Remove, then Revert: Static Point cloud Map Construction using Multiresolution Range Images

Abstract

We present a novel static point cloud map construction algorithm, called Removert, for use within dynamic urban environments. Leaving only static points and excluding dynamic objects is a critical problem in various robust robot missions in changing outdoors, and the procedure commonly contains comparing a query to the noisy map that has dynamic points. In doing so, however, the estimated discrepancies between a query scan and the noisy map tend to possess errors due to imperfect pose estimation, which degrades the static map quality. To tackle the problem, we propose a multiresolution range image-based false prediction reverting algorithm. We first conservatively retain definite static points and iteratively recover more uncertain static points by enlarging the query-to- map association window size, which implicitly compensates the LiDAR motion or registration errors. We validate our method on the KITTI dataset using SemanticKITTI as ground truth, and show our method qualitatively competes or outperforms the human-labeled data (SemanticKITTI) in ambiguous regions.