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Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information

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arxiv 1909.03752 v4 pith:PXUH4XAR submitted 2019-09-09 cs.CV cs.LGcs.RO

Dan Barnes , Rob Weston , Ingmar Posner This is my paper · ORCID
classification cs.CVcs.LGcs.RO
keywords radarodometryposesystemapproachestimatesinformationallows
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This paper presents an end-to-end radar odometry system which delivers robust, real-time pose estimates based on a learned embedding space free of sensing artefacts and distractor objects. The system deploys a fully differentiable, correlation-based radar matching approach. This provides the same level of interpretability as established scan-matching methods and allows for a principled derivation of uncertainty estimates. The system is trained in a (self-)supervised way using only previously obtained pose information as a training signal. Using 280km of urban driving data, we demonstrate that our approach outperforms the previous state-of-the-art in radar odometry by reducing errors by up 68% whilst running an order of magnitude faster.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DRO: Doppler-Aware Direct Radar Odometry

    cs.RO 2025-04 conditional novelty 7.0 of 10

    Direct radar odometry that uses all radar intensity information, continuous-time motion and Doppler distortion correction, and an optional Doppler-based velocity constraint outperforms point-based radar odometry on dr...

  2. FD-RIO: Fast Dense Radar Inertial Odometry

    cs.RO 2025-05 conditional novelty 5.0 of 10

    FD-RIO fuses dense phase-correlation radar odometry with IMU data in a Kalman filter, achieving state-of-the-art trajectory accuracy on two public datasets with low computational cost.

  3. RaSCL: Radar to Satellite Crossview Localization

    cs.RO 2025-04 conditional novelty 5.0 of 10

    Radar-to-satellite crossview localization with learned occupancy prediction, ICP registration, and factor-graph fusion achieves 1.3 to 4.8 m RMSE on Boreas, Oxford, and a marine dataset.

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