Abstract
In this work, we propose a semantic-map building method and localization method for ubiquitous service robot. Our semantic-map is organized by using SIFT feature-based object representation. In addition to semantic map, a vision-based relative localization is employed as a process model of extended Kalman filters, where optical flows and Levenberg-Marquardt least square minimization are incorporated to predict relative robot locations. Thus, robust map-building performances can be obtained even under poor conditions in which localization cannot be achieved by classical odometry-based map-building. To localize robot position and solve kidnap problem, we also propose simple, but fast localization method with a relatively high accuracy by incorporating our semantic-map.
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© 2007 Springer Berlin Heidelberg
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Park, Y., Jeong, S., Suh, I.H., Choi, BU. (2007). Map-Building and Localization by Three-Dimensional Local Features for Ubiquitous Service Robot. In: Stajano, F., Kim, H.J., Chae, JS., Kim, SD. (eds) Ubiquitous Convergence Technology. ICUCT 2006. Lecture Notes in Computer Science, vol 4412. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71789-8_8
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DOI: https://doi.org/10.1007/978-3-540-71789-8_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71788-1
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