PGP-DOR: A Point-Grid-Point Scheme for Efficient Dynamic Object Removal

Abstract

In the field of autonomous driving, constructing high-precision maps, typically represented as 3D point cloud maps or bird's-eye view (BEV) grid maps, is essential for both offline and online applications. However, the presence of dynamic objects within a scene can introduce artifacts and noise that significantly degrade the quality of these maps. To address this challenge, we propose a method in this paper that can accurately identify those dynamic objects in both online and offline settings. Our approach fully exploits the spatio-temporal attributes of BEV grid maps and utilizes a point-grid-point (PGP) scheme to identify moving objects at both the 3D point cloud level and the 2D BEV grid level. Experimental results from public datasets, as well as a self-collected dataset, demonstrate that our method consistently outperforms state-of-the-art approaches in dynamic object removal in both online and offline contexts.