This article describes the method CFEAR Radar odometry, submitted to a competition at the Radar in Robotics workshop, ICRA 2024. 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 se -- 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 0.66% 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 https://github.com/dan11003/cfear_2024_workshop.

翻译：本文描述了CFEAR雷达里程计方法，该方法提交至2024年ICRA机器人雷达研讨会竞赛。CFEAR是一种高效且精确的旋转式二维雷达里程计方法，具有良好的跨环境泛化能力。本文概述了该里程计流程，并在公共Boreas数据集上进行了新实验。研究表明，采用原始参数配置的实时CFEAR方案在Boreas数据集上展现出令人惊讶的低漂移。此外，我们讨论了一种改进的实现方案与求解策略，使最高精度配置能以68 Hz帧率实时运行且鲁棒性提升，平移漂移低至0.66%。近期已向社区开源代码（https://github.com/dan11003/CFEAR_Radarodometry_code_public），并发布了本文的评估结果（https://github.com/dan11003/cfear_2024_workshop）。