This paper proposes a robust image stabilization system for a mobile robot using an Extended Kalman Filter (EKF). Though image information is one of the most efficient data used for robot navigation, it is subjected to noise which is the result of internal vibration as well as external factors such as uneven terrain, stairs, or marshy surfaces. The camera vibration deteriorates the image resolution by destroying the image sharpness, which seriously prevents mobile robots from recognizing their environment for navigation. In this paper, an inclinometer was used to measure the vibration angle of the camera system mounted on the robot to obtain a reliable image by compensating for the angle of the camera vibration. In addition the angle prediction obtained by using the EKF enhances the image response analysis for real time performance. The experimental results show the effectiveness of the proposed system used to compensate for the blurring of the images.

In this paper, we propose a digital image stabilization technique using edge detection and Lucas-Kanade optical flow in order to minimize the motion of the shaken image. The accuracy of motion estimation based on block matching technique depends on the size of search window, which results in long calculation time. Therefore it is not applicable to real-time system. In addition, since the size of vector depends on that of block, it is difficult to estimate the motion which is bigger than the block size. The proposed method extracts the trust region using edge detection, to estimate the motion of some critical points in trust region based on Lucas-Kanade optical flow algorithm. The experimental results show that the proposed method stabilizes the shaking of motion image effectively in real time.