Surfel-LIO：基于预计算Surfel和层次Z-order体素哈希的快速激光雷达-惯性里程计
Surfel-LIO: Fast LiDAR-Inertial Odometry with Pre-computed Surfels and Hierarchical Z-order Voxel Hashing
AI总结：
Surfel-LIO通过预计算Surfel和层次Z-order体素哈希实现快速激光雷达-惯性里程计，提升处理速度并保持精度。
摘要：
激光雷达-惯性里程计（LIO）是一个活跃的研究领域，因为它能够在外星导航环境下实现准确的实时状态估计。近年来，地图数据结构和空间索引的进展显著提高了LIO系统的效率。然而，我们发现有两个方面仍有改进空间：（1）最近邻搜索通常需要检查多个空间单元以收集足够的点进行平面拟合；（2）平面参数通常在每次迭代中重新计算，尽管地图几何未变。受这些观察的启发，我们提出了Surfel-LIO，它使用带有预计算Surfel表示的层次体素结构（hVox）。这种设计使得在不进行运行时邻居枚举或平面拟合的情况下实现O(1)的对应关系检索，结合Z-order曲线编码实现缓存友好的空间索引。在M3DGR数据集上的实验结果表明，我们的方法在保持与最新最先进的方法相当的状态估计精度的同时，实现了显著更快的处理速度。我们的实现已公开在https://github.com/93won/lidar_inertial_odometry。
英文摘要：
LiDAR-inertial odometry (LIO) is an active research area, as it enables accurate real-time state estimation in GPS-denied environments. Recent advances in map data structures and spatial indexing have significantly improved the efficiency of LIO systems. Nevertheless, we observe that two aspects may still leave room for improvement: (1) nearest neighbor search often requires examining multiple spatial units to gather sufficient points for plane fitting, and (2) plane parameters are typically recomputed at every iteration despite unchanged map geometry. Motivated by these observations, we propose Surfel-LIO, which employs a hierarchical voxel structure (hVox) with pre-computed surfel representation. This design enables O(1) correspondence retrieval without runtime neighbor enumeration or plane fitting, combined with Z-order curve encoding for cache-friendly spatial indexing. Experimental results on the M3DGR dataset demonstrate that our method achieves significantly faster processing speed compared to recent state-of-the-art methods while maintaining comparable state estimation accuracy. Our implementation is publicly available at https://github.com/93won/lidar_inertial_odometry.
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。
