Vision-Based semantic-map building and localization

Authors:
Seungdo Jeong;Jounghoon Lim;Hong Il Suh;Byung-Uk Choi
Affiliations:
Department of Electrical and Computer Engineering, Hanyang University, Seoul, Korea;School of Information and Communications, Hanyang University;School of Information and Communications, Hanyang University;School of Information and Communications, Hanyang University
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2006

Citing 3
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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.