Robust one-dimensional calibration and localisation of a distributed camera sensor network
作者：
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.
摘要
•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.
论文关键词：Multi-view calibration,Localisation,Distributed algorithms,Gaussian belief propagation,ADMM
论文评审过程：Received 10 December 2018, Revised 7 September 2019, Accepted 13 September 2019, Available online 13 September 2019, Version of Record 25 September 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.107058
