6 papers
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…
Sight Guide: A Wearable Assistive Perception and Navigation System for the Vision Assistance Race in the Cybathlon 2024
Patrick Pfreundschuh, Giovanni Cioffi, Cornelius von Einem +6
Visually impaired individuals face significant challenges navigating and interacting with unknown situations, particularly in tasks requiring spatial awareness and semantic scene u…
A robust baro-radar-inertial odometry m-estimator for multicopter navigation in cities and forests
Rik Girod, Marco Hauswirth, Patrick Pfreundschuh +2
Search and rescue operations require mobile robots to navigate unstructured indoor and outdoor environments. In particular, actively stabilized multirotor drones need precise movem…
Informed, Constrained, Aligned: A Field Analysis on Degeneracy-aware Point Cloud Registration in the Wild
Turcan Tuna, Julian Nubert, Patrick Pfreundschuh +3
The ICP registration algorithm has been a preferred method for LiDAR-based robot localization for nearly a decade. However, even in modern SLAM solutions, ICP can degrade and becom…
TULIP: Transformer for Upsampling of LiDAR Point Clouds
Bin Yang, Patrick Pfreundschuh, Roland Siegwart +3
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…
MOZARD: Multi-Modal Localization for Autonomous Vehicles in Urban Outdoor Environments
Lukas Schaupp, Patrick Pfreundschuh, Mathias Buerki +3
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 l…