Local geometric information, i.e. normal and distribution of points, is crucial for LiDAR-based simultaneous localization and mapping (SLAM) because it provides constraints for data association, which further determines the direction of optimization and ultimately affects the accuracy of localization. However, estimating normal and distribution of points are time-consuming tasks even with the assistance of kdtree or volumetric maps. To achieve fast normal estimation, we look into the structure of LiDAR scan and propose a ring-based fast approximate least squares (Ring FALS) method. With the Ring structural information, estimating the normal requires only the range information of the points when a new scan arrives. To efficiently estimate the distribution of points, we extend the ikd-tree to manage the map in voxels and update the distribution of points in each voxel incrementally while maintaining its consistency with the normal estimation. We further fix the distribution after its convergence to balance the time consumption and the correctness of representation. Based on the extracted and maintained local geometric information, we devise a robust and accurate hierarchical data association scheme where point-to-surfel association is prioritized over point-to-plane. Extensive experiments on diverse public datasets demonstrate the advantages of our system compared to other state-of-the-art methods. Our open source implementation is available at https://github.com/tiev-tongji/LOG-LIO.

翻译：局部几何信息（即点的法向量与分布）是激光雷达同步定位与建图的关键要素，因为它为数据关联提供了约束，进而决定了优化方向并最终影响定位精度。然而，即使在kd树或体素地图的辅助下，估计点的法向量与分布仍是耗时任务。为实现快速法向量估计，我们深入分析激光雷达扫描结构，提出一种基于环形的快速近似最小二乘方法。利用环形结构信息，当新扫描数据到达时，仅需点的距离信息即可完成法向量估计。为高效估计点的分布，我们将ikd树扩展至体素地图管理，并以增量方式更新各体素内的点分布，同时保持其与法向量估计的一致性。我们进一步在分布收敛后将其固定，以平衡时间消耗与表达准确性。基于提取并维护的局部几何信息，我们设计了一种稳健且精确的分层数据关联方案，其中点-面元关联优先于点-平面关联。在多个公开数据集上的大量实验表明，与现有先进方法相比，本系统具有显著优势。我们的开源实现可在https://github.com/tiev-tongji/LOG-LIO获取。