Stochastic map merging in noisy rescue environments

We address the problem of merging multiple noisy maps in the rescue environment. The problem is tackled by performing a stochastic search in the space of possible map transformations. The proposed technique has its roots in a motion planning algorithm we recent developed, and turns out to be a generalization of other search techniques like hill-climbing or simulated annealing. We provide the proofs of its probabilistic convergence and we provide numerical examples of its performance while merging partial maps build by our rescue robots.