictive control

Mina Kamel, Javier Alonso-Mora, Roland Siegwart, Juan Nieto
IROS 2017poster

When several Multirotor Micro Aerial Vehicles (MAVs) share the same airspace, reliable and robust collision avoidance is required. In this paper we address the problem of multi-MAV reactive collision avoidance. We employ a model-based controller to simultaneously track a reference trajectory and avo…

Cited by 140Source ↗Scholar ↗
2017

Sampling-based motion planning for active multirotor system identification

Rik Bähnemann, Michael Burri, Enric Galceran, Roland Siegwart, Juan Nieto
ICRA 2017poster

This paper reports on an algorithm for planning trajectories that allow a multirotor micro aerial vehicle (MAV) to quickly identify a set of unknown parameters. In many problems like self calibration or model parameter identification some states are only observable under a specific motion. These mot…

Cited by 21Source ↗Scholar ↗
2017

SegMatch: Segment based place recognition in 3D point clouds

Renaud Dubé, Daniel Dugas, Elena Stumm, Juan Nieto, Roland Siegwart, Cesar Cadena
ICRA 2017poster

Place recognition in 3D data is a challenging task that has been commonly approached by adapting image-based solutions. Methods based on local features suffer from ambiguity and from robustness to environment changes while methods based on global features are viewpoint dependent. We propose SegMatch…

Cited by 418Source ↗Scholar ↗
2017

TSDF-based change detection for consistent long-term dense reconstruction and dynamic object discovery

Marius Fehr, Fadri Furrer, Ivan Dryanovski, Jürgen Sturm, Igor Gilitschenski, Roland Siegwart +1
ICRA 2017poster

Robots that are operating for extended periods of time need to be able to deal with changes in their environment and represent them adequately in their maps. In this paper, we present a novel 3D reconstruction algorithm based on an extended Truncated Signed Distance Function (TSDF) that enables to c…

Cited by 93Source ↗Scholar ↗
2017

UAV-based crop and weed classification for smart farming

Philipp Lottes, Raghav Khanna, Johannes Pfeifer, Roland Siegwart, Cyrill Stachniss
ICRA 2017poster

Unmanned aerial vehicles (UAVs) and other robots in smart farming applications offer the potential to monitor farm land on a per-plant basis, which in turn can reduce the amount of herbicides and pesticides that must be applied. A central information for the farmer as well as for autonomous agricult…

Cited by 514Source ↗Scholar ↗
2017

Visual-inertial self-calibration on informative motion segments

Thomas Schneider, Mingyang Li, Michael Burri, Juan Nieto, Roland Siegwart, Igor Gilitschenski
ICRA 2017poster

Environmental conditions and external effects, such as shocks, have a significant impact on the calibration parameters of visual-inertial sensor systems. Thus long-term operation of these systems cannot fully rely on factory calibration. Since the observability of certain parameters is highly depend…

Cited by 31Source ↗Scholar ↗
2017

Voxblox: Incremental 3D Euclidean Signed Distance Fields for on-board MAV planning

Helen Oleynikova, Zachary Taylor, Marius Fehr, Roland Siegwart, Juan Nieto
IROS 2017poster

Micro Aerial Vehicles (MAVs) that operate in unstructured, unexplored environments require fast and flexible local planning, which can replan when new parts of the map are explored. Trajectory optimization methods fulfill these needs, but require obstacle distance information, which can be given by…

Cited by 768Source ↗Scholar ↗