A Novel Multifeature Based On-Site Calibration Method for LiDAR-IMU System

• Published in: IEEE transactions on industrial electronics (1982) Vol. 67; no. 11; pp. 9851 - 9861
• Main Authors: Liu, Wanli, Li, Zhixiong, Malekian, Reza, Sotelo, Miguel Angel, Ma, Zhenjun, Li, Weihua
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Calibration; Coordinate transformations; Cylinder liners; Feature extraction; Full information maximum-likelihood estimation; inertial measurement unit (IMU); Inertial platforms; Labor; Laser radar; Lidar; light detection and ranging (LiDAR); Mathematical model; Maximum likelihood estimation; multifeature; on-site calibration; Onsite; Optimization; Parameter estimation; Performance enhancement; Three-dimensional displays; Two dimensional displays
• ISSN: 0278-0046; 1557-9948; 1557-9948
• DOI: 10.1109/TIE.2019.2956368

Calibration is an essential prerequisite for the combined application of light detection and ranging (LiDAR) and inertial measurement unit (IMU). However, current LiDAR-IMU calibration usually relies on particular artificial targets or facilities and the intensive labor greatly limits the calibration flexibility. For these reasons, this article presents a novel multifeature based on-site calibration method for LiDAR-IMU system without any artificial targets or specific facilities. This new on-site calibration combines the point/sphere, line/cylinder, and plane features from LiDAR scanned data to reduce the labor intensity. The main contribution is that a new method is developed for LiDAR extrinsic parameters on-site calibration and this method could incorporate two or more calibration models to generate more accurate calibration results. First of all, the calibration of LiDAR extrinsic parameters is performed through estimating the geometric features and solving the multifeature geometric constrained optimization problem. Then, the relationships between LiDAR and IMU intrinsic calibration parameters are determined by the coordinate transformation. Lastly, the full information maximum likelihood estimation (FIMLE) method is applied to solve the optimization of the IMU intrinsic parameters calibration. A series of experiments are conducted to evaluate the proposed method. The analysis results demonstrate that the proposed on-site calibration method can improve the performance of the LiDAR-IMU.