ABSTRACT Accurate calibration of multi‐camera systems is essential for the deployment of vision‐based mobile robots and autonomous vehicles. Conventional checkerboard‐based calibration methods typically require full field‐of‐view overlap of the calibration pattern across all cameras—a condition often difficult to satisfy in complex multi‐camera configurations. To address this limitation, we propose a multi‐camera calibration method based on joint optimization of adjacent camera nodes. Our approach establishes local overlapping constraints between neighboring cameras and estimates the intrinsic parameters of individual cameras along with the extrinsic parameters between adjacent cameras in a staged manner. These extrinsic parameters are then recursively transformed via intermediate nodes to achieve globally consistent parameter alignment within a unified coordinate system. Furthermore, by constructing a camera topology graph and incorporating a confidence evaluation function, our method quantitatively assesses the quality of calibration contributions from each camera. This enables dynamic selection of the optimal extrinsic parameter propagation path. The introduction of soft global consistency regularization and a covariance‐aware path selection mechanism effectively mitigates cumulative errors arising from chain propagation. The proposed method successfully addresses the challenges of calibrating multi‐camera systems with non‐overlapping fields of view and uneven calibration quality. Experimental results demonstrate that our algorithm achieves high calibration accuracy.
Li et al. (Thu,) studied this question.