Camera-Sonar Combination for Improved Underwater Localization and Mapping
Alexandre Cardaillac, Martin Ludvigsen
Abstract
Taking advantage of the complimentary properties of sonars and cameras can improve underwater visual odometry and point cloud generation. However, this task remains difficult as the image generation concepts are different, giving challenges to direct acoustic and optic feature matching. Solving this problem can improve applications such as underwater navigation and mapping. A camera-sonar combination is proposed for real time scale estimation using underwater monocular image features combined with a multibeam forward looking sonar. The detected features from a monocular SLAM framework are matched with the acoustic features based on the relative distances in instrument reference frame calculated using the two data streams, and used to estimate a depth ratio. The ratio is optimised over a large sample set to ensure scale stability. The sensor combination enables real time scale estimation of the trajectory and the mapped environment, which is a requirement for autonomous systems. The proposed approach is experimentally demonstrated for two underwater environments and scenarios, a subsea module mapping and a ship hull inspection. The results demonstrate the efficiency and applicability of the proposed solution. In addition to correctly restoring the scale, it significantly improves the localization and outperforms the tested dead reckoning and visual inertial SLAM methods.