Line maps in cluttered environments

Authors:
Leonardo Romero;Carlos Lara
Affiliations:
Division de Estudios de Posgrado, Facultad de Ingenieria Electrica, Universidad Michoacana, Morelia, Mexico;Division de Estudios de Posgrado, Facultad de Ingenieria Electrica, Universidad Michoacana, Morelia, Mexico
Venue:
MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
Year:
2010

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Abstract

This paper uses the smoothing and mapping framework to solve the SLAM problem in indoor environments; focusing on how some key issues such as feature extraction and data association can be handled by applying probabilistic techniques. For feature extraction, an odds ratio approach to find multiple lines from laser scans is proposed, this criterion allows to decide which model must be merged and to output the best number of models. In addition, to solve the data association problem a method based on the segments of each line is proposed. Experimental results show that high quality indoor maps can be obtained from noisy data.