In order to optimize the movements of a robot, every object found in the work environment must not just be identified, but located in reference to the robot itself. Usually, object segmentation from an image is achieved using color segmentation. This segmentation can be achieved by processing the R, G and B chromatic components. However, this method has the disadvantage of been very sensitive to the changes on lighting. Converting the RGB image to the CIE-Lab color space avoids the lack of sensitivity by increasing the accuracy of the color segmentation. Unfortunately, if multiple objects of the same color are presented in the scene, is not possible to identify one of these objects using only this color space. Therefore, we need to consider an additional data source, in this case the depth, in order to discriminate objects that are not in the same plane as the object of interest. In this paper, we introduce an algorithm to detect objects, essentially on indoor environments, using CIE-Lab and depth segmentation techniques. We process the color and depth images provided by the Kinect sensor for proposing a visual strategy with real-time performance
