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.

翻译：本文提出一种对剧烈运动具有鲁棒性的快速激光雷达惯性里程计（LIO）。为在剧烈运动场景中实现鲁棒跟踪，我们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退化，我们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，我们提出机器人中心体素地图（RC-Vox）以提升系统效率。RC-Vox通过将局部地图点映射至固定大小的双层三维数组结构，实现对地图点的高效维护与k近邻（k-NN）查询。我们在4个公开数据集的27个序列及自制数据集上进行了广泛实验。结果表明：在角速度高达21.8 rad/s的剧烈运动场景中（其他先进方法无法处理），本系统可实现稳定跟踪；同时在常规场景下，本系统性能与这些方法相当。此外，得益于RC-Vox，本系统速度显著优于目前已发表的最高效LIO系统。