| Navigation is a critical issue for underwater vehicles. Navigation and localization with high precision is vital for the safety and effective completion of missions of the vehicles. The Simultaneous Localization and Mapping algorithm allows the vehicle using on-board sensors such as sonars to sense the environment and extract useful information to build up a feature map of the environment while simultaneously using this map to locate itself without the help of prior environmental information map.The paper firstly discusses the principles of the simultaneous localization and mapping algorithm and the convergence property of the map generated by the algorithm. The two leading problems existed in the SLAM application that is, computational complexity and data association problem, are analyzed, so are the factors leading to their appearance and their negative influence to the system performance. For the nonlinear property of the process model and the observation model, a systemic execution of the algorithm based on the Extended Kalman Filter is presented, and simulations are also designed to demonstrate its effectiveness according to two working modes usually adopted by the underwater vehicles. The results show that the vehicle can locate itself with an improved precision compared with the one obtained by pure dead reckoning and the SLAM algorithm can be a feasible way to navigate underwater vehicles.The computational burden may arise for a long- term mission. Starting from the origin of the problem, some optimizations have been presented to reduce the on-line computational burden for the system. Since the complexity of the underwater environment makes the data association process more difficult, a method aiming at reducing association ambiguity is introduced to reduce the wrong association probability, enhance the association performance and thus settle a firm basis for the algorithm applied to the navigation and localization of underwater vehicles.
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