100 citations · 231 across the 9 of their papers we have counts for
11 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…
COIN-LIO: Complementary Intensity-Augmented LiDAR Inertial Odometry
Patrick Pfreundschuh, Helen Oleynikova, Cesar Cadena +2
We present COIN-LIO, a LiDAR Inertial Odometry pipeline that tightly couples information from LiDAR intensity with geometry-based point cloud registration. The focus of our work is…
Dynablox: Real-time Detection of Diverse Dynamic Objects in Complex Environments
Lukas Schmid, Olov Andersson, Aurelio Sulser +2
Real-time detection of moving objects is an essential capability for robots acting autonomously in dynamic environments. We thus propose Dynablox, a novel online mapping-based appr…
Resilient Terrain Navigation with a 5 DOF Metal Detector Drone
Patrick Pfreundschuh, Rik Bähnemann, Tim Kazik +3
Micro aerial vehicles (MAVs) hold the potential for performing autonomous and contactless land surveys for the detection of landmines and explosive remnants of war (ERW). Metal det…
Team CERBERUS Wins the DARPA Subterranean Challenge: Technical Overview and Lessons Learned
Marco Tranzatto, Mihir Dharmadhikari, Lukas Bernreiter +33
This article presents the CERBERUS robotic system-of-systems, which won the DARPA Subterranean Challenge Final Event in 2021. The Subterranean Challenge was organized by DARPA with…
Dynamic Object Aware LiDAR SLAM based on Automatic Generation of Training Data
Patrick Pfreundschuh, Hubertus Franciscus Cornelis Hendrikx, Victor Reijgwart +3
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…
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…