Abstract
ABSTRACTReal-time seabed tracking applications play an important role in underwater systems. A lot of them use computer vision for servoing, positioning, navigation, odometry and simultaneous localisation and mapping. They are mostly based on local image features, therefore feature detection, description and matching are crucial for their efficient operations. The aim of this study was to investigate the most popular feature detection and description algorithms such as SIFT, SURF, FAST, STAR, HARRIS, ORB, BRISK and FREAK. Additionally, the image correction technique was presented and image enhancement methods were analysed in order to increase efficiency of image features matching. The matching algorithm was based on the homography matrix and random sample consensus technique. Our results indicate that the combination of the histogram equalisation technique and ORB detector and descriptor enables real-time seabed tracking with sufficient efficiency.
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