In the field of robotics, the point cloud has become an essential map representation. From the perspective of downstream tasks like localization and global path planning, points corresponding to dynamic objects will adversely affect their performance. Existing methods for removing dynamic points in point clouds often lack clarity in comparative evaluations and comprehensive analysis. Therefore, we propose an easy-to-extend unified benchmarking framework for evaluating techniques for removing dynamic points in maps. It includes refactored state-of-art methods and novel metrics to analyze the limitations of these approaches. This enables researchers to dive deep into the underlying reasons behind these limitations. The benchmark makes use of several datasets with different sensor types. All the code and datasets related to our study are publicly available for further development and utilization.

翻译：在机器人领域，点云已成为一种重要的地图表示形式。从定位与全局路径规划等下游任务的角度来看，与动态物体对应的点会对其性能产生不利影响。现有用于移除点云中动态点的方法在比较评估和综合分析方面往往缺乏清晰性。因此，我们提出一个易于扩展的统一基准框架，用于评估地图中动态点移除技术。该框架包含重构的最新方法以及新颖的评估指标，用以分析这些方法的局限性，使研究者能够深入探究这些局限性背后的根本原因。该基准利用了多种传感器类型的数据集。我们研究相关的所有代码和数据集均已公开提供，以供进一步开发和应用。