Robust Multiview Extrinsic Calibration With Limited Co-Observations via Sparse Graph Reprojection and Target-Driven Loop Closure

Extrinsic calibration of multiview camera systems typically uses limited local simultaneous co-observations to achieve global optimization. Conventional approaches face two key shortcomings: when local simultaneous co-observations involve multiple viewpoints, the inability to exploit co-observation relationships leads to modeling gaps and decreased optimization stability; when explicit loop-closure constraints are insufficient, the effectiveness of global optimization is limited, resulting in reduced overall calibration accuracy. To address these issues, this article presents a sparse graph reprojection-based extrinsic calibration method for multicamera systems. The method first performs local optimization based on the hand–eye calibration principle, followed by global optimization by integrating a pose graph with bundle adjustment (BA). By uniformly modeling the sparse simultaneous co-observation relationships in local measurements and concurrently establishing reprojection constraints within the sparse pose graph, overall robustness increases. Additionally, a target pair reuse mechanism is introduced to explicitly compensate for missing loop-closure constraints in chain-like configurations, ensuring globally consistent solutions through graph optimization. Simulation experiments evaluated the calibration accuracy and stability of the proposed method for all cameras, while real-world tests assessed its calibration performance for the camera without loop closures, demonstrating its feasibility and effectiveness.