SR-LIO: LiDAR-Inertial Odometry with Sweep Reconstruction

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This paper proposes a novel LiDAR-Inertial odometry (LIO), named SR-LIO, based on an iterated extended Kalman filter (iEKF) framework. We adapt the sweep reconstruction method, which segments and reconstructs raw input sweeps from spinning LiDAR to obtain reconstructed sweeps with higher frequency. We found that such method can effectively reduce the time interval for each iterated state update, improving the state estimation accuracy and enabling the usage of iEKF framework for fusing high-frequency IMU and low-frequency LiDAR. To prevent inaccurate trajectory caused by multiple distortion correction to a particular point, we further propose to perform distortion correction for each segment. Experimental results on four public datasets demonstrate that our SR-LIO outperforms all existing state-of-the-art methods on accuracy, and reducing the time interval of iterated state update via the proposed sweep reconstruction can improve the accuracy and frequency of estimated states. The source code of SR-LIO is publicly available for the development of the community.

Zikang Yuan, Fengtian Lang, Tianle Xu, Xin Yang · 2022

Related benchmarks

Task	Dataset	Result
LiDAR-Inertial Odometry Processing Time Estimation	NCLT	Processing Time (ms)27.2	42
LiDAR-Inertial Odometry	NCLT nclt_1 2012-01-08	ATE RMSE (m)1.34	7
LiDAR-Inertial Odometry	NCLT nclt_6 2012-05-26	ATE RMSE (m)2.1	7
LiDAR-Inertial Odometry	UTBM utbm_3 2019-04-18	ATE RMSE (m)8.42	7
LiDAR-Inertial Odometry	ULHK ulhk_1 2019-01-17	ATE RMSE (m)0.93	7
LiDAR-Inertial Odometry	NCLT 3 2012-02-04	ATE RMSE (m)2.37	7
LiDAR-Inertial Odometry	NCLT 9 2012-08-20	ATE RMSE (m)2.11	7
LiDAR-Inertial Odometry	NCLT 10 2012-09-28	ATE RMSE (m)1.67	7
LiDAR-Inertial Odometry	NCLT nclt_11 2012-12-01	ATE RMSE (m)1.61	7
LiDAR-Inertial Odometry	UTBM utbm_1 2018-07-19	ATE RMSE (m)7.7	7