Real time distributed map building in large environments

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. In this paper, we describe the development of a scalable, multiple-vehicle SLAM system. This system, based on the Covariance Intersection algorithm, is scalable - its storage and computational costs are linearly proportional to the number of beacons in the map. Furthermore, it is scalable to multiple robots - each has complete freedom to exchange partial or full map information with any other robot at any other time step. We demonstrate the real-time performance of this system in a scenario of 15,000 beacons.