Remote robotic exploration holds vast potential for gaining knowledge about extreme environments accessible to humans only with great difficulty. Robotic explorers have been sent to other solar system bodies, and on this planet into inaccessible areas such as caves and volcanoes. In fact, the largest unexplored land area on earth lies hidden in the airless cold and intense pressure of the ocean depths. Exploration in the oceans is further hindered by water's high absorption of electromagnetic radiation, which both inhibits remote sensing from the surface, and limits communications with the bottom. The Earth's oceans thus provide an attractive target for developing remote exploration capabilities. As a result, numerous robotic vehicles now routinely survey this environment, from remotely operated vehicles piloted over tethers from the surface to torpedo-shaped autonomous underwater vehicles surveying the mid-waters.;However, these vehicles are limited in their ability to navigate relative to their environment. This limits their ability to return to sites with precision without the use of external navigation aids, and to maneuver near and interact with objects autonomously in the water and on the sea floor. The enabling of environment-relative positioning on fully autonomous underwater vehicles will greatly extend their power and utility for remote exploration in the furthest reaches of the Earth's waters---even under ice and under ground---and eventually in extraterrestrial liquid environments such as Europa's oceans.;This thesis presents an operational, fielded system for visual navigation of underwater robotic vehicles in unexplored areas of the seafloor. The system does not depend on external sensing systems, using only instruments on board the vehicle. As an area is explored, a camera is used to capture images and a composite view, or visual mosaic, of the ocean bottom is created in real time. Side-to-side visual registration of images is combined with dead-reckoned navigation information in a framework allowing the creation and updating of large, locally consistent mosaics. These mosaics are used as maps in which the vehicle can navigate and localize itself with respect to points in the environment.;The system achieves real-time performance in several ways. First, wherever possible, direct sensing of motion parameters is used in place of extracting them from visual data. Second, trajectories are chosen to enable a hierarchical search for side-to-side links which limits the amount of searching performed without sacrificing robustness. Finally, the map estimation is formulated as a sparse, linear information filter allowing rapid updating of large maps.;The visual navigation enabled by the work in this thesis represents a new capability for remotely operated vehicles, and an enabling capability for a new generation of autonomous vehicles which explore and interact with remote, unknown and unstructured underwater environments. The real-time mosaic can be used on current tethered vehicles to create pilot aids and provide a vehicle user with situational awareness of the local environment and the position of the vehicle within it. For autonomous vehicles, the visual navigation system enables precise environment-relative positioning and mapping, without requiring external navigation systems, opening the way for ever-expanding autonomous exploration capabilities.;The utility of this system was demonstrated in the field at sites of scientific interest using the ROVs Ventana and Tiburon operated by the Monterey Bay Aquarium Research Institute. A number of sites in and around Monterey Bay, California were mosaicked using the system, culminating in a complete imaging of the wreck site of the USS Macon , where real-time visual mosaics containing thousands of images were generated while navigating using only sensor systems on board the vehicle.
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