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Vision-Based Semantic-Map Building and Localization

  • Seungdo Jeong
  • Jounghoon Lim
  • Hong Il Suh
  • Byung-Uk Choi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4251)

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.

Keywords

Stereo Camera Robot Localization Camera Coordinate System Symbolic Object Harris Corner Detector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Seungdo Jeong
    • 1
  • Jounghoon Lim
    • 2
  • Hong Il Suh
    • 2
  • Byung-Uk Choi
    • 2
  1. 1.Department of Electrical and Computer EngineeringHanyang UniversitySeoulKorea
  2. 2.School of Information and CommunicationsHanyang University 

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