This paper presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments -- outdoors, from urban to woodland, and indoors in warehouses and mines - without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach CFEAR, we present an in-depth investigation on a wider range of data sets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar SLAM and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160Hz.

翻译：本文提出了一种精确、高效且无需学习的大规模里程计估计方法，采用旋转雷达传感器。实验证明，该方法在极多样化的环境（包括室外——从城市到林地，以及室内——如仓库和矿井）中无需调整参数便能良好泛化。该方法将运动补偿集成于扫描周期内，通过一对多扫描配准最小化邻近有向曲面点之间的距离，并利用鲁棒损失函数抑制异常值影响。在前期工作CFEAR的基础上，本文在更广泛的数据集上进行了深入研究，量化了滤波、分辨率、配准代价与损失函数、关键帧历史以及运动补偿等因素的重要性。我们提出了一种新的求解策略与配置，克服了此前在稀疏性与偏差方面的局限，并将现有最优方法的性能提升38%。令人惊讶的是，该方法不仅超越了雷达SLAM，且逼近激光雷达SLAM的性能。最高精度配置在牛津基准测试中以5Hz频率运行时误差为1.09%，最快配置以160Hz频率运行时误差为1.79%。