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Renaud Dubé

17 accepted papers

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

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 ↗
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

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 ↗
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

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 ↗
2018

Delight: An Efficient Descriptor for Global Localisation Using LiDAR Intensities

Konrad P Cop, Paulo V K Borges, Renaud Dubé
ICRA 2018poster

Place recognition is a key element of mobile robotics. It can assist with the “wake-up” and “kidnapped robot” problems, where the robot position needs to be estimated without prior information. Among the different sensors that can be used for the task (e.g., camera, GPS, LiDAR), LiDAR has the advant…

Cited by 126Source ↗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

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

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 ↗
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

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 ↗
2016

Non-uniform sampling strategies for continuous correction based trajectory estimation

Renaud Dubé, Hannes Sommer, Abel Gawel, Michael Bosse, Roland Siegwart
ICRA 2016

Sliding window estimation is widely used for online simultaneous localization and mapping. While increasing the sliding window size generally yields improved accuracy, it also comes at an increase in computational cost. In order to reduce this cost, we propose smarter non-uniform sampling of the tra

Cited by 16Source ↗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 ↗