A Feature-Based Underwater Path Planning Approach using Multiple Perspective Prior Maps

This paper presents a path planning methodology which enables Autonomous Underwater Vehicles (AUVs) to navigate in shallow complex environments such as coral reefs. The approach leverages prior information from an aerial photographic survey, and derived bathymetric information of the corresponding area. From these prior maps, a set of features is obtained which define an expected arrangement of objects and bathymetry likely to be perceived by the AUV when underwater. A navigation graph is then constructed by predicting the arrangement of features visible from a set of test points within the prior, which allows the calculation of the shortest paths from any pair of start and destination points. A maximum likelihood function is defined which allows the AUV to match its observations to the navigation graph as it undertakes its mission. To improve robustness, the history of observed features are retained to facilitate possible recovery from non-detectable or misclassified objects. The approach is evaluated using a photo-realistic simulated environment, and results illustrate the merits of the approach even when only a relatively small number of features can be identified from the prior map.