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http://dx.doi.org/10.7746/jkros.2014.9.2.117

Improvement of Localization Accuracy with COAG Features and Candidate Selection based on Shape of Sensor Data  

Kim, Dong-Il (Mechanical Engineering, Korea University)
Song, Jae-Bok (Mechanical Engineering, Korea University)
Choi, Ji-Hoon (UGV Technology Directorate, Agency for Defense Development)
Publication Information
The Journal of Korea Robotics Society / v.9, no.2, 2014 , pp. 117-123 More about this Journal
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.
Keywords
Monte Carlo Localization; Particle Filter; Localization; COAG Features;
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Times Cited By KSCI : 2  (Citation Analysis)
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