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

Abstract

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