Illumination-invariant image-based novelty detection in a cognitive mobile robot's environment
Image-based scene representations enable a mobile robot to make a realistic prediction of its environment. Hence, it is able to rapidly detect changes in its surroundings by comparing a virtual image generated from previously acquired reference images and its current observation. This facilitates attentional control to novel events. However, illumination effects can impair attentional control if the robot does not take them into account. To address this issue, we present in this paper an approach for the acquisition of illumination-invariant scene representations. Using multiple spatial image sequences which are captured under varying illumination conditions the robot computes an illumination-invariant image-based environment model. With this representation and statistical models about the illumination behavior, the robot is able to robustly detect texture changes in its environment under different lighting. Experimental results show high-quality images which are free of illumination effects as well as more robust novelty detection compared to state-of-the-art methods.