Abstract
Underwater robot perception is crucial in scientific subsea exploration and
commercial operations. The key challenges include non-uniform lighting and poor
visibility in turbid environments. High-frequency forward-look sonar cameras
address these issues, by providing high-resolution imagery at maximum range of
tens of meters, despite complexities posed by high degree of speckle noise, and
lack of color and texture. In particular, robust feature detection is an
essential initial step for automated object recognition, localization,
navigation, and 3-D mapping. Various local feature detectors developed for RGB
images are not well-suited for sonar data. To assess their performances, we
evaluate a number of feature detectors using real sonar images from five
different sonar devices. Performance metrics such as detection accuracy, false
positives, and robustness to variations in target characteristics and sonar
devices are applied to analyze the experimental results. The study would
provide a deeper insight into the bottlenecks of feature detection for sonar
data, and developing more effective methods