arXiv 2308.027992023-08-05cs.RO

VoxelMap++：用于在线LiDAR(-惯性)里程计的可合并体素建图方法

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

Institute of Information and Communication Engineering, University of Electronic Science and Technology of China（电子科技大学信息与通信工程研究院）

Yifei Yuan, Chang Wu, Yuan You, Xiaotong Kong, Ying Zhang, Qiyan Li

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AI总结：

本文提出VoxelMap++，通过基于并查集的平面合并模块将体素中的共面子平面合并为更精确的父平面，在降低不确定性的同时提升LiDAR(-惯性)SLAM的精度与效率，并在走廊、森林等挑战性环境中验证了其优越性。

摘要：

本文提出了VoxelMap++：一种具有平面合并能力的体素建图方法，能够有效提升基于LiDAR(-惯性)的同时定位与建图（SLAM）的精度和效率。该地图是一组体素的集合，每个体素包含一个具有3自由度表示及相应协方差估计的平面特征。考虑到整个地图会包含大量共面特征（子平面），这些子平面的3自由度估计可以被视为一个更大平面（父平面）的带协方差测量。因此，我们设计了一个基于并查集的平面合并模块，该模块能够节省资源并进一步提高平面拟合的精度。该模块可以区分不同体素中的子平面，并将这些子平面合并以估计父平面。合并后，父平面的3自由度表示将比子平面更精确，不确定性将显著降低，从而进一步提高LiDAR(-惯性)里程计的性能。在走廊和森林等具有挑战性的环境中的实验表明，与其他最先进的方法相比，我们的方法具有高精度和高效率（见随附视频）。此外，我们的实现VoxelMap++已在GitHub上开源，适用于非重复扫描LiDAR和传统扫描LiDAR。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。