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

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 is available at https://github.com/url-kaist/ERASOR2.

  • Published in:
    Robotics: Science and Systems
  • Type:
    Inproceedings
  • Authors:
    Lim, Hyungtae; Nunes, Lucas; Mersch, Benedikt; Chen, Xieyunali; Behley, Jens; Myung, Hyun; Stachniss, Cyrill
  • Year:
    2023

Citation information

Lim, Hyungtae; Nunes, Lucas; Mersch, Benedikt; Chen, Xieyunali; Behley, Jens; Myung, Hyun; Stachniss, Cyrill: ERASOR2: Instance-Aware Robust 3D Mapping of the Static World in Dynamic Scenes, Robotics: Science and Systems, 2023, https://www.ipb.uni-bonn.de/pdfs/lim2023rss.pdf, Lim.etal.2023a,

Associated Lamarr Researchers

lamarr institute person Stachniss Cyrill e1663922306234 - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Prof. Dr. Cyrill Stachniss

Principal Investigator Embodied AI to the profile