Combining color histogram and gradient orientation histogram for vision based global localization

Global image features and local image features are comprehensively used in mobile robot´s localization. In this paper, we proposed a geometric approach based on the combination of global image features. Considering the deficiency of the Weighted Gradient Orientation Histograms (WGOHs) for similar structure environments, color histograms are integrated as one vector for the localization. Besides the improving of weight and division for WGOH and color histogram, another weight for different emphasis on color and gradient orientation is carried out. A normalizing process is performed to better integrate the two global features. This combining approach is tested by means of locations recognition. Experimental results show that the proposed combining approach is efficient for indoor environments.