ERASOR2: Instance-Aware Robust 3D Mapping of the Static World in Dynamic Scenes

Hyungtae Lim
Korea Advanced Institute of Science & Technology
Lucas Nunes
University of Bonn
Benedikt Mersch
University of Bonn
Xieyuanli Chen
the College of Intelligence Science and Technology, National University of Defense Technology
Jens Behley
University of Bonn
Hyun Myung
Korea Advanced Institute of Science & Technology
Cyrill Stachniss
University of Bonn
Paper Website

Paper ID 67

Session 9. Robot State Estimation

Poster Session Thursday, July 13

Poster 3

Abstract: A map of the environment is an essential component for robotic navigation. In the majority of cases, a map of the static part of the world is the basis for localization, planning, and navigation. However, dynamic objects that are presented in the scenes during mapping leave undesirable traces in the map, which can impede mobile robots from achieving successful robotic navigation. To remove the artifacts caused by dynamic objects in the map, we propose a novel instance-aware map building method. Our approach rejects dynamic points at an instance-level while preserving most static points by exploiting instance segmentation estimates. Furthermore, we propose effective ways to consider the erroneous estimates of instance segmentation, enabling our proposed method to be robust even under imprecise instance segmentation. As demonstrated in our experimental evaluation, our approach shows substantial performance increases in terms of both, the preservation of static points and rejection of dynamic points. Our code will be made available on publication.