  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.
