This paper shows a design-to-simulation approach for tackling the autonomous underwater vehicle navigation problem. Simultaneous Localization and Mapping (SLAM) is a primary research topic in robotics. Efficiently solving the problem of robotic navigation allows for robotic platforms to truly operate autonomously without the need for human in the loop interaction. This problem becomes even more important in underwater environments where traditional navigational aids such as GPS are denied due to the nature of the environment. Autonomous navigation provides the ability to address a much wider array of problems, especially in large scale deployments of AUVs in ocean environments. The goal is to provide Yellowfin, a low-cost, highly-portable AUV for use in littoral and open water environments, a robust and efficient autonomous navigation package. Use of a high frequency imaging sonar for exteroception in the underwater environment is demonstrated as well as simulation results of Extended Kalman Filters and Smoothing and Mapping algorithms for SLAM.
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