Target re-acquisition using acoustic features with an autonomous underwater vehicle-borne sonar

Abstract

Concurrent mapping and localization (CML) is a technique for unsupervised feature-based mapping of unknown environments, and is an essential tool for autonomous robots. For land robots, CML can be applied using video, laser, or acoustic sensors, while for autonomous underwater vehicles (AUVs) the only effective transducer in most situations is sonar. In the Generic Oceanographic Array Technology Sonar (GOATS) experiment series, CML was effectively demonstrated using a single AUV. A further hurdle in the full implementation of AUV minehunting is to re-acquire and identify targets of interest. Target re-acquisition allows other vehicles to be called into a target location to further investigate with adaptive sonar geometries or alternative sensor suites designed for classification. In this work, the features in the CML-generated map are extended from only spatial coordinates to include acoustic features such as spectral response. It is demonstrated that the inclusion of acoustic features aids in the global positioning within the map, although the fine positioning is still accomplished through standard CML. In addition, areas that are sparsely populated with targets, e.g., a sandy coastline, are shown to be more readily navigable using acoustic features.