A Dynamic Points Removal Benchmark in Point Cloud Maps
This provides a standardized evaluation tool for researchers in robotics to compare dynamic point removal methods, though it is incremental as it builds on existing techniques.
The authors tackled the problem of dynamic points in point cloud maps adversely affecting robotics tasks like localization and path planning by proposing a unified benchmarking framework with refactored methods and novel metrics, making code and datasets publicly available.
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.