Stochastic map merging in rescue environments

Abstract

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, i.e. rotations and translations. The proposed technique, which performs a time variant Gaussian random walk, turns out to be a generalization of other search techniques like hill-climbing or simulated annealing. Numerical examples of its performance while merging partial maps built by our rescue robots are provided. © Springer-Verlag Berlin Heidelberg 2005.