ABSTRACT
Navigation is a central problem in robotics, and localization plays a central role in navigation. There are two general kinds of sensors used for navigation: proprioceptive and external or exteroceptive. This chapter presents a centralized Simultaneous localization and mapping (SLAM)-like scheme for cooperative localization using the Extended Kalman Filter to improve the autonomous underwater vehicle (AUV) pose estimates. In the underwater environment, a submerged AUV usually cannot rely on optical instruments, mainly for navigation in open spaces, and global positioning system data can be used to update its position only after surfacing. Cooperative localization for AUVs is a challenging area because of the challenges posed by the underwater environment, especially the communication constraints. The key idea to decentralize the Cooperative localization using Extended Kalman Filter based on SLAM algorithm is to keep the cross-correlation information of the whole system in each vehicle, without a great increase in the communication burden.
