4 citations · 6 across the 5 of their papers we have counts for
7 papers
Trinity: Unifying Class-Agnostic Terrain and Semantic Segmentation for Unstructured Outdoor Environments by Leveraging Synthetic Data
Marcus G Müller, Wout Boerdijk, Maximilian Durner +5
Terrain understanding is fundamental for mobile robots operating in unstructured outdoor environments. Existing vision-based traversability estimation methods rely on robot-specifi…
BIEVR-LIO: Robust LiDAR-Inertial Odometry through Bump-Image-Enhanced Voxel Maps
Patrick Pfreundschuh, Turcan Tuna, Cedric Le Gentil +3
Reliable odometry is essential for mobile robots as they increasingly enter more challenging environments, which often contain little information to constrain point cloud registrat…
Efficient Hierarchical Any-Angle Path Planning on Multi-Resolution 3D Grids
Victor Reijgwart, Cesar Cadena, Roland Siegwart +1
Hierarchical, multi-resolution volumetric mapping approaches are widely used to represent large and complex environments as they can efficiently capture their occupancy and connect…
CompSLAM: Complementary Hierarchical Multi-Modal Localization and Mapping for Robot Autonomy in Underground Environments
Shehryar Khattak, Timon Homberger, Lukas Bernreiter +5
Robot autonomy in unknown, GPS-denied, and complex underground environments requires real-time, robust, and accurate onboard pose estimation and mapping for reliable operations. Th…
Obstacle-Avoidant Leader Following with a Quadruped Robot
Carmen Scheidemann, Lennart Werner, Victor Reijgwart +5
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…
Framework for Robust Localization of UUVs and Mapping of Net Pens
David Botta, Luca Ebner, Andrej Studer +3
This paper presents a general framework integrating vision and acoustic sensor data to enhance localization and mapping in highly dynamic and complex underwater environments, with…
A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments
Mark Pfeiffer, Giuseppe Paolo, Hannes Sommer +3
This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles. When navigating in such workspace…