Concurrent mapping and localization and map matching on autonomous underwater vehicles

In many autonomous underwater vehicle (AUV) applications, navigation is a critical issue. The positional error growth associated with dead reckoning and inertial navigation systems may preclude their use. In order to circumvent this problem, artificial beacons may be employed for long duration positioning, or Global Positioning System (GPS) data are used to provide navigation resets. Unfortunately, there are numerous missions, both military and commercial, where these solutions are unworkable. Concurrent mapping and localization (CML) is a technique, with roots in the robotics community that endeavors to build a map of a vehicle's environment by extracting information from objects within it while concurrently using that map to position the vehicle. As presented in this paper, CML relies on accurate vehicle attitude information and target tracking to retard the positional error growth rate. Map matching Is an independent, but complementary process to CML. Map matching requires good short-term navigation for the creation of a small, local map. The location of this local map Is then identified within a larger reference map, providing a periodic positional reset. The a priori reference map may have been created completely In a previous mission, or earlier in the current mission at a time when the positional uncertainty was comparatively small.