Abstract
A semantic-map building method is proposed to localize a robot in the semantic-map. 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 SLAM performances can be obtained even under poor conditions in which localization cannot be achieved by classical odometry-based SLAM.
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© 2006 Springer-Verlag Berlin Heidelberg
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Jeong, S., Lim, J., Suh, H.I., Choi, BU. (2006). Vision-Based Semantic-Map Building and Localization. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892960_68
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DOI: https://doi.org/10.1007/11892960_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46535-5
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