Papers › CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry

CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry

16 Sep 2021IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021 9archive 2025-07-28

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

This paper presents the accurate, highly efficient, and learning-free method CFEAR Radarodometry for large-scale radar odometry estimation. By using a filtering technique that keeps the k strongest returns per azimuth and by additionally filtering the radar data in Cartesian space, we are able to compute a sparse set of oriented surface points for efficient and accurate scan matching. Registration is carried out by minimizing a point-to-line metric and robustness to outliers is achieved using a Huber loss. We were able to additionally reduce drift by jointly registering the latest scan to a history of keyframes and found that our odometry method generalizes to different sensor models and datasets without changing a single parameter. We evaluate our method in three widely different environments and demonstrate an improvement over spatially cross-validated state-of-the-art with an overall translation error of 1.76% in a public urban radar odometry benchmark, running at 55Hz merely on a single laptop CPU thread.

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Code

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Tasks

Radar odometryTranslation

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Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Radar odometry Oxford Radar RobotCar Dataset CFEAR-3-s4 translation error [%] 1.31 #1 of 1 Archive leaderboard report
Translation Oxford Radar RobotCar Dataset CFEAR-3-s50 translation error [%] 1.09 #1 of 1 Archive leaderboard report

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Methods

Huber loss

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