CFEAR Radarodometry -- Conservative Filtering for Efficient and Accurate Radar Odometry
Abstract
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.
Cite
@article{arxiv.2105.01457,
title = {CFEAR Radarodometry -- Conservative Filtering for Efficient and Accurate Radar Odometry},
author = {Daniel Adolfsson and Martin Magnusson and Anas Alhashimi and Achim J. Lilienthal and Henrik Andreasson},
journal= {arXiv preprint arXiv:2105.01457},
year = {2021}
}
Comments
Accepted for IROS 2021