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:本文展示了一种快速的利达- 内皮odology (LIO), 它能对攻击性运动进行强力跟踪。 为了在攻击性运动场景中实现强力跟踪, 我们利用利达连续扫描属性, 根据运动强度, 将全扫描进行适应性地将全扫描分为多个部分扫描( 以子框架命名 ) 。 为了避免亚框架因限制不足而退化, 我们建议了一种强力的州估算方法, 其依据是紧密交错的迭接误差状态 卡尔曼光滑( ESKS) 框架 。 此外, 我们提议了一个 Roboocenter voxel 地图( RC- Vox ), 以提高系统的效率。 RC- Vox 允许通过将本地地图点和 k最近的邻居( k- NNN) 查询点绘制成一个固定大小的双层 3D 阵列结构, 从而避免本地地图站点的切换序。 我们的系统可以稳定地追踪攻击场景( 向21.8 rad rax ) ), 我们的系统可以比已公布的系统更快速地实现这些系统。</s>