Industrial augmented reality (AR) applications require fast, robust, and precise tracking. In environments where conventional high-end tracking systems cannot be applied for certain reasons, marker-based tracking can be used with success as a substitute if care is taken about (1) calibration and (2) run-time tracking fidelity. In out-of-the-laboratory environments multi-marker tracking is needed because the pose estimated from a single marker is not stable enough. The overall pose estimation can be dramatically improved by fusing information from several markers fixed relative to each other compared to a single marker only. To achieve results applicable in an industrial context relative marker poses need to be properly calibrated. We propose a semiautomatic image-based calibration method requiring only minimal interaction within the workflow. Our method can be used off-line, or preferably incrementally online. When used online, our method shows reasonably good accuracy and convergence with workflow interruption of less than one second per incremental step. Thus, it can be interactively used. We illustrate our method with an industrial application scenario.

An algorithm for online distance computation based on images taken in a sewer by a robot-inspector is presented. Modern concrete sewers are made of standard cylindrical pipes separated by manhole junction areas. Circular structures typically originate at the pipe ends and entrances as well as at the joints of the pipe sections. These structures provide regular marks on the sewer images and can be used for their 3D interpretation. After each robot motion, we extract these circular structures from camera images, and recover their distance relative to the robot. The use of a laser-based orientation method to ensure almost parallel viewing direction of the robot head with respect to the pipe axis drastically simplifies the necessary image processing operations. The method is applied to real image sequences taken in a sewer test-net. Our results show that pipe ends and junctions can be reliably detected by the robot. We intend to use the approach to facilitate robot navigation for sewer inspection.

A practical algorithm for 3D interpretation of images taken in a sewer by a robot-inspector is presented. Modern sewers made of concrete present regular circular structures originating at the pipe ends as well as at the joints of the pipe sections. These pipe ends and joints provide regular marks on the sewer images and can be used for their 3D interpretation. The key idea of the paper is to extract from their image projections the 3D orientations of the circular structures and their distances relative to the robot. We adopt for this purpose a formula for the possible position of a calibrated camera on the basis of the image of a circle of known size. We describe a sequence of image processing operations that perform circle extraction, robust conic fitting and distance calculation based on original images. The reported results are preliminary in the framework of the project aimed at developing an autonomous robot for sewer inspections. We intend to use the approach to facilitate the orientation of the robot while moving in the sewer.

Biological and technical autonomous agents have to achieve basic behaviors of navigation and obstacle avoidance. If they are confined to a monocular visual sensor the optical flow field induced by egomotion can be evaluated in the periphery for speed and direction control, whereas the central flow indicates imminent collisions. We show how a complex-logarithmic mapping of the image is especially apt for the combined evaluation of the flow field for these navigational tasks. We implemented a robot simulation tool to test reliability, speed, control and robustness of our scheme.

The work presented in this paper is a first attempt at evaluating an ordinal representation for the local shape of smooth surface patches. To this end we investigate the reliability of estimating the tilt and slant components of the local surface orientation from binocular parallax. This is of interest because parallax is a commonly used cue, but has not been evaluated as to its susceptibility to image measurement errors. Our theoretical sensitivity analysis shows that the tilt is less sensitive than the slant to errors in the estimation of the viewing geometry, but that image measurement errors affect both slant and tilt seriously. In view of these results, it can not be claimed that the tilt is more reliably estimated from parallax than the slant. Despite the discovered sensitivity to image measurement errors, the tilt remains a very useful piece of information, in that it still specifies partial ordinal relations even when substantially degraded. We present a simple and efficient method to keep track of such partial information during all ongoing computation.