PLACE-LIO: Plane-Centric LiDAR-Inertial Odometry

Linkun He ,BofengLi，Guang’e Chen

Abstract

Planes provide effective and reliable constraints for a LiDAR (-Inertial) Odometry method to achieve accurate pose estimation. Typically, one can readily construct local planes by nearestneighborsearchorvoxelization.Comparedtoglobalplanes (GPs), these local planes are of lower confidence and always intro duce many redundant constraints that may impair the real-time capability. Hence, in this letter, we explicitly extract GPs using a modified uncertainty-guided plane segmentation approach. On this basis, we propose the plane-centric lidar-inertial odometry (PLACE-LIO) method combined with a plane-occupied voxel grid for map representation. Moreover, the proposed LIO system does notsolelyrelyonGPs,whichleadstolimitedapplications.Wemake full use of the scans via a hierarchical data association scheme, and three types of correspondences (i.e., point-to-point, point-to-plane andplane-to-plane)areutilized.WevalidatetheproposedPLACE LIO on diverse public datasets, and make comparison with other state-of-the-art methods. Index Terms—Localization, sensor fusion, global plane. , and Guang’e Chen