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This paper presents a novel approach to AUV localization, based on asemantic-aided particle filter. Particle filters have been used successfullyfor robotics localization since many years. Most of the approaches are howeverbased on geometric measurements and geometric information and simulations. Inthe past years more and more efforts from research goes towards cognitiverobotics and the marine domain is not exception. Moving from signal to symbolbecomes therefore paramount for more complex applications. This paper presentsa contribution in the well-known area of underwater localization, incorporatingsemantic information. An extension to the standard particle filter approach ispresented, based on semantic information of the environment. A comparison withthe geometric approach shows the advantages of a semantic layer to successfullyperform self-localization.

A semantic-aided particle filter approach for AUV localization