Vision-Based Path Following Without Calibration

Abstract

Because our system does not explicitly model the geometric world, its geometric accuracy is limited. Therefore, when compared with map-based approaches using calibrated cameras (Royer et al., 2007), the errors exhibited by the simple control scheme of our algorithm are rather large. Nevertheless, the remarkable flexibility and versatility of the system offer some important advantages over more precise techniques. With our approach, one can literally take an off-the-shelf camera, attach it to the robot, align it approximately in the forward direction, and start the system. The algorithm is not perfect, and there are scenarios in which it will fail. For example, occasionally the algorithm does not properly transition to the next milestone image, in which case the overlap between the current and milestone image can decrease to the point that an insufficient number of features are matched. Also, untextured scenes containing distant trees, bushes, or undecorated indoor hallways sometimes prevent the KLT algorithm from successfully tracking enough features to accurately compute the heading direction. While only a handful of features are necessary for the algorithm to succeed, it is important that features exist on both sides of the image, and that some number of features remain visible throughout the milestone. Another source of error is due to distant features. Although features near the center of the image produce a narrow funnel lane even when they are far from the camera, distant features near the side of the image produce much larger funnel lanes which are less useful for navigation. Moreover, image parallax is inversely proportional to the distance to a feature. As a result, distant features are primarily useful for correcting the rotation of the robot and are quite incapable of informing the robot about minor translation errors. This problem is compounded by the inherent ambiguity between rotation and translation in the funnel lane itself. Even though this ambiguity has little effect when the robot is near the path, it hinders the ability of the visual information to correctly determine the correct amount of rotation when the robot has deviated significantly. Odometry helps to overcome this limitation, and we have conducted experiments in which the robot consistently returns to the path after deviating by several meters. However, much larger deviations either initially or during replay cannot be handled by our present system. At any rate, it should be noted that odometry drift is not an issue because we only store odometry values local to the segment, not in a global coordinate frame.