Tightly Coupled 3D Lidar Inertial Odometry and Mapping

Haoyang Ye, Yuying Chen and Ming Liu from RAM-LAB.

The Hong Kong University of Science and Technology

Link to [arXiv] pre-print, [supplementary material].


Ego-motion estimation is a fundamental requirement for most mobile robotic applications. By sensor fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable estimations. We introduce a tightly coupled lidar-IMU fusion method in this paper. By jointly minimizing the cost derived from lidar and IMU measurements, the lidar-IMU odometry (LIO) can perform well with acceptable drift after long-term experiment, even in challenging cases where the lidar measurements can be degraded. Besides, to obtain more reliable estimations of the lidar poses, a rotation-constrained refinement algorithm (LIO-mapping) is proposed to further align the lidar poses with the global map. The experiment results demonstrate that the proposed method can estimate the poses of the sensor pair at the IMU update rate with high precision, even under fast motion conditions or with insufficient features.


Link to YouTube.


Source code is available at GitHub.

If you find LIO-mapping helpful for your academic research, please kindly cite our related paper (arXiv, bib).


HKUST Lidar-IMU Dataset.