Stochastic reasoning for structural pattern recognition: An example from image-based UAV navigation

This paper reports on the statistical embedding of a structural pattern recognition system into the autonomous navigation of an unmanned aerial vehicle (UAV). A rule-based system is used for the recognition of visual landmarks such as bridges in aerial views. In principle, rule-based systems can be designed and coded with no training data at hand, but a sound interpretation and utilization of the achieved results needs statistical inference and representative data sets of sufficient coverages. Flying a UAV with an experimental system is expensive, risky, and legally questionable. Therefore, we chose a virtual globe as a camera simulator providing arbitrary amounts of training and test data. The expected positions of landmarks in the aerial views are modeled by mixture models representing inliers, outliers, and intermediate forms which stem from similar structures appearing frequently in the vicinity of landmarks. The parameters of the corresponding likelihood functions are estimated by the Expectation–Maximization method. Using these estimates, we carry out tests and compare the results for heuristic, pessimistic, optimistic, and Bayesian decision rationales. This performance evaluation reveals the superiority of the Bayesian approach.