Multi-view fusion based on belief functions for seabed recognition
Hicham Laanaya, Arnaud Martin
Abstract
Hicham Laanaya, Arnaud Martin
Abstract
In this paper, we present an approach of automatic seabed recognition from multiple views of side-scan sonar. We integrate detailed knowledge about each view: the nature of the seabed, the position and the uncertainty and the imprecision related to each information. To exploit information from multiple views, a fusion strategy for seabed recognition has been developed. It is based on the theory of belief functions, that deals with the imperfection of information, computed over tiles of seabed. We show the application of our method on simulated sonar data given by an autonomous underwater vehicle. This application illustrates the interest of a belief fusion approach and the analysis of the final results show the benefits.
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In this paper, we present an approach of automatic seabed recognition from multiple views of side-scan sonar. We integrate detailed knowledge about each view: the nature of the seabed, the position and the uncertainty and the imprecision related to each information. To exploit information from multiple views, a fusion strategy for seabed recognition has been developed. It is based on the theory of belief functions, that deals with the imperfection of information, computed over tiles of seabed. We show the application of our method on simulated sonar data given by an autonomous underwater vehicle. This application illustrates the interest of a belief fusion approach and the analysis of the final results show the benefits.
Key concepts: Seabed, Sonar, Side-scan sonar, Computer science, Sensor fusion, Exploit, Underwater, Information fusion