VoxelMap++: Mergeable Voxel Mapping Method for Online LiDAR(-inertial) Odometry

TL;DR

VoxelMap++ introduces a mergeable voxel mapping technique with plane merging to enhance the accuracy and efficiency of LiDAR(-inertial) SLAM, especially in environments with coplanar features.

Contribution

It proposes a novel plane merging module based on union-find that improves map accuracy and resource efficiency in LiDAR-based SLAM.

Findings

Achieves higher accuracy in corridor and forest environments.

Reduces computational resources through plane merging.

Outperforms state-of-the-art methods in experiments.

Abstract

This paper presents VoxelMap++: a voxel mapping method with plane merging which can effectively improve the accuracy and efficiency of LiDAR(-inertial) based simultaneous localization and mapping (SLAM). This map is a collection of voxels that contains one plane feature with 3DOF representation and corresponding covariance estimation. Considering total map will contain a large number of coplanar features (kid planes), these kid planes' 3DOF estimation can be regarded as the measurements with covariance of a larger plane (father plane). Thus, we design a plane merging module based on union-find which can save resources and further improve the accuracy of plane fitting. This module can distinguish the kid planes in different voxels and merge these kid planes to estimate the father plane. After merging, the father plane 3DOF representation will be more accurate than the kids plane and the uncertainty will decrease significantly which can further improve the performance of LiDAR(-inertial) odometry. Experiments on challenging environments such as corridors and forests demonstrate the high accuracy and efficiency of our method compared to other state-of-the-art methods (see our attached video). By the way, our implementation VoxelMap++ is open-sourced on GitHub which is applicable for both non-repetitive scanning LiDARs and traditional scanning LiDAR.