FR-LIO：基于紧耦合迭代卡尔曼平滑器与机器人中心体素的快速鲁棒激光惯性里程计
FR-LIO: Fast and Robust Lidar-Inertial Odometry by Tightly-Coupled Iterated Kalman Smoother and Robocentric Voxels
AI总结：
针对剧烈运动下激光惯性里程计易失效的问题，提出基于自适应子帧、紧耦合迭代ESKS及RC-Vox的FR-LIO，在高动态场景稳定跟踪且速度优于现有高效LIO系统。
摘要：
本文提出了一种对剧烈运动具有鲁棒性的快速激光惯性里程计（LIO）。为在剧烈运动场景下实现鲁棒跟踪，本文利用激光雷达的连续扫描特性，根据运动强度将完整扫描自适应划分为多个局部扫描（称为子帧）。为避免子帧因约束不足导致性能退化，本文提出一种基于紧耦合迭代误差状态卡尔曼平滑器（ESKS）框架的鲁棒状态估计方法。此外，本文提出机器人中心体素地图（RC-Vox）以提升系统效率，该地图通过将局部地图点映射到固定大小的双层3D数组结构，实现地图点的高效维护与k近邻（k-NN）查询。本文在4个公开数据集的27个序列及自有数据集上开展了大量实验。结果表明，本文系统可在角速度最高达21.8 rad/s的剧烈运动场景下实现稳定跟踪，而其他现有最优方法无法处理此类场景；同时在常规场景下，本文系统的性能与这些方法相当。此外，得益于RC-Vox，本文系统的运行速度远快于目前已发表的最高效LIO系统。
英文摘要：
This paper presents a fast lidar-inertial odometry (LIO) that is robust to aggressive motion. To achieve robust tracking in aggressive motion scenes, we exploit the continuous scanning property of lidar to adaptively divide the full scan into multiple partial scans (named sub-frames) according to the motion intensity. And to avoid the degradation of sub-frames resulting from insufficient constraints, we propose a robust state estimation method based on a tightly-coupled iterated error state Kalman smoother (ESKS) framework. Furthermore, we propose a robocentric voxel map (RC-Vox) to improve the system's efficiency. The RC-Vox allows efficient maintenance of map points and k nearest neighbor (k-NN) queries by mapping local map points into a fixed-size, two-layer 3D array structure. Extensive experiments are conducted on 27 sequences from 4 public datasets and our own dataset. The results show that our system can achieve stable tracking in aggressive motion scenes (angular velocity up to 21.8 rad/s) that cannot be handled by other state-of-the-art methods, while our system can achieve competitive performance with these methods in general scenes. Furthermore, thanks to the RC-Vox, our system is much faster than the most efficient LIO system currently published.
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。
