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Patrick Pfreundschuh

8 accepted papers

2026

BIEVR-LIO: Robust LiDAR-Inertial Odometry through Bump-Image-Enhanced Voxel Maps

Patrick Pfreundschuh, Turcan Tuna, Cedric Le Gentil, Roland Siegwart, Cesar Cadena, Helen Oleynikova
RSS 2026poster

Reliable odometry is essential for mobile robots as they increasingly enter more challenging environments, which often contain little information to constrain point cloud registration, resulting in degraded LiDAR–Inertial Odometry (LIO) accuracy or even divergence. To address this, we present BIEVR-…

Cited by 0Source ↗Scholar ↗
2026

Informed, Constrained, Aligned: A Field Analysis on Degeneracy-Aware Point Cloud Registration in the Wild (I)

Turcan Tuna, Julian Nubert, Patrick Pfreundschuh, Cesar Cadena, Shehryar Khattak, Marco Hutter
ICRA 2026poster

The iterative closest point registration algorithm has been a preferred method for light detection and ranging LiDAR-based robot localization for nearly a decade. However, even in modern simultaneous localization and mapping (SLAM) solutions, ICP can degrade and become unreliable in geometrically il…

Cited by 0Scholar ↗
2024

COIN-LIO: Complementary Intensity-Augmented LiDAR Inertial Odometry

Patrick Pfreundschuh, Helen Oleynikova, Cesar Cadena, Roland Siegwart, Olov Andersson
ICRA 2024poster

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 to improve the robustness of LiDAR-inertial odometry in geometrically degenerate scenarios, like tunnels or flat fields…

Cited by 23Source ↗code ↗Scholar ↗
2024

TULIP: Transformer for Upsampling of LiDAR Point Clouds

Bin Yang, Patrick Pfreundschuh, Roland Siegwart, Marco Hutter, Peyman Moghadam, Vaishakh Patil
CVPR 2024poster

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 works propose to solve this problem by converting LiDAR data from 3D Euclidean space into an image super-resolution prob…

Cited by 6Source ↗PDF ↗code ↗Scholar ↗
2023

Dynablox: Real-Time Detection of Diverse Dynamic Objects in Complex Environments

Lukas Schmid, Olov Andersson, Aurelio L. Sulser, Patrick Pfreundschuh, Roland Siegwart
RA-L 2023

Real-time detection of moving objects is an essential capability for robots acting autonomously in dynamic environments. We thus propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dynablox</i> , a novel online mapping-based approach for robust m

Cited by 82Source ↗code ↗Scholar ↗
2023

Resilient Terrain Navigation with a 5 DOF Metal Detector Drone

Patrick Pfreundschuh, Rik Bähnemann, Tim Kazik, Thomas Mantel, Roland Siegwart, Olov Andersson
ICRA 2023poster

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 detectors are the standard detection tool but must be operated close to and parallel to the terrain. A successful combinati…

Cited by 3Source ↗Scholar ↗
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 ↗
2020

MOZARD: Multi-Modal Localization for Autonomous Vehicles in Urban Outdoor Environments

Lukas Schaupp, Patrick Pfreundschuh, Mathias Bürki, Cesar Cadena, Roland Siegwart, Juan Nieto
IROS 2020poster

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 localization system for urban outdoor environments using vision and LiDAR. By fusing key point based visual multi-session…

Cited by 2Source ↗Scholar ↗