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Juan Nieto

48 accepted papers

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 ↗
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

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

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

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 ↗
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

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

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

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

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 ↗
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

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

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 ↗
2018

A Data-driven Model for Interaction-Aware Pedestrian Motion Prediction in Object Cluttered Environments

Mark Pfeiffer, Giuseppe Paolo, Hannes Sommer, Juan Nieto, Rol Siegwart, Cesar Cadena
ICRA 2018poster

This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles. When navigating in such workspaces shared with humans, robots need accurate motion predictions of the surrounding pedestrians. Human navigation behavior…

Cited by 140Source ↗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

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

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

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 ↗
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

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

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

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

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 ↗
2016

Appearance-based landmark selection for efficient long-term visual localization

Mathias Bürki, Igor Gilitschenski, Elena Stumm, Roland Siegwart, Juan Nieto
IROS 2016poster

In this paper, we present an online landmark selection method for distributed long-term visual localization systems in bandwidth-constrained environments. Sharing a common map for online localization provides a fleet of autonomous vehicles with the possibility to maintain and access a consistent map…

Cited by 42Source ↗Scholar ↗
2016

Continuous-time trajectory optimization for online UAV replanning

Helen Oleynikova, Michael Burri, Zachary Taylor, Juan Nieto, Roland Siegwart, Enric Galceran
IROS 2016poster

Multirotor unmanned aerial vehicles (UAVs) are rapidly gaining popularity for many applications. However, safe operation in partially unknown, unstructured environments remains an open question. In this paper, we present a continuous-time trajectory optimization method for real-time collision avoida…

Cited by 335Source ↗Scholar ↗
2016

Robust Visual Place Recognition With Graph Kernels

Elena Stumm, Christopher Mei, Simon Lacroix, Juan Nieto, Marco Hutter, Roland Siegwart
CVPR 2016poster

A novel method for visual place recognition is introduced and evaluated, demonstrating robustness to perceptual aliasing and observation noise. This is achieved by increasing discrimination through a more structured representation of visual observations. Estimation of observation likelihoods are bas…

Cited by 69PDF ↗Scholar ↗
2016

Structure-based vision-laser matching

Abel Gawel, Titus Cieslewski, Renaud Dubé, Mike Bosse, Roland Siegwart, Juan Nieto
IROS 2016poster

Persistent merging of maps created by different sensor modalities is an insufficiently addressed problem. Current approaches either rely on appearance-based features which may suffer from lighting and viewpoint changes or require pre-registration between all sensor modalities used. This work present…

Cited by 67Source ↗Scholar ↗