Obstacle detection and self-localization without camera calibration using projective invariants
نویسندگان
چکیده
In this paper, we propose two new vision-based methods for irzdooimobile robot navigation. One is a selflocalization algorithm using projective invariant and the other is a method for obstacle detection by simple image difference and relative positioning. Foia geometric model of corridor erzvironnaent, we use natural features formed by floor, walls, and door frames. Using the cross-ratios of the features can be effective and robust in building and updating model-base, arzd matching. We predefine n risk zone witho~it obstacles fo r a robot, and store the image of the risk zone, which will be used to detect obstacles imide the zoize by comparing the stored image with the current image of a new risk zone. The position of the robot atad obstacles are determined by relative positioning. The robustness and feasibility of OW algorithms have been deinonstrated through experiments in corridor enviroonnaents using an indoor mobile robot, KASIRI-II ( m i s t SImple Roving Intelligence).
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