Stereo calibration for high-precision wheel tread 3D detection using phase-shifting profilometry

Stereo active structured light technology has attracted wide attention in the field of 3D measurement due to its high precision and high spatial resolution. However, when measuring large objects, a single 3D scan often cannot obtain complete surface information, and multi-angle sampling is required to reconstruct the complete 3D shape. Due to the nonparallel optical axes of the cameras, the traditional binocular calibration method has large errors. To address this limitation, this paper proposes a stereo calibration method based on singular value decomposition (SVD). The proposed method uses the Harris corner detection algorithm to identify the same point between system 1 and system 2. Through stereo matching, the 3D point cloud of the checkerboard corner points is obtained. Since the point clouds of the two systems correspond to the same point in the physical world, the singular value decomposition method calculates the external parameters (R, T) and aligns the multi-angle point cloud data into a unified coordinate system. Experimental results show that in wheel tread inspection, the proposed calibration method is superior to the traditional binocular calibration technology and significantly improves the measurement accuracy.