Type-2 Fuzzy Sets based Ego-Motion Compensation of a Humanoid Robot for Object Recognition

Humanoid robots have the similar appearance to human being with a head, two arms and two legs, and has some intelligent abilities as human being, such as object recognition, tracking, voice identification, obstacle avoidance, and so on. Since they try to simulate the human structure and behavior and they are autonomous systems, most of the times humanoid robots are more complex than other kinds of robots. In the case of moving over an obstacle or detecting and localizing an object, it is critically important to attain as much precise information regarding obstacles/object as possible since the robot establishes contact with an obstacle/object by calculating the appropriate motion trajectories to the obstacle/object. Vision system supplies most of the information, but the image sequence from the vision system of a humanoid robot is not static when a humanoid robot is walking, so some problems occur due to the ego-motion. Therefore, the humanoid robots need the algorithms that can autonomously determine their action and paths in unknown environments and compensate the ego-motion using the vision system. The vision system is one of the most important sensors in the humanoid robot system, it can supply lots of information which a humanoid robot needs. However the vision system indispensably requires the stabilization module, which can compensate the ego-motion of itself for the more precise recognition. Over the years, a number of researches have been achieved in motion compensation field on the vision system mounted in the robot. Some researches use single camera, but the stereovision, which can extract information regarding the depth of the environment, is commonly used. Robot motion from stereo-vision can be estimated by the 3D rigid transform, using the 2D multi-scale tracker, which projects 3D depth information on the 2D feature space. The scale invariant feature transform (SIFT) (Hu et al., 2007), which is a local feature based algorithms to extract features from images and estimate transformation using their location, and iterative closest point (ICP) (Milella & Siegwart, 2006), which is used for registration of digitized data from a rigid object with an idealized geometric model, have been used mainly for motion estimation using single camera or stereo camera for the video stabilization or autonomous navigation purposes, and have been widely used in wheeled robots (Lienhart & Maydt, 2002)(Beveridge et al., 2001)(Morency & Gupta, 2003). Moreover, the optical flow based method, which can estimate the motion by 3D normal flow constraint using gradient-based error function, is widely used, because of the simplicity of