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：现有的雷达传感器可以分为汽车雷达和扫描雷达两种类型。虽然大多数雷达辗米法（RO）方法仅针对特定类型的雷达，但我们的 RO 方法适用于扫描和汽车雷达。我们的 RO 方法简单而有效，流程包括阈值分割、概率子图建立和基于 NDT 的雷达扫描匹配。我们的 RO 方法已在两个公共雷达数据集（牛津雷达机器人车数据集和 nuScenes 数据集）上进行了测试，这些数据集提供扫描和汽车雷达数据。结果表明，我们的方法优于使用汽车雷达或扫描雷达的最先进 RO 方法，将平移误差分别降低了51%和30%，旋转误差则分别降低了17%和29%。此外，我们还展示了我们的 RO 达到了与激光雷达辗米法相当的厘米级精度，汽车雷达和扫描雷达的 RO 精度相当。