On-Line Adaptation to Illumination Change for Mobile Robot Based on Omni-Directional Vision

Abstract

In this paper a method of on-line adaptation to illumination is proposed for mobile robot based on omni-directional in a changing illumination environment. Illumination condition is represented by an average luminance distribution of a reference object in a time series images. Illumination change is detected by computing the KL-divergence between two different distributions. A dual-threshold strategy is used to classify the current illumination into known conditions or an unknown one. According to illumination the robot decides to switch to a corresponding color calibration or learn a new one. Experiments have been carried out on the soccer robot M-TR. Experimental results show the efficiency of the proposed method.