引用本文: 谢婧婷, 蔺小虎, 王甫红, 石欣, 晏凌云. 一种点线面约束的激光雷达和相机标定方法[J]. 武汉大学学报 ( 信息科学版), 2021, 46(12): 1916-1923.
XIE Jingting, LIN Xiaohu, WANG Fuhong, SHI Xin, YAN Lingyun. Extrinsic Calibration Method for LiDAR and Camera with Joint Point-Line-Plane Constraints[J]. Geomatics and Information Science of Wuhan University, 2021, 46(12): 1916-1923.
 Citation: XIE Jingting, LIN Xiaohu, WANG Fuhong, SHI Xin, YAN Lingyun. Extrinsic Calibration Method for LiDAR and Camera with Joint Point-Line-Plane Constraints[J]. Geomatics and Information Science of Wuhan University, 2021, 46(12): 1916-1923.

## Extrinsic Calibration Method for LiDAR and Camera with Joint Point-Line-Plane Constraints

• 摘要: 精确稳定的外参数标定是相机和激光雷达(light detection and ranging, LiDAR)融合感知定位的基础，针对Dhall标定方法在约束精度、稳定性和收敛性等方面存在的问题，提出了一种点线面联合约束的LiDAR和相机标定方法。首先，加入线-线约束和面-面约束并用Kabsch方法求得闭式解，减小了单一角点约束中的噪声影响，提高了标定结果的精度和稳定性；然后，利用多帧点云叠加拟合直线，并对拟合角点求均值，减小了点云噪声的影响，提高了标定方法的收敛速度和稳定性。该方法能在20帧内收敛，且角点的重投影误差小于2 cm，实验结果表明, 该方法比原方法具有更快的收敛速度和更高的精度。

Abstract:
Objectives  Accurate and stable extrinsic parameter calibrations are the basis of fusing perception and positioning of cameras and light detection and ranging (LiDAR). To address the problems of the Dhall calibration method in constraint accuracy, stability, and convergence, this paper proposed a LiDAR and camera calibration method with joint point-line-plane constraints.
Methods  First, line-line and plane-plane constraints were added, and the Kabsch method was used to obtain a closed-form solution, which reduced the influence of noise in the single corner constraint and improved the accuracy and stability of the calibration results. Then, multi-frame point clouds were superimposed to fit lines, and the fitting corners were averaged, which further reduced the influence of the point cloud noise, thus improving the convergence speed and stability of the calibration method.
Results  Experimental results show that the proposed method can converge within 20 frames, and the reprojection error of the corner points was less than 2 cm.
Conclusions  The proposed method has faster convergence speed and higher accuracy than the original method.

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