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.

中文翻译：

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CFEAR Radarodometry-保守过滤，实现高效，精确的雷达里程表

本文提出了一种用于大规模雷达里程计估计的准确，高效且无需学习的方法CFEAR Radarodometry。通过使用保持每个方位k个最强返回值的滤波技术，并通过对笛卡尔空间中的雷达数据进行附加滤波，我们能够计算出稀疏的定向曲面点集，以实现高效，准确的扫描匹配。通过最小化点对线度量进行注册，并使用Huber损失实现对异常值的鲁棒性。通过联合将最新的扫描记录到关键帧的历史记录中，我们能够进一步减少漂移，并发现我们的里程计方法可以推广到不同的传感器模型和数据集，而无需更改单个参数。