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Delayed stochastic mapping

This thesis presents improved methods for performing feature-based concurrent mapping and localization (CML) using sonar. A key limitation in previous stochastic mapping algorithms for CML has been the requirement that either a measurement must be used when it is obtained or it must be discarded. The motivation for delayed stochastic mapping is to develop methods whereby decisions about measurements can be safely made without risking the loss of data. A limited multiple hypothesis approach is developed in which first, tentative decisions are made in isolation of other decisions, preventing a computational explosion, and then later recombined with the full solution using delayed mapping. Iterative data association strategies are developed that use a temporal recursion to try to make improved about past data in light of future information. Two experiments to validate these new methods have been performed using a binaural 500 kHz sonar system mounted on a robotic positioning system in a 9 meter by 3 meter by 1 meter tank. The first experiment demonstrates successful classification of simple objects (corners and planes) using multiple submaps and delayed mapping. The second experiment demonstrates the use of iterative data association to achieve increased accuracy with only a modest increase in computational requirements.