Open AccessHighly accurate 3D reconstruction based on a precise and robust binocular camera calibration method
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.2019.1525
Subject(s) - computer science , singular value decomposition , artificial intelligence , camera resectioning , computer vision , calibration , essential matrix , fundamental matrix (linear differential equation) , position (finance) , transformation matrix , reprojection error , matrix (chemical analysis) , singular value , algorithm , range (aeronautics) , mathematics , image (mathematics) , symmetric matrix , state transition matrix , statistics , mathematical analysis , eigenvalues and eigenvectors , physics , kinematics , finance , quantum mechanics , classical mechanics , composite material , economics , materials science
The precision of the camera calibration is one of the key factors that affect attitude measurement accuracy in many computer vision tasks. This study proposes a new calibration approach for binocular cameras. Firstly, based on singular value decomposition, the best transformation matrix to the essential matrix is approximated as the initial guess, which is solved in using the Frobenius norm. Secondly, the initial guess is refined through maximum likelihood estimation. A new calculating expression is derived for computing the relative position matrix of the binocular cameras. The Levenberg–Marquardt algorithm is then implemented to refine the initial guess. Large sets of synthesised and real point correspondences were tested to demonstrate the validity of the proposed method. Extensive experiments demonstrated that the proposed method outperforms the state‐of‐the‐art methods. The error rate of the proposed method was 0.5% for the length test and about 1% for the angle test at a range of 1 m. This method can advance three‐dimensional (3D) computer vision one additional step from laboratory environments to real‐world use.