Sensing passive thermal objects in outdoor scenes for autonomous robots

A physics-based adaptive Bayesian pattern classification model that uses a thermal infrared imaging system to automatically sense passive thermal objects in unstructured outdoor environments for mobile robots is described. The usefulness of thermal infrared imaging as a mobile robot sensing modality is explored, and a set of thermal-physical features used to characterize passive thermal objects in outdoor environments is presented. Objects that extend laterally beyond the thermal camera's field of view, such as brick walls, hedges, picket fences, and wood walls as well as compact objects that are laterally within the thermal camera's field of view, such as metal poles and tree trunks, are considered. Classification of passive thermal objects is a subtle process since they are not a source for their own emission of thermal energy. A detailed analysis is included of the acquisition and preprocessing of thermal images, as well as the generation and selection of thermal-physical features from these objects within thermal images. Classification performance using these features is discussed, as a precursor to the design of the physics-based model to automatically classify these objects.