Camber and toe-in are important alignment parameters of vehicle wheels, which determine the operation stability and safety during vehicle driving. An automatic and accurate measurement method is proposed for camber and toe-in alignment without vision system calibration. Different from the previous target-based vision measurement (TVM) based on the maximum likelihood estimation (MLE), the RVM-DBSC model is proposed to achieve the automatic measurement of camber and toe-in alignment. Furthermore, the camber and toe-in measurement model is established by the maximum a posteriori (MAP) and the alternating direction method of multipliers (ADMM), rather than the traditional MLE. The contrast experiments are performed by the verification instrument, which indicates that the proposed method achieves effectiveness and high accuracy of camber and toe-in measurement.

We present a reliable calibration method using the constraint of 2D projective lines and 3D world points to elaborate the accuracy of the camera calibration. Based on the relationship between the 3D points and the projective plane, the constraint equations of the transformation matrix are generated from the 3D points and 2D projective lines. The transformation matrix is solved by the singular value decomposition. The proposed method is compared with the point-based calibration to verify the measurement validity. The mean values of the root-mean-square errors using the proposed method are 7.69×10−4, 6.98×10−4, 2.29×10−4, and 1.09×10−3 while the ones of the original method are 8.10×10−4, 1.29×10−2, 2.58×10−2, and 8.12×10−3. Moreover, the average logarithmic errors of the calibration method are evaluated and compared with the former method in different Gaussian noises and projective lines. The variances of the average errors using the proposed method are 1.70×10−5, 1.39×10−4, 1.13×10−4, and 4.06×10−4, which indicates the stability and accuracy of the method

Wheel alignment plays an important role in industrial inspection for vehicle safety. A laser-line-family-orientated method is proposed to achieve the global registration for the shoulder-to-shoulder (SS) and face-to-back (FB) camera group of the alignment measurement system, and to flexibly and accurately solve the on-site convenient registration problem in the large-scale measurement. Moreover, the global & absolute references determination model is built to achieve the 1D-target all-wheel alignment measurement, in the light of the laser-line-family-orientated model. Furthermore, the global registration and 1D-target all-wheel alignment method are experimentally verified on the basis of the global & absolute & physical benchmark by the global & absolute measurement error. The measurement repeatability error of the 1D-target all-wheel alignment of vehicle is also experimentally performed, due to the unknown real values of vehicle-wheel alignment parameters and the virtual axes defining the parameters. The experiments indicate the accuracy, the availability and the practicability of the wheel alignment method.

Ellipse detection is of great significance in the fields of image processing and computer vision. Accurate, stable and direct ellipse detection in real-world images has always been a key issue. Therefore, an ellipse detection method is proposed on the basis of the constructed three-intersection-chord-invariant. First, in the inflexion point detection, the PCA minimum bounding box considering the distribution characteristics of edge points is studied to achieve the more refined line segment screening. Second, a multi-scale inflexion point detection method is proposed to effectively avoid over-segmentation of small arc segments, providing assurance for more reasonable and reliable arc segment combinations. Then, the 20 precisely classified arc segment combinations are refined into 4 combinations. A number of non-homologous arc segment combinations can be quickly removed to reduce incorrect combinations by the constructed midpoint distance constraint and quadrant constraint. Moreover, in order to accurately reflect the strict arc segment combination constraints of geometric features of ellipses, a three-intersection-chord-invariant model of ellipses is established with strong constraint of relative distances among five constraint points, by which a more robust initial ellipse set of homologous arc segment combinations is further obtained. Finally, ellipse validation and clustering are performed on the initial set of ellipses to obtain the high-precision ellipses. The algorithm accuracy of the ellipse detection method is experimentally validated on 6 publicly available datasets and 2 established wheel rim datasets.