Stereo-vision based motion stabilization of a humanoid robot for the environment recognition by type-2 fuzzy logic

This paper presents an efficient ego-motion compensation method for a humanoid robot, using stereo vision and type-2 fuzzy logic. A humanoid robot should have the ability to autonomously recognize its surroundings and to make right decisions in an unknown environment. To enable a humanoid robot to do this, the ego-motion compensation method, which can eliminate the motion of a humanoid robot that causes an error of environment recognition, is suggested in this paper. The method uses a disparity map obtained from stereo vision and can be divided into three modules: the segmentation, feature extraction, and compensation modules. In the segmentation module, the objects are analyzed using type-2 FCM and features are extracted using wavelet level set extraction in the feature extraction module. The displacement for the rotation and translation can be estimated by tracking the least-square ellipse and correlation coefficient using FNCC in the compensation module. Based on the results of the experiments that were conducted in this study, it was found that the proposed method can be effectively applied to a humanoid robot.