Abstract
A framework for underwater navigation by combining raw information from different sensors into a single scene description is presented. It is shown that features extracted from such a description are more robust than those extracted from an individual sensor. These descriptions are then combined from many consecutive scenes to form the basis of a new method of map building and representation as a multilevel feature space using probability theory. Comparison of maps previously created and stored in this format against newly acquired scene descriptions are then used for vehicle localization. Experimental results from offshore trials, as well as simulation results, are presented.
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Majumder, S., Rosenblatt, J., Scheding, S., Durrant-Whyte, H. (2001). Map Building and Localization for Underwater Navigation. In: Rus, D., Singh, S. (eds) Experimental Robotics VII. Lecture Notes in Control and Information Sciences, vol 271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45118-8_51
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DOI: https://doi.org/10.1007/3-540-45118-8_51
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