Visual-based Trajectory Optimization for Autonomous Underwater Vehicles

This paper presents a visual-based trajectory opti-mization framework designed to enhance the navigation capabil-ities of Autonomous Underwater Vehicles (AUVs) utilized for underwater monitoring and inspection operations. The framework leverages the optical feedback from a monocular camera to detect loop closures and refine the estimation of the robot trajectory through a pose graph optimization procedure. The solution builds upon a state-of-the-art appearance-based loop closure detection method, which has been modified to improve its robustness and efficiency in underwater scenarios. The modifications include the use of contrast-limited adaptive histogram equalization to enhance image quality, the introduction of a keyframe selection procedure to reduce computational costs, and the implementation of a motion estimation stage for computing relative rigid transformations between loop closures. The optimization strategy was tested of Fline using a dataset of real underwater images acquired during structure inspection activities at sea. In par-ticular, the implemented loop closure detection and pose graph optimization functionalities were integrated with a visual-based dead-reckoning navigation approach that utilizes a monocular visual odometry algorithm providing information concerning the linear velocity of the AUV. The experimental results demonstrate the effectiveness of the proposed system in improving navigation performance within complex and unstructured underwater environments. The trajectory optimization process significantly reduces the drift of the monocular visual-based dead-reckoning estimation, thereby enhancing the accuracy of state estimation and the 2eo-referencin2 of collected data.