Extrinsic calibration for large FOV based on inverse depth parameterized bundle adjustment

To overcome the reliance on high-precision calibration plates in camera calibration, this paper proposes an extrinsic calibration method suitable for three-dimensional digital image correlation (3D-DIC) in large field of view (FOV). The method combines image feature algorithms with DIC techniques to extract matching point pairs (MPPs) from the left and right images of binocular cameras. These MPPs are then homogenized within the FOV. Next, initial values of the extrinsic parameters are solved based on epipolar constraint theory. Finally, the calibration parameters are nonlinearly optimized using the bundle adjustment method. To achieve stable and reliable numerical optimization in large FOV applications, the calibration control points (CCPs)' spatial coordinates are represented using inverse depth parameterization. In scenarios where there might be a lack of sufficient CCPs, speckle patterns are artificially introduced to supplement the scene features. However, there is a lack of reliable experimental basis on how to add CCPs within the FOV. Therefore, through simulations, the factors affecting calibration accuracy are analyzed to guide the actual calibration process. The proposed method's reliability and accuracy in large FOV 3D measurement are verified through experiments.