337 citations · 719 across the 5 of their papers we have counts for
6 papers
NavDreams: Towards Camera-Only RL Navigation Among Humans
Daniel Dugas, Olov Andersson, Roland Siegwart +1
Autonomously navigating a robot in everyday crowded spaces requires solving complex perception and planning challenges. When using only monocular image sensor data as input, classi…
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 +7
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 be…
NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human Environments
Daniel Dugas, Juan Nieto, Roland Siegwart +1
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…
SegMap: Segment-based mapping and localization using data-driven descriptors
Renaud Dubé, Andrei Cramariuc, Daniel Dugas +5
Precisely estimating a robot's pose in a prior, global map is a fundamental capability for mobile robotics, e.g. autonomous driving or exploration in disaster zones. This task, how…
SegMap: 3D Segment Mapping using Data-Driven Descriptors
Renaud Dubé, Andrei Cramariuc, Daniel Dugas +3
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. Thi…
SegMatch: Segment based loop-closure for 3D point clouds
Renaud Dubé, Daniel Dugas, Elena Stumm +3
Loop-closure detection on 3D data is a challenging task that has been commonly approached by adapting image-based solutions. Methods based on local features suffer from ambiguity a…