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.Comment: Code check https://github.com/KTH-RPL/DynamicMap_Benchmark.git , 7
pages, accepted by ITSC 202