Depth-Consistent Monocular Visual Trajectory Estimation for AUVs

Visual trajectory estimation can endow autonomous underwater vehicles (AUVs) with environmental perception capabilities and has broad application prospects in the fields such as oceanographic surveys, underwater construction, and marine ranching. However, due to featureless images and depth ambiguity issues caused by underwater multiple mediums environments, monocular visual trajectory estimation in complex underwater environments remains a challenging problem. In this paper, we propose a monocular visual trajectory estimation method for AUVs, which can address the challenges of featureless and depth ambiguity by leveraging deep image representations and multi-view geometry. Specifically, we design a bidirectional optical flow consistency scheme that selects sparse correspondences from monocular dense predictions to deal with the featureless images, and then achieve AUV trajectory estimation through epipolar constraints. Furthermore, we propose an iterative depth-consistent method, which solves the problem of depth ambiguity by aligning geometrically triangulated depths to the scale-consistent deep depths. We also develop a low-cost, agile, and portable AUV picking system with real-time trajectory estimation capabilities, and carry out extensive experiments in the Yellow Sea to test its performance. The experimental results demonstrate the effectiveness of the proposed method.