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.

翻译：本文提出VoxelMap++：一种基于平面合并的体素建图方法，可有效提升基于激光雷达（-惯性）的同时定位与建图（SLAM）的精度与效率。该地图由一组包含三维自由度（3DOF）平面特征及其协方差估计的体素构成。考虑到全局地图中包含大量共面特征（子平面），这些子平面的三维自由度估计可视为较大平面（父平面）带有协方差的观测值。因此，我们设计了一种基于并查集的平面合并模块，既能节省资源又可进一步提升平面拟合精度。该模块可区分不同体素中的子平面，并通过合并这些子平面来估计父平面。合并后，父平面的三维自由度表示比子平面更精确，且不确定性显著降低，从而进一步提升激光雷达（-惯性）里程计的性能。在走廊、森林等复杂环境中的实验表明，相较其他前沿方法，我们的方法具有更高的精度与效率（详见附赠视频）。此外，我们的VoxelMap++实现已在GitHub开源，适用于非重复扫描型与传统扫描型激光雷达。