R-VoxelMap: 通过递归平面拟合实现高精度体素映射以提升在线激光雷达里程计精度
R-VoxelMap: Accurate Voxel Mapping with Recursive Plane Fitting for Online LiDAR Odometry
AI总结：
R-VoxelMap通过递归平面拟合提升在线激光雷达里程计精度，有效解决异常值和平面合并问题，实现高精度体素映射。
摘要：
本文提出R-VoxelMap，一种新的体素映射方法，通过几何驱动的递归平面拟合策略构建准确的体素地图，以提高在线激光雷达里程计的定位精度。VoxelMap及其变体通常使用voxel内的所有点来拟合和检查平面，这可能导致由异常值引起的平面参数偏差、大平面的过度分割以及不同物理平面之间的错误合并。为了解决这些问题，R-VoxelMap采用基于异常值检测和重用流水线的几何驱动递归构建策略。具体来说，对于每个voxel，首先使用随机采样一致性（RANSAC）分离异常值来拟合准确的平面。剩下的异常值随后被传播到更深层次的八叉树层级进行递归处理，确保对环境的详细表示。此外，还设计了一种基于点分布的有效性检查算法，以防止错误的平面合并。在多种开源激光雷达（惯性）同时定位与建图（SLAM）数据集上的广泛实验验证了，我们的方法在准确性上优于其他最先进的方法，具有可比的效率和内存使用。代码将在GitHub上提供。
英文摘要：
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.
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。
