R3O: Robust Radon Radar Odometry

Daniel Louback S. Lubanco, Ahmed Hashem, Markus Pichler-Scheder, Andreas Stelzer, Reinhard Feger, Thomas Schlechter

Research output: Contribution to journalArticlepeer-review

Abstract

Being able to measure and track positions of mobile systems is an important capability in many applications, autonomous driving being one major example. With the Robust Radon Radar Odometry algorithm, this article presents an approach for estimating the odometry of vehicles based on radar data only. The algorithm embodies a robust method for estimating the change in orientation as a key feature. The odometry algorithm is under the realm of direct methods, and it exploits properties of the Fourier transform for decoupling the changes in orientation from the changes in translation. In the first step, the Radon transform along with phase-correlation, outlier removal, robust measure of central tendency, keyframe selection and graph optimization are used in order to achieve a robust method for estimating the change in orientation, next the translation is estimated with the support of phase-correlation. The algorithm's performance was evaluated with real world data. Significant improvements in position and orientation error in terms of relative pose error and the KITTI odometry error metric are shown as compared to other direct methods for radar based odometry.

Original languageEnglish
Article number1
Pages (from-to)231-246
Number of pages16
JournalIEEE Transactions on Intelligent Vehicles
Volume9
Issue number1
DOIs
Publication statusPublished - 1 Jan 2024

Keywords

  • Autonomous Vehicles
  • Estimation
  • Odometry
  • Phase-Correlation
  • Radar
  • Radar imaging
  • Radon
  • Radon Transform
  • Spaceborne radar
  • Uncertainty
  • autonomous vehicles
  • odometry
  • radon transform
  • phase-correlation

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