Abstract
Point cloud registration is a key step in robotic perception tasks, such as Simultaneous Localization and Mapping (SLAM). It is especially challenging in conditions with sparse points and heavy noise. Traditional registration methods, such as Iterative Closest Point (ICP) and Normal Distributions Transform (NDT), often have difficulties in achieving a robust and accurate alignment under these conditions. In this paper, we propose a registration framework based on moment matching. In particular, the point clouds are regarded as i.i.d. samples drawn from the same distribution observed in the source and target frames. We then match the generalized Gaussian Radial Basis moments calculated from the point clouds to estimate the rigid transformation between two frames. Moreover, such method does not require explicit point-to-point correspondences among the point clouds. We further show the consistency of the proposed method. Experiments on synthetic and real-world datasets show that our approach achieves higher accuracy and robustness than existing methods. In addition, we integrate our framework into a 4D Radar SLAM system. The proposed method significantly improves the localization performance and achieves results comparable to LiDAR-based systems. These findings demonstrate the potential of moment matching technique for robust point cloud registration in sparse and noisy scenarios.
Abstract (translated)
点云配准是机器人感知任务中的关键步骤，例如同步定位与地图构建（SLAM）。在点稀疏且噪声严重的情况下，这一过程特别具有挑战性。传统的配准方法，如迭代最近点法（ICP）和正态分布变换（NDT），在这种条件下往往难以实现稳健而准确的对齐效果。本文中，我们提出了一种基于矩匹配的注册框架。具体而言，我们将点云视为来自源帧和目标帧观察到的同一分布中的独立同分布样本。然后，我们通过计算从点云得出的一般化高斯径向基函数矩来匹配这两个帧之间的刚性变换。此外，该方法不需要明确地在点云之间建立点对点对应关系。我们进一步展示了所提方法的一致性。合成数据集和真实世界数据集的实验表明，我们的方法比现有方法实现了更高的准确性和鲁棒性。此外，我们将框架整合到了一个4D雷达SLAM系统中。提出的这种方法显著改善了定位性能，并达到了与基于LiDAR系统的相当水平的结果。这些发现展示了矩匹配技术在稀疏且噪声严重的场景下实现稳健点云配准的潜力。
URL
https://arxiv.org/abs/2508.02187
