Existing radar sensors can be classified into automotive and scanning radars. While most radar odometry (RO) methods are only designed for a specific type of radar, our RO method adapts to both scanning and automotive radars. Our RO is simple yet effective, where the pipeline consists of thresholding, probabilistic submap building, and an NDT-based radar scan matching. The proposed RO has been tested on two public radar datasets: the Oxford Radar RobotCar dataset and the nuScenes dataset, which provide scanning and automotive radar data respectively. The results show that our approach surpasses state-of-the-art RO using either automotive or scanning radar by reducing translational error by 51% and 30%, respectively, and rotational error by 17% and 29%, respectively. Besides, we show that our RO achieves centimeter-level accuracy as lidar odometry, and automotive and scanning RO have similar accuracy.

翻译：现有雷达传感器可分为车载雷达与扫描雷达两类。多数雷达里程计方法仅针对特定雷达类型设计，而本方法可同时适配扫描雷达与车载雷达。本里程计算法简洁高效，其处理流程包括阈值化处理、概率性子地图构建，以及基于NDT的雷达扫描匹配。所提出的雷达里程计已在两个公开雷达数据集上完成测试：提供扫描雷达数据的牛津雷达机器人车数据集（Oxford Radar RobotCar dataset）与提供车载雷达数据的nuScenes数据集。实验结果表明，本方法分别将基于车载雷达与扫描雷达的最优里程计平移误差降低51%和30%，旋转误差降低17%和29%，在两类雷达上均超越现有最优方法。此外，我们证实本雷达里程计可达到与激光雷达里程计相当的厘米级精度，且车载雷达与扫描雷达的里程计精度相近。