Optical Coastal GNSS-Denied Navigation for Reduction in AUV Underwater Navigation Error

The paper addresses the problem of maritime navigation in coastal areas without access to satellite positioning data. The proposed approach relies on visual information from a monocular camera supported by map-derived coastal information. Although the operational concept assumes the use of publicly available cartographic data, the method's preparation and evaluation require additional processing steps, including high-resolution aerial imagery, GIS-based extraction of coastal features, semantic segmentation, and trained convolutional neural network models. The problem discussed in the paper concerns, for example, Autonomous Underwater Vehicles that seek to reduce underwater dead-reckoning navigation error by surfacing and using information about what is visible around them, in a way similar to how a human would. To solve the above problem, a system was proposed that compares the camera's representation of the observed coastline with the map representation of the area where the vehicle is most likely located. The system was validated using real-world data. The tests revealed that the information contained in a flat map is insufficient for accurate position estimation. Accuracy is also significantly affected by errors in the semantic segmentation used to extract land features from camera images, as well as by potential errors in the camera viewing angle. The achieved accuracies are sufficient for navigation away from land, but when operating close to land, the proposed system appears significantly insufficient. The paper specifies the system and reports the results.