AUV SLAM constraint formation using side scan sonar

Abstract: Autonomous underwater vehicle (AUV) navigation has been a challenging problem for a long time. Navigation is challenging due to the drift present in underwater environments and the lack of precise localisation systems such as GPS. Therefore, the uncertainty of the vehicle’s pose grows with the mission’s duration. This research investigates methods to form constraints on the vehicle’s pose throughout typical surveys. Current underwater navigation relies on acoustic sensors. Side Scan Sonar (SSS) is cheaper than Multibeam echosounder (MBES) but can generate 2D intensity images of wide sections of the seafloor instead of 3D representations. The methodology consists in extracting information from pairs of side-scan sonar images representing overlapping portions of the seafloor and computing the sensor pose transformation between the two reference frames of the image to generate constraints on the pose. The chosen approach relies on optimisation methods within a Simultaneous Localisation and Mapping (SLAM) framework to directly correct the trajectory and provide the best estimate of the AUV pose. I tested the optimisation system on simulated data to evaluate the proof of concept. Lastly, as an experiment trial, I tested the implementation on an annotated dataset containing overlapping side-scan sonar images provided by SMaRC. The simulated results indicate that AUV pose error can be reduced by optimisation, even with various noise levels in the measurements.