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Map-Building and Localization by Three-Dimensional Local Features for Ubiquitous Service Robot

  • Youngbin Park
  • Seungdo Jeong
  • Il Hong Suh
  • Byung-Uk Choi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4412)

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.

Keywords

Scale Invariant Feature Transform Stereo Vision Stereo Camera Robot Position Robot Localization 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Youngbin Park
    • 1
  • Seungdo Jeong
    • 2
  • Il Hong Suh
    • 1
  • Byung-Uk Choi
    • 1
  1. 1.College of Information and Communications, Hanyang University, 17 Haengdang-dong, Sungdong-gu, Seoul, 133-791Korea
  2. 2.Department of Electrical and Computer Engineering, Hanyang University, 17 Haengdang-dong, Sungdong-gu, Seoul, 133-791Korea

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