LOG-LIO：一种具有高效局部几何信息估计的激光雷达惯性里程计
LOG-LIO: A LiDAR-Inertial Odometry with Efficient Local Geometric Information Estimation
AI总结：
本文提出LOG-LIO，一种利用环结构快速估计点法向量和增量更新体素分布的高效激光雷达惯性里程计，通过分层数据关联提升定位精度，并在公开数据集上验证了其优越性。
摘要：
局部几何信息，即点的法向量和分布，对于基于激光雷达的同步定位与建图（SLAM）至关重要，因为它为数据关联提供了约束，这进一步决定了优化的方向，并最终影响定位的精度。然而，即使在kdtree或体素地图的辅助下，估计点的法向量和分布也是耗时的任务。为了实现快速的法向量估计，我们研究了激光雷达扫描的结构，并提出了一种基于环的快速近似最小二乘（Ring FALS）方法。利用环结构信息，当新扫描到达时，估计法向量仅需要点的距离信息。为了高效估计点的分布，我们扩展了ikd-tree以体素方式管理地图，并增量更新每个体素中点的分布，同时保持其与法向量估计的一致性。我们在分布收敛后进一步固定分布，以平衡时间消耗和表示的准确性。基于提取和维护的局部几何信息，我们设计了一种鲁棒且准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证明了我们的系统相对于其他最先进方法的优势。我们的开源实现可在https://github.com/tiev-tongji/LOG-LIO获取。
英文摘要：
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.
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。
