Stereo-vision based motion estimation of a humanoid robot for the ego-motion compensation by type-2 fuzzy sets

This paper addresses an efficient the vision based motion estimation method of a humanoid robot for the ego-motion compensation using type-2 fuzzy sets. A humanoid robot should have the ability to autonomously recognize its surroundings and to make right decisions under unknown environment. To enable a humanoid robot to do this, ego-motion compensation for a humanoid robot is mandatory. Therefore, we suggest the ego-motion estimation method so that the errors of the environment recognition caused by the ego-motion of a humanoid robot 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. Moreover, least square ellipse approximation is used in the estimation part to calculate the displacement for the rotation and translation between image sequences. From the results of experiments, we can know that the proposed method can be applied to a humanoid robot effectively.