3D robot mapping: combining active and non active sensors in a probabilistic framework

Authors:
F. Aznar;M. Sempere;M. Pujol;R. Rizo;R. Molina
Affiliations:
Department of Computer Science and Artificial Intelligence, University of Alicante;Department of Computer Science and Artificial Intelligence, University of Alicante;Department of Computer Science and Artificial Intelligence, University of Alicante;Department of Computer Science and Artificial Intelligence, University of Alicante;Department of Computer Science and Artificial Intelligence, University of Alicante
Venue:
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
Year:
2005

Citing 2
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Bayesian Robot Programming

Autonomous Robots
A cognitive model for autonomous agents based on bayesian programming

BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence

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Abstract

Map reconstruction and robot location are two essential problems in the field of robotics and artificial intelligence. A robot could need a model of the environment that can be incomplete and therefore the robot must work considering the uncertainty. Bayesian Units consider the uncertainty and allow the fusion of information from different sensors. In this paper a map reconstruction system in 3D based on Bayesian Units is presented. The reconstruction is carried out integrating the data obtained by a laser and by an omnivision system. In addition, to improve the quality of the reconstruction, the fusion of several Bayesian Units is defined using a competitive fusion operator. Finally, the obtained results as well as the validity of the system are shown.