This paper proposes R-VoxelMap, a novel voxel mapping method that constructs accurate voxel maps using a geometry-driven recursive plane fitting strategy to enhance the localization accuracy of online LiDAR odometry. VoxelMap and its variants typically fit and check planes using all points in a voxel, which may lead to plane parameter deviation caused by outliers, over segmentation of large planes, and incorrect merging across different physical planes. To address these issues, R-VoxelMap utilizes a geometry-driven recursive construction strategy based on an outlier detect-and-reuse pipeline. Specifically, for each voxel, accurate planes are first fitted while separating outliers using random sample consensus (RANSAC). The remaining outliers are then propagated to deeper octree levels for recursive processing, ensuring a detailed representation of the environment. In addition, a point distribution-based validity check algorithm is devised to prevent erroneous plane merging. Extensive experiments on diverse open-source LiDAR(-inertial) simultaneous localization and mapping (SLAM) datasets validate that our method achieves higher accuracy than other state-of-the-art approaches, with comparable efficiency and memory usage. Code will be available on GitHub.

翻译：本文提出R-VoxelMap，一种新颖的体素建图方法，采用几何驱动的递归平面拟合策略构建精确体素地图，以提升在线激光雷达里程计的定位精度。传统VoxelMap及其变体通常使用体素内全部点云进行平面拟合与校验，易因离群点导致平面参数偏差、大平面过度分割以及不同物理平面的错误合并。为解决这些问题，R-VoxelMap基于离群点检测-复用流程，采用几何驱动的递归构建策略。具体而言，对每个体素首先通过随机采样一致性（RANSAC）算法分离离群点并拟合精确平面，随后将剩余离群点传递至更深的八叉树层级进行递归处理，从而实现对环境的精细表征。此外，本文设计了基于点云分布的有效性校验算法以防止错误平面合并。在多种开源激光雷达（-惯性）即时定位与建图（SLAM）数据集上的大量实验表明，本方法在保持相近计算效率与内存占用的同时，相较于其他先进方法具有更高的精度。代码将在GitHub平台开源。