Autonomous surface vehicle 3D seafloor reconstruction from monocular images and sonar data

Traditionally seafloor surveys have been conducted with research vessels, divers or with an autonomous underwater vehicle (AUV) and are time consuming, expensive and high risk. In this paper we present an approach to merge sonar and monocular images to perform large scale mapping of shallow areas from an autonomous surface vessel (ASV), reducing the mission time, cost and risk. Our method uses multibeam sonar data to generate a mesh of the seafloor. Optical images are then blended and projected onto the mesh after a color correction process which increases contrast and overall image quality. In applicable scenarios, ASVs offer an alternative approach to AUVs for autonomous acoustic and optical site mapping. ASVs are typically less expensive than AUVs and often offer easier deployment and recovery logistics. Also, the mechanical requirements are less demanding because they do not have to withstand increased atmospheric water pressure at depth.