Autonomous global localisation using Markov chains and optimised sonar landmarks

This paper presents both a new sonar based place learning method and a global localisation algorithm based on finite Markov chains for autonomous robots. Landmarks are calculated by projecting the Fourier transform of the depth map obtained from a ring of equally spaced sonar sensors onto a bidimensional base of its vectorial subspace. Resulting landmarks can be acquired at any position of the environment and they do not depend on the robot orientation. Localisation relies on segmenting available landmarks into homogeneous regions and calculating transition matrices between them. Then, the position of the robot is estimated according to finite Markovian chains, but the probability of occupying a given region is modulated by the most recently acquired landmark. The method is valid for complex unstructured environments and it has experimentally proven to be fast, reliable and computationally cheap