The oceanographic research and industrial communities have a persistent demand for detailed three dimensional sea floor maps which convey both shape and texture. Such data products are used for archeology, geology, ship inspection, biology, and habitat classification. There are a variety of sensing modalities and processing techniques available to produce these maps and each have their own potential benefits and related challenges. Multibeam sonar and stereo vision are such two sensors with complementary strengths making them ideally suited for data fusion. Data fusion approaches however, have seen only limited application to underwater mapping and there are no established methods for creating hybrid, 3D reconstructions from two underwater sensing modalities. This thesis develops a processing pipeline to synthesize hybrid maps from multi-modal survey data. It is helpful to think of this processing pipeline as having two distinct phases: Navigation Refinement and Map Construction. This thesis extends existing work in underwater navigation refinement by incorporating methods which increase measurement consistency between both multibeam and camera. The result is a self consistent 3D point cloud comprised of camera and multibeam measurements. In map construction phase, a subset of the multi-modal point cloud retaining the best characteristics of each sensor is selected to be part of the final map. To quantify the desired traits of a map several characteristics of a useful map are distilled into specific criteria. The different ways that hybrid maps can address these criteria provides justification for producing them as an alternative to current methodologies. The processing pipeline implements multi-modal data fusion and outlier rejection with emphasis on different aspects of map fidelity. The resulting point cloud is evaluated in terms of how well it addresses the map criteria. The final hybrid maps retain the strengths of both sensors and show significant improvement over the single modality maps and naively assembled multi-modal maps.
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