Vision based ego-motion estimation for robot systems by type-2 fuzzy sets

This paper addresses an efficient vision based motion estimation method of robot systems for the ego-motion compensation using type-2 fuzzy sets. Every intelligent robot system like walking robots, service robots, automatic vehicles should have the ability to autonomously recognize its surroundings and to make right decisions under unknown environment. To enable a robot system to do this, ego-motion compensation is mandatory. Therefore, we suggest the ego-motion estimation method so that the errors of the environment recognition caused by the egomotion of intelligent robot systems are eliminated. This method uses the disparity map obtained from the stereo-vision and can be divided into three parts - segmentation, feature extraction, estimation. In the segmentation part, a novel type-2 fuzzy sets based segmentation method to extract the objects is used. In the feature extraction module, features are extracted by the wavelet level-set transform. In the estimation part, least square ellipse approximation is used to calculate the displacement for the rotation and translation between image sequences. we can estimate the displacement for the rotation and translation by tracking the least square ellipse and type-2 fuzzy sets based filtering method. From the results of experiments, we can know that the proposed method can be applied to intelligent robot systems effectively.