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This paper presents a fast lidar-inertial odometry (LIO) that is robust toaggressive motion. To achieve robust tracking in aggressive motion scenes, weexploit the continuous scanning property of lidar to adaptively divide the fullscan into multiple partial scans (named sub-frames) according to the motionintensity. And to avoid the degradation of sub-frames resulting frominsufficient constraints, we propose a robust state estimation method based ona tightly-coupled iterated error state Kalman smoother (ESKS) framework.Furthermore, we propose a robocentric voxel map (RC-Vox) to improve thesystem's efficiency. The RC-Vox allows efficient maintenance of map points andk nearest neighbor (k-NN) queries by mapping local map points into afixed-size, two-layer 3D array structure. Extensive experiments are conductedon 27 sequences from 4 public datasets and our own dataset. The results showthat our system can achieve stable tracking in aggressive motion scenes(angular velocity up to 21.8 rad/s) that cannot be handled by otherstate-of-the-art methods, while our system can achieve competitive performancewith these methods in general scenes. Furthermore, thanks to the RC-Vox, oursystem is much faster than the most efficient LIO system currently published.

FR-LIO: Fast and Robust Lidar-Inertial Odometry by Tightly-Coupled Iterated Kalman Smoother and Robocentric Voxels