Vision based obstacle avoidance and motion tracking for autonomous behaviors in underwater vehicles

Performing reliable underwater localization and maneuvering of Remotely Operated underwater Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs) near nature protection areas, historical sites or other man-made structures is a difficult task. Traditionally, different sensing techniques are exploited with sonar being the most often used to extract depth information and to avoid obstacles. However, little has been published on complete control systems that utilize robotic vision for such underwater applications. This paper provides a proof of concept regarding a series of experiments investigating the use of stereo vision for underwater obstacle avoidance and position estimation. The test platform has been a ROV equipped with two industrial cameras and external light sources. Methods for underwater calibration, disparity map and 3D point cloud processing have been used, to obtain more reliable information about obstacles in front of the ROV. Results from laboratory research work and from field experiments demonstrate that underwater obstacle avoidance with stereo cameras is possible and can increase the autonomous capabilities of ROVs by providing appropriate information for navigation, path planning, safer missions and environment awareness.