Robust one-dimensional calibration and localisation of a distributed camera sensor network. (February 2020) - Halloran, Brendan; Premaratne, Prashan; Vial, Peter James
Highlights: We improve upon general-motion 1D calibration for multi-view networks. Normalising the projective reconstruction improves linear least squares result. A final bundle adjustment stage greatly improves calibration accuracy. ADMM and Gaussian belief propagation allows a distributed algorithm of high accuracy. Improved algorithms outperform original algorithm and checkerboard calibration. Abstract: Calibration and localisation of a camera sensor network is an essential requirement for higher-level computer vision tasks, such as mapping or tracking. Additionally, distributed algorithms are being increasingly used to create scalable networks robust to node failure. We propose a distributed calibration and localisation algorithm based on multi-view one-dimensional calibration, alternating direction method of multipliers, and Gaussian belief propagation. Our algorithm builds upon an existing calibration algorithm by improving the numerical conditioning and non-linear refinement. We adapt this to a distributed network, bringing local estimates at each camera node to global consensus. Simulation and experimental results show that our algorithm performs with high accuracy compared to other calibration techniques, in centralised and distributed networks, and is well suited for practical applications.