Open AccessNon‐linear calibration optimisation based on the Levenberg–Marquardt algorithm
Author(s) -
Hu Guoliang,
Zhou Zuofeng,
Cao Jianzhong,
Huang Huimin
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.1489
Subject(s) - reprojection error , point cloud , artificial intelligence , computer science , range (aeronautics) , calibration , algorithm , radar , feature (linguistics) , computer vision , point (geometry) , mathematics , image (mathematics) , philosophy , telecommunications , linguistics , statistics , materials science , geometry , composite material
An outstanding calibration algorithm is the most important factor that affects the precision of attitude measurement. This study proposes a non‐linear optimisation algorithm to refine the solutions of the initial guess obtained using the Zhang's technique, the Bouget's technique, or the Hartley's algorithm. Large sets of point correspondences were adopted to test the validity of the proposed method. Extensive practical experiments demonstrated that the proposed method can significantly improve the accuracy of calibration and ultimately obtains higher measurement precision. The error of the reprojection in the proposed method was <0.13 px. At a range of 1 m, the error rate was 0.5% for the length test and about 3% for the angle test. This study proposes a new method to calibrate the relationship between laser radar and the camera. Binocular vision was used to reconstruct the point cloud of the non‐cooperative target. At the same time, data was also obtained using laser radar. Finally, the two groups of systems were fused. Accurate and dense three‐dimensional information of the target was obtained. It could not only obtain the dense pose information of the target surface but also the texture and colour feature information of the target surface.