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Submapping SLAM based on acoustic data from a 6-DOF AUV

Abstract Autonomous Underwater Vehicles (AUVs) need positioning systems besides the Global Positioning System (GPS), since GPS does not work in underwater scenarios. Possible solutions are the Simultaneous Localization and Mapping (SLAM) algorithms. SLAM algorithms aim to build a map while simultaneously localizing the vehicle within this map. However, they offer limited performance when faced with large scale scenarios. For instance, they do not create consistent maps for large areas, mainly because uncertainties increase with the scale of the scenario. In addition, the computational cost increases with the map size. The use of local maps reduces computational cost and improves map consistency. Following this idea, in this paper we propose a new SLAM approach that uses independent local maps together with a global level stochastic map. The global level contains the relative transformations between local maps. These local maps are updated once a new loop is detected. Local maps that are sharing a high number of features are updated through fusion, maintaining the correlation between landmarks and vehicle. Experimental results on real data obtained from the REMUS-100 AUV show that our approach is able to obtain large map areas consistently.