LiDAR-Inertial Odometry in Dynamic Driving Scenarios using Label Consistency Detection

Zikang Yuan; Xiaoxiang Wang; Jingying Wu; Junda Cheng; Xin Yang

IROS 20252 citations

LiDAR-Inertial Odometry in Dynamic Driving Scenarios using Label Consistency Detection

Zikang Yuan, Xiaoxiang Wang, Jingying Wu, Junda Cheng, Xin Yang

Abstract

In this paper, a LiDAR-inertial odometry (LIO) method that eliminates the influence of moving objects in dynamic driving scenarios is proposed. This method constructs binarized labels for 3D points of current sweep, and utilizes the label difference between each point and its surrounding points in global map to identify moving objects. The surrounding points in global map are localized by voxel-location-based nearest neighbor search, without involving any massive computations. In addition, the proposed method is embeded into a LIO system (i.e., Dynamic-LIO), and achieves state-of-the-art performance on public datasets with extremlely low computational overhead (i.e., 1~9ms/sweep). We have released the source code of this work for the development of the community.

BibTeX

@inproceedings{iros2025_lidarinertialodo,
  title = {LiDAR-Inertial Odometry in Dynamic Driving Scenarios using Label Consistency Detection},
  author = {Zikang Yuan and Xiaoxiang Wang and Jingying Wu and Junda Cheng and Xin Yang},
  booktitle = {IROS 2025},
  year = {2025}
}