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Abstract: This paper presents both a new sonar based place learning method and a global localisation algorithm based on #nite 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 adquired at any position of the environment and they do not depend on the robot orientation. Localisation relies on segmenting... (Update)
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BibTeX entry: (Update)
@misc{ bandera-autonomous,
author = "Antonio Bandera and Cristina Urdiales and Francisco Sandoval",
title = "Autonomous Global Localisation Using Markov Chains and Optimised Sonar
Landmarks",
url = "citeseer.ist.psu.edu/401055.html" }
Citations (may not include all citations):137 Directed sonar sensing for mobile robot navigation - Leonard, Durrant-Whyte - 1992
110 Probability and Statistics (context) - DeGroot - 1986
73 A comparison of position estimation techniques using occupan.. - Schiele, Crowley - 1994
42 Spatial learning for navigation in dynamic environments - Yamauchi, Beer - 1996
23 A k-means clustering algorithm (context) - Hartigan - 1979
16 Optimal estimation of position and heading for mobile robots.. - Kleeman - 1992
4 The mobile robot RHINO (context) - Thrun - 1994
1 Mobile robot localisation via classi#cation of multisensor m.. (context) - Courtney, Jain - 1994
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