3D LiDAR Mapping in Dynamic Environments using a 4D Implicit Neural Representation

Building accurate maps is a key building block to enable reliable localization, planning, and navigation of autonomous vehicles. We propose a novel approach for building accurate maps of dynamic environments utilizing a sequence of LiDAR scans. To this end, we propose encoding the 4D scene into a novel spatio-temporal implicit neural map representation by fitting a time-dependent truncated signed distance function to each point. Using our representation, we extract the static map by filtering the dynamic parts. Our neural representation is based on sparse feature grids, a globally shared decoder, and time-dependent basis functions, which we jointly optimize in an unsupervised fashion. To learn this representation from a sequence of LiDAR scans, we design a simple yet efficient loss function to supervise the map optimization in a piecewise way. We evaluate our approach 1 on various scenes containing moviing objects in terms of the reconstruction quality of static maps and the segmentation of dynamic point clouds. The experimental results demonstrate that our method is capable of removing the dynamic part of the input point clouds while reconstructing accurate and complete 3D maps, outperforming several state-of-the-art methods

  • Published in:
    IEEE/CVF Conference on Computer Vision and Pattern Recognition
  • Type:
    Inproceedings
  • Authors:
    Zhong, Xiong; Pan, Yue; Stachniss, Cyrill; Behley, Jens
  • Year:
    2024

Citation information

Zhong, Xiong; Pan, Yue; Stachniss, Cyrill; Behley, Jens: 3D LiDAR Mapping in Dynamic Environments using a 4D Implicit Neural Representation, IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, https://www.ipb.uni-bonn.de/pdfs/zhong2024cvpr.pdf, Zhong.etal.2024a,

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