共视场车载激光雷达下线标定设计与实现

doi:10.19562/j.chinasae.qcgc.2025.10.008

Abstract

Abstract:

During the mass production of intelligent driving vehicles， there are inevitable errors in the assembly of lidar. In order to ensure that the lidar can provide accurate and reliable perception information， in this paper a calibration method and workstation construction scheme for the off-line calibration of lidar in intelligent driving vehicles is proposed. A calibration workstation is constructed， which includes a vehicle parking area and a target placement area with a target having spatial characteristics. The ground plane point cloud of the reference lidar is extracted in combination with the workstation， and a planar constraint is established with the point cloud of the design reference plane. At the same time， the constraint relationship is constructed by using the spatial position between the reference lidar and the target to complete the calibration of the reference lidar. A rigid body transformation calibration model between the reference lidar and the left and right lidars is established through target detection to complete the calibration of the left and right lidars. oint simulation calibration environment is established， and simulation experiments are designed and carried out to verify the effectiveness of the algorithm. Through the comparative analysis of real vehicle tests， in terms of stability and accuracy， the calibration results of the three-axis rotation and translation are similar to those of manual calibration. Compared with the scheme in the literature， the relative error rate of the rotation is significantly reduced， with the roll， pitch， and yaw reduced by approximately 11.7%， 3%， and 0.03% respectively， and the standard deviation reduced by 0.48， 0.68， and 0.11 respectively. In terms of calibration efficiency， it is approximately 16 times higher than manual calibration and approximately 8 times higher than the scheme in the literature. The results show that the scheme proposed in this paper has obvious advantages in terms of accuracy， stability， and efficiency， providing strong support and a solid guarantee for the off-line calibration of lidar.

Key words: LiDAR, offline calibration, common field of view, intelligent driving

Weilong Fu,Jing Wang,Zhengchao Lei,Jun Zhao,Huan Xie. Design and Implementation of Offline Calibration for Vehicle-Mounted LiDAR with Common Field of View[J].Automotive Engineering, 2025, 47(10): 1923-1932.

Figures/Tables 15

References 20

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方法	场景	是否有标定物	标定结果（x/mm，y/mm，z/mm，roll/（°），pitch/（°），yaw/（°））
			车辆1		车辆2		车辆3
			标定结果	标定时间/s	标定结果	标定时间/s	标定结果	标定时间/s
手动标定	特定场景	有	（20.18，-10.25，-1.56， -2.13，2.34，-9.18）	787	（25.33，-12.56，8.22， -1.23，2.55，-5.67）	823	（-20.12，18.77，-7.43， 0.88，-2.91，7.34）	793
文献［20］	公开道路	无	（-，-，-， -1.31，2.80，-7.89）	347	（-，-，-， -2.48，2.12，-6.03）	396	（-，-，-， 1.39，-3.24，6.63）	458
本文方案	特定场景	有	（21.28，-11.14，-1.78， -2.70，1.95，-9.02）	47	（24.33，-11.56，8.36， -1.97，2.36，-5.41）	52	（-19.62，19.23，-6.63， 1.17，-3.11，7.84）	49

方法	场景	是否有标定物	标定结果（x/mm，y/mm，z/mm，roll/（°），pitch/（°），yaw/（°））
			第1次		第2次		第3次
			标定结果	标定时间/s	标定结果	标定时间/s	标定结果	标定时间/s
手动标定	特定场景	有	（20.18，-10.25，-1.56， -2.13，2.34，-9.18）	787	（20.23，-10.09，-1.45， -2.07，2.44，-9.30）	477	（20.20，-10.16，-1.60， -2.17，2.29，-9.25）	342
文献［20］	公开道路	无	（-，-，-， -2.31，1.10，-7.89）	339	（-，-，-， -2.47，2.21，-9.13）	416	（-，-，-， -1.39，2.55，-7.78）	428
本文方案	特定场景	有	（21.47，-9.53，-1.68， -2.10，2.45，-8.02）	46	（21.15，-9.23，-1.54， -2.21，2.38，-9.23）	48	（21.12，-9.38，-1.47， -2.01，2.32，-8.98）	53

Design and Implementation of Offline Calibration for Vehicle-Mounted LiDAR with Common Field of View

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