AUV navigation using cues in the sand ripples

Subsea navigation by autonomous underwater vehicles (AUVs) is a demanding task that involves the integration of inertial sensors, gyrocompasses, Doppler velocity loggers, and reference from acoustic beacons. In this paper, we propose to augment this information by providing an external measurement of heading change. We rely on the direction of sand ripples, which are abundant on the seabed near the shore and whose direction is, locally, constant. Thus, any apparent change in their directivity, as detected by the AUV, would reflect as a change in the vehicle’s heading. Considering this, we developed a mechanism that detects regions of interest (ROIs) containing sand ripples within a synthetic aperture sonar (SAS) image, segments the ROI into highlight and shadow, and evaluates the angle difference between ROIs within two consecutive SAS images. For detection of sand ripples and estimation of angle difference, we employ two deep neural networks, while for segmentation we formulate a fuzzy-logic clustering. Taking advantage of a transfer learning approach, we trained the deep networks on simulated SAS images and on a large database of 2088 real SAS images, which we share for reproducibility. Results from real SAS images from three different sites show a good trade-off between precision and recall for sand-ripple detection, and an error of a few degrees in the heading change estimation, which well exceeds a geometrical-based benchmark. We also show performance from a real-time experiment for which we implemented our method on an AUV and estimated its heading change on-the-fly.