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.