MSPA-LIO: LiDAR-Inertial Odometry with Multi-Scale Plane Adjustment

Abstract

Most current LiDAR-based odometry methods use point-to-local plane registration to constrain poses, ignoring the explicit plane structure in the environment. Due to noise interference and uneven distribution of point cloud, local planes are prone to tilt, resulting in registration errors. Therefore, we propose MSPA-LIO, a LiDAR-Inertial odometry with multi-scale plane adjustment, which uses geometric constraints and plane adjustment at both local voxel plane scale and large plane scale to improve odometry accuracy and enhance map consistency. In order to make full use of the planar structure in the environment, we propose an explicit large plane extraction method based on the voxel-based. We use large planes to correct the direction of the associated voxel planes, thereby overcoming the misregistration problem caused by local plane tilt. To further improve the odometry accuracy, we perform plane adjustments at the voxel plane scale and the large plane scale to make the pose and map more consistent. Experiments conducted on the VECtor Dataset and the Newer College Dataset demonstrate that our proposed algorithm outperforms four state-of-the-art algorithms.