Abstract
A mobile robot system usually has multiple sensors of various types. In a dynamic and unstructured environment, information processing and decision making using the data acquired by these sensors pose a signi.cant challenge. Kalman .lter- based methods have been developed for fusing data from various sensors for mobile robots. However, the Kalman .lter methods are computationally intensive. Markov and Monte Carlo methods are even less e.cient than Kalman .lter methods. In this paper, we present an alternative method based on principal component analysis (PCA) for processing the data acquired with multiple sensors.
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© 2005 Springer-Verlag Berlin Heidelberg
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Hou, ZG. (2005). Principal Component Analysis (PCA) for Data Fusion and Navigation of Mobile Robots. In: Kantor, P., et al. Intelligence and Security Informatics. ISI 2005. Lecture Notes in Computer Science, vol 3495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427995_72
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DOI: https://doi.org/10.1007/11427995_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25999-2
Online ISBN: 978-3-540-32063-0
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