用广义矩方法实现稀疏毫米波雷达点云的高精度配准
Registering the 4D Millimeter Wave Radar Point Clouds Via Generalized Method of Moments
- 无需显式点对点匹配，直接利用点云统计特性进行配准
- 在真实与合成数据上精度优于基准方法，接近激光雷达水平
- 适合极端天气下机器人感知，尤其适用于稀疏噪声雷达数据
4D毫米波雷达是新兴传感器，可提供带位置和径向速度信息的点云。相比激光雷达，其成本更低、在恶劣天气下更可靠。点云配准是机器人定位与建图（SLAM）等应用中的关键模块。然而，4D雷达点云稀疏且噪声大，导致配准难度显著增加。为此，本文提出基于广义矩方法的雷达点云配准框架，无需显式点对点对应关系，该特性对稀疏雷达点云尤为重要。我们证明了该方法的一致性。在合成及真实数据集上的实验表明，本方法在准确性和鲁棒性上均优于现有基准，精度甚至可媲美激光雷达方案。
原文摘要 · Abstract (English)
4D millimeter wave radars (4D radars) are new emerging sensors that provide point clouds of objects with both position and radial velocity measurements. Compared to LiDARs, they are more affordable and reliable sensors for robots' perception under extreme weather conditions. On the other hand, point cloud registration is an essential perception module that provides robot's pose feedback information in applications such as Simultaneous Localization and Mapping (SLAM). Nevertheless, the 4D radar point clouds are sparse and noisy compared to those of LiDAR, and hence we shall confront great challenges in registering the radar point clouds. To address this issue, we propose a point cloud registration framework for 4D radars based on Generalized Method of Moments. The method does not require explicit point-to-point correspondences between the source and target point clouds, which is difficult to compute for sparse 4D radar point clouds. Moreover, we show the consistency of the proposed method. Experiments on both synthetic and real-world datasets show that our approach achieves higher accuracy and robustness than benchmarks, and the accuracy is even comparable to LiDAR-based frameworks.
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