Radar-Only Odometry and Mapping for Autonomous Vehicles

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 approach that leverages the velocity information provided by 3D radar sensors. The second one is advantageous for 2D radars with a low number of samples, and particularly useful for scenarios where the sensor is being blocked by large dynamic obstacles. It exploits a constant velocity filter and the measured Doppler velocities to estimate the vehicle’s ego-motion. We enrich this with a filtering step to improve the accuracy of the points in the resulting map. We put our work to the test using the View of Delft and NuScenes datasets, which involve 3D and 2D radar sensors. Our findings illustrate state-of-the-art performance of our odometry techniques in terms of accuracy when compared to existing alternatives. Moreover, we demonstrate that our map filtering methodology achieves higher similarity rates than the raw unfiltered map when benchmarked against a corresponding LiDAR map.

  • Veröffentlicht in:
    IEEE International Conference on Robotics and Automation
  • Typ:
    Inproceedings
  • Autoren:
    Casado Herraez, Daniel; Zeller, Matthias; Chang, Le; Vizzo, Ignacio; Heidingsfeld, Michael; Stachniss, Cyrill
  • Jahr:
    2024

Informationen zur Zitierung

Casado Herraez, Daniel; Zeller, Matthias; Chang, Le; Vizzo, Ignacio; Heidingsfeld, Michael; Stachniss, Cyrill: Radar-Only Odometry and Mapping for Autonomous Vehicles, IEEE International Conference on Robotics and Automation, 2024, https://www.ipb.uni-bonn.de/pdfs/casado-herraez2024icra.pdf, Casado.Herraez.etal.2024a,

Assoziierte Lamarr-ForscherInnen

lamarr institute person Stachniss Cyrill e1663922306234 - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Prof. Dr. Cyrill Stachniss

Principal Investigator Embodied AI zum Profil