DORF: A Dynamic Object Removal Framework for Robust Static LiDAR Mapping in Urban Environments
Zhiming Chen, Kun Zhang, Hua Chen, Michael Yu Wang, Wei Zhang, Hongyu Yu
Abstract
3D point cloud maps are widely used in robotic tasks like localization and planning. However, dynamic objects, such as cars and pedestrians, can introduce ghost artifacts during the map generation process, leading to reduced map quality and hindering normal robot navigation. Online dynamic object removal methods are restricted to utilize only local scope information and have limited performance. To address this challenge, we propose DORF (Dynamic Object Removal Framework), a novel coarse-to-fine offline framework that exploits global 4D spatial-temporal LiDAR information to achieve clean static point cloud map generation, which reaches the state-of-the-art performance among existing offline methods. DORF first conservatively preserves the definite static points leveraging the Receding Horizon Sampling (RHS) mechanism proposed by us. Then DORF gradually recovers more ambiguous static points, guided by the inherent characteristic of dynamic objects in urban environments which necessitates their interaction with the ground. We validate the effectiveness and robustness of DORF across various types of highly dynamic datasets.