Many of the future missions for mobile robots demand multi-robot systems which are capable of operating in large environments for long periods of time. One of the most critical capabilities is the ability to localize - a mobile robot must be able to estimate its own position and to consistently transmit this information to other robots and control sites. ALthough state-of-the-art GPS is capable of yielding unmatched performance over large areas, it is not applicable in many environments (such as within city streets, under water, indoors, beneath foliage or extra-terrestrial robotic missions) where mobile robots are likely to become commonplace. A widely researched alternative is Simultaneous Localization and Map Building (SLAM): the vehicle constructs a map and, concurrently, estimates its own position. However, most approaches are non-scalable (the storage and computational costs vary quadratically and cubically with the number of beacons in the map) and can only be used with multiple robotic vehicles with a great degree of difficulty.
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