Uncalibrated Obstacle Detection using Normal

This paper addresses the problem of obstacle detection for mobile robots, using visual information provided by a single on-board camera, as the input. The method proposed, assumes that the robot is moving on a planar pavement, and any point lying outside this plane is considered to be an obstacle. The problem of obstacle detection is addressed by exploiting the geometric arrangement between the robot, camera and scene. During an initialization stage, we estimate an inverse perspective transformation, that maps the image plane onto the horizontal plane. During normal operation , the normal ow is computed and inverse projected on the horizontal plane. The resultant ow pattern is much simpler, and fast tests can be used to detect obstacles. A salient feature of the method proposed is the fact that only the normal ow information, or rst order time and space image derivatives, is used, thus coping with the aperture problem. Another important issue is that, contrasting with other methods, the vehicle motion and camera intrinsic and extrinsic parameters are not required to be known or calibrated. Both translational and rotational motion can be dealt with. Motion estimation results are presented, on synthetic and real image data. A real-time version implemented on a mobile robot, is described.