The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
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Improvement of Localization Accuracy with COAG Features and Candidate Selection based on Shape of Sensor Data

COAG 특징과 센서 데이터 형상 기반의 후보지 선정을 이용한 위치추정 정확도 향상

  • Received : 2014.02.13
  • Accepted : 2014.04.23
  • Published : 2014.05.28
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Abstract

Localization is one of the essential tasks necessary to achieve autonomous navigation of a mobile robot. One such localization technique, Monte Carlo Localization (MCL) is often applied to a digital surface model. However, there are differences between range data from laser rangefinders and the data predicted using a map. In this study, commonly observed from air and ground (COAG) features and candidate selection based on the shape of sensor data are incorporated to improve localization accuracy. COAG features are used to classify points consistent with both the range sensor data and the predicted data, and the sample candidates are classified according to their shape constructed from sensor data. Comparisons of local tracking and global localization accuracy show the improved accuracy of the proposed method over conventional methods.

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References

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