CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry
Daniel Adolfsson, Martin Magnusson, Anas Alhashimi, Achim J. Lilienthal, Henrik Andreasson
Abstract
This paper presents an accurate, highly efficient and learning free method for large-scale radar odometry estimation. By using a simple filtering technique that keeps the strongest returns, we produce a clean radar data representation and reconstruct surface normals 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. Drift is additionally reduced by jointly registering the latest scan to a history of keyframes. We found that our odometry pipeline generalize well 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 merely on a single laptop CPU thread at 55 Hz.
BibTeX
@inproceedings{iros2021_cfearradarodomet,
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},
booktitle = {IROS 2021},
year = {2021}
}