LiDAR-Inertial Odometry in Dynamic Driving Scenarios using Label Consistency Detection
<|reference_start|>LiDAR-Inertial Odometry in Dynamic Driving Scenarios using Label Consistency Detection: 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 map to identify moving objects. Firstly, the binarized labels, i.e., ground and non-ground are assigned to each 3D point in current sweep using ground segmentation. In actual driving scenarios, dynamic objects are always located on the ground. For most points scanned from moving objects, they cannot coincide with any existing structures in space. For a minority of moving objects' points that are close to the ground, their labels exhibit differences with surrounding ground points. Thus, the points on moving objects are identified due to lacking of nearest neighbors in map or inconsistency with the labels of surround ground points. The nearest neighbors from global map are localized by voxel-location-based nearest neighbor search and the consistency is evaluated by comparing the label consistency with nearest neighbors, without involving any massive computations. Finally, the points on moving objects are removed. The proposed method is embeded into a self-developed LIO system (i.e., Dynamic-LIO), evaluated with six public datasets, and tested in both dynamic and static environments. Experimental results demonstrate that our method can identify moving objects with extremlely low computational overhead (i.e., 1-9ms/sweep), and our Dynamic-LIO can achieve state-of-the-art pose estimation accuracy in both static and dynamic scenarios. We have released the source code of this work for the development of the community.<|reference_end|>
@article{yuan2024lidar-inertial,
    title={LiDAR-Inertial Odometry in Dynamic Driving Scenarios using Label
  Consistency Detection},
    author={Zikang Yuan, Xiaoxiang Wang, Jingying Wu, Junda Cheng, Xin Yang},
    journal={arXiv preprint arXiv:2407.03590},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03590},
    primaryClass={cs.RO}
}