| Summary: | 碩士 === 國立中山大學 === 海下科技研究所 === 106 === Baseline acoustic positioning systems can obtain the absolute positions of underwater targets, but its update rate is slow and calibration is often complicated and tedious. In addition, temporal and spatial variations of water column sound speed profile significantly affect positioning accuracy of the baseline system. Doppler velocity log (DVL) and inertial navigation system (INS) have higher update rate than the baseline systems. But DVL and INS are dead reckoning systems which provide relative positioning only and suffer from time-dependent drift error. Considering that video cameras are standard equipment on almost underwater vehicles, it is easy to collect seafloor videos for a vehicle while conducting seafloor survey. With the advantages of high resolution and high frame rate, the seafloor video has great potential for accurately positioning an underwater vehicle based on image feature detection and matching. Therefore, in this study, the feature-based image matching algorithm for positioning an underwater vehicle is proposed. The seafloor videos collected off southwestern Taiwan at a depth of about 1000 meters by the deep-towed vehicle FITS (Fiber-optical Instrumentation Towed System) are used for evaluating the performance of the proposed algorithm. As the success of image feature detection and matching depends on various factors such as seafloor richness and roughness, descriptor of feature detection algorithms, threshold for Hessian keypoint detector, overlapping area of two images, and illumination, considerable effort in this study was made to assess the effects of various factors on the performance of the proposed algorithm. Performance evaluations were conducted by comparing the estimates of the proposed algorithm to the measurements of the DVL onboard the FITS.
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