This article describes the method CFEAR Radar odometry, submitted to a competition at the Radar in Robotics workshop, ICRA 20241. CFEAR is an efficient and accurate method for spinning 2D radar odometry that generalizes well across environments. This article presents an overview of the odometry pipeline with new experiments on the public Boreas dataset. We show that a real-time capable configuration of CFEAR - with its original parameter set - yields surprisingly low drift in the Boreas dataset. Additionally, we discuss an improved implementation and solving strategy that enables the most accurate configuration to run in real-time with improved robustness, reaching as low as 0.61% translation drift at a frame rate of 68 Hz. A recent release of the source code is available to the community https://github.com/dan11003/CFEAR_Radarodometry_code_public, and we publish the evaluation from this article on https://github.com/dan11003/cfear_2024_workshop

翻译：本文介绍了CFEAR雷达里程计方法，该方法提交至ICRA 2024机器人雷达研讨会竞赛。CFEAR是一种面向旋转式二维雷达的高效且精确的里程计算法，能良好地泛化至不同环境。本文概述其里程计流水线，并在公开Boreas数据集上开展了新实验。结果表明，采用原始参数配置的CFEAR实时可行版本在Boreas数据集中展现出极低的漂移。此外，我们讨论了经过改进的实现与求解策略，使最精确配置在提升鲁棒性的同时实现实时运行，帧率达到68 Hz时平移漂移低至0.61%。社区可通过https://github.com/dan11003/CFEAR_Radarodometry_code_public获取最新发布的源代码，并在https://github.com/dan11003/cfear_2024_workshop查阅本文的评估结果。