Lidar-Level Localization with Radar? the CFEAR Approach to Accurate, Fast, and Robust Large-Scale Radar Odometry in Diverse Environments

Daniel Adolfsson, Martin Magnusson, Anas Alhashimi, Achim J. Lilienthal, Henrik Andreasson

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

This article presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments - outdoors, from urban to woodland, and indoors in warehouses and mines - without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach conservative filtering for efficient and accurate radar odometry (CFEAR), we present an in-depth investigation on a wider range of datasets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar simultaneous localization and mapping (SLAM) and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5 Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160 Hz.

Original languageEnglish
Pages (from-to)1476-1495
Number of pages20
JournalIEEE Transactions on Robotics
Volume39
Issue number2
DOIs
StatePublished - 1 Apr 2023
Externally publishedYes

Keywords

  • Localization
  • SLAM
  • radar odometry
  • range sensing

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