Selective Submap Joining for underwater large scale 6-DOF SLAM

Autonomous Underwater Vehicles (AUVs) need positioning systems different than the Global Positioning System (GPS), which does not work in underwater scenarios. A possible solution to this lack of GPS signal are the Simultaneous Localization and Mapping (SLAM) algorithms. SLAM algorithms aim to build a map while simultaneously localize the vehicle within it. These algorithms suffer from several limitations in front of large scale scenarios. For instance, they do not perform consistent maps for large areas, mainly because uncertainties increase with the scenario. In addition, the computational cost increases with the map size. It has been demonstrated that the use of local maps reduces computational cost and improves map consistency. Following this idea, in this paper we propose a new SLAM technique based on using independent local maps, combined 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 and the amount of overlapping between local maps is high. Thus, maps 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.