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The unstructured scenario, the extraction of significant features, the imprecision of sensors along with the impossibility of using GPS signals are some of the challenges encountered in underwater environments. Given this adverse context, the Simultaneous Localization and Mapping techniques (SLAM) attempt to localize the robot in an efficient way in an unknown underwater environment while, at the same time, generate a representative model of the environment. In this paper, we focus on key topics related to SLAM applications in underwater environments. Moreover, a review of major studies in the literature and proposed solutions for addressing the problem are presented. Given the limitations of probabilistic approaches, a new alternative based on a bio-inspired model is highlighted.

Mapping a 3D environment is a big challenge for roboticists, expecially in underwater environments. Nowadays, the most applied solution to this problem relies in Probabilistic Filters, but with the discovery of neurons in the mammalian brain associated with navigation tasks, biological approaches has been take place. This paper presents a system inspired in mammalian brain to solve the problem of mapping and localization of robots. Preliminaries results in simulated environments shows the relevance of the proposed method, which is highly parallelizable and capable of running in real time applications.

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