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Mobile Robot Navigation with Intelligent Infrared Image Interpretation
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Mobile robots require the ability to make decisions such as 'go through the hedges' or 'go around the brick wall'. "Mobile Robot Navigation with Intelligent Infrared Image Interpretation" describes in detail an alternative to GPS navigation: a physics-based adaptive Bayesian pattern classification model that uses a passive thermal infrared imaging system to automatically characterize non-heat generating objects in unstructured outdoor environments for mobile robots. The resulting classification model complements an autonomous robot's situational awareness by providing the ability to classify smaller structures commonly found in the immediate operational environment. The approach described in this book is an application of Bayesian statistical pattern classification where learning involves labeled classes of data (supervised classification), assumes no formal structure regarding the density of the data in the classes (nonparametric density estimation), and makes direct use of prior knowledge regarding an object class' existence in a robot's immediate area of operation when making decisions regarding class assignments for unknown objects.
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