Obstacle detection and self-localization without camera calibration using projective invariants

In this paper, we propose two new vision-based methods for indoor mobile robot navigation. One is a self-localization algorithm using projective invariant and the other is a method for obstacle detection by simple image difference and relative positioning. For a geometric model of corridor environment, 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, and image matching. We predefine a risk zone without obstacles for a robot, and store the image of the risk zone, which will be used to detect obstacles inside the zone by comparing the stored image with the current image of a new risk zone. The position of the robot and obstacles are determined by relative positioning. The robustness and feasibility of our algorithms have been demonstrated through experiments in corridor environments using the KASIRI-II indoor mobile robot.