Data fusion and distributed robotic perception

TL;DRAbstract

This thesis explores data fusion and distributed robotic perception through a series of theoretical developments, analyses and experiments. First, a GSF with component extended Kalman filters (EKF) is proposed as an approach to localize an autonomous vehicle in an urban environment with limited GPS availability. The GSF is used because of its ability to represent the posterior distribution of the vehicle pose with better efficiency (fewer terms, less computational complexity) than a corresponding bootstrap particle filter with various numbers of particles due to the interaction with measurement hypothesis tests. A series of in-depth empirical studies are performed using 37 minutes of recorded data from Cornell University’s autonomous vehicle driven in an urban environment, including a 32 minute GPS blackout. Second, a distributed grid-based terrain mapping algorithm using Gaussian Mixture Models is developed for use in tree connected and arbitrary connected sensor networks. The distr

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