A Dynamic Points Removal Benchmark in Point Cloud Maps

TL;DR

This paper introduces a comprehensive benchmarking framework for evaluating dynamic points removal methods in point cloud maps, aiding researchers in understanding limitations and improving techniques for robotics applications.

Contribution

It presents a unified, extensible benchmark with new metrics and refactored methods, facilitating detailed analysis of dynamic points removal in diverse datasets.

Findings

Benchmark reveals limitations of current methods

New metrics provide deeper analysis of techniques

Code and datasets are publicly available

Abstract

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.