Autonomous Calibration Algorithms for Networks of Cameras

We deal with the important applicative problem of distributed cameras calibration. We model a network of N cameras as an undirected graph in which communicating cameras can measure their relative orientation in a noisy way. These measures can be used in order to minimize a suitable cost function. The shape of this cost function depends on a vector of integers K. We propose two algorithms which in a distributive way estimate such K, comparing advantages and disadvantages of both. Simulations are run on a grid network to prove effectiveness of the algorithms.