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Matthias Zeller

10 accepted papers

2026

Multi-Session Mapping and Long-Term Localization for Autonomous Vehicles Using Radar

Daniel Casado Herraez, Matthias Zeller, Dong Wang, Jens Behley, Michael Heidingsfeld, Cyrill Stachniss
RA-L 2026

Localization of autonomous vehicles in existing maps is crucial for reliable navigation. Using previously constructed maps allows vehicles to estimate their pose without the inherent odometry drift. Building such maps involves aligning data recorded at different times and maintaining the map over ti

Cited by 0Source ↗Scholar ↗
2026

Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds (Abstract Reprint)

Matthias Zeller, Vardeep Singh Sandhu, Benedikt Mersch, Jens Behley, Michael Heidingsfeld, Cyrill Stachniss
AAAI 2026technical

The perception of moving objects is crucial for autonomous robots performing collision avoidance in dynamic environments. LiDARs and cameras tremendously enhance scene interpretation but do not provide direct motion information and face limitations under adverse weather. Radar sensors overcome these

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Ground-Aware Automotive Radar Odometry

Daniel Casado Herraez, Franz Kaschner, Matthias Zeller, Dominik Muhle, Jens Behley, Michael Heidingsfeld +2
ICRA 2025

Odometry is crucial for the navigation of autonomous vehicles in unknown environments. While cameras and LiDARs are commonly used to estimate the ego-motion of a vehicle, these sensors face limitations under bad lighting and severe weather conditions. Automotive radars overcome these challenges, but

Cited by 5Source ↗Scholar ↗
2025

RaI-SLAM: Radar-Inertial SLAM for Autonomous Vehicles

Daniel Casado Herraez, Matthias Zeller, Dong Wang, Jens Behley, Michael Heidingsfeld, Cyrill Stachniss
RA-L 2025

Simultaneous localization and mapping are essential components for the operation of autonomous vehicles in unknown environments. While localization focuses on estimating the vehicle's pose, mapping captures the surrounding environment to enhance future localization and decision-making. Localization

Cited by 24Source ↗code ↗Scholar ↗
2025

SemRaFiner: Panoptic Segmentation in Sparse and Noisy Radar Point Clouds

Matthias Zeller, Daniel Casado Herraez, Bengisu Ayan, Jens Behley, Michael Heidingsfeld, Cyrill Stachniss
RA-L 2025

Semantic scene understanding, including the perception and classification of moving agents, is essential to enabling safe and robust driving behaviours of autonomous vehicles. Cameras and LiDARs are commonly used for semantic scene understanding. However, both sensor modalities face limitations in a

Cited by 7Source ↗Scholar ↗
2024

Radar Tracker: Moving Instance Tracking in Sparse and Noisy Radar Point Clouds

Matthias Zeller, Daniel Casado Herraez, Jens Behley, Michael Heidingsfeld, Cyrill Stachniss
ICRA 2024poster

Robots and autonomous vehicles should be aware of what happens in their surroundings. The segmentation and tracking of moving objects are essential for reliable path planning, including collision avoidance. We investigate this estimation task for vehicles using radar sensing. We address moving insta…

Cited by 5Source ↗Scholar ↗
2024

Radar-Only Odometry and Mapping for Autonomous Vehicles

Daniel Casado Herraez, Matthias Zeller, Le Chang, Ignacio Vizzo, Michael Heidingsfeld, Cyrill Stachniss
ICRA 2024poster

Odometry and mapping play a pivotal role in the navigation of autonomous vehicles. In this paper, we address the problem of pose estimation and map creation using only radar sensors. We focus on two odometry estimation approaches followed by a mapping step. The first one is a new point-to-point ICP…

Cited by 12Source ↗Scholar ↗
2024

SPR: Single-Scan Radar Place Recognition

Daniel Casado Herraez, Le Chang, Matthias Zeller, Louis Wiesmann, Jens Behley, Michael Heidingsfeld +1
RA-L 2024

Localization is a crucial component for the navigation of autonomous vehicles. It encompasses global localization and place recognition, allowing a system to identify locations that have been mapped or visited before. Place recognition is commonly approached using cameras or LiDARs. However, these s

Cited by 13Source ↗Scholar ↗
2023

Gaussian Radar Transformer for Semantic Segmentation in Noisy Radar Data

Matthias Zeller, Jens Behley, Michael Heidingsfeld, Cyrill Stachniss
RA-L 2023

Scene understanding is crucial for autonomous robots in dynamic environments for making future state predictions, avoiding collisions, and path planning. Camera and LiDAR perception made tremendous progress in recent years, but face limitations under adverse weather conditions. To leverage the full

Cited by 35Source ↗Scholar ↗
2023

Radar Velocity Transformer: Single-scan Moving Object Segmentation in Noisy Radar Point Clouds

Matthias Zeller, Vardeep S. Sandhu, Benedikt Mersch, Jens Behley, Michael Heidingsfeld, Cyrill Stachniss
ICRA 2023poster

The awareness about moving objects in the surroundings of a self-driving vehicle is essential for safe and reliable autonomous navigation. The interpretation of LiDAR and camera data achieves exceptional results but typically requires to accumulate and process temporal sequences of data in order to…

Cited by 9Source ↗Scholar ↗