This chapter presents the prototypical design and implementation of an Intelligent Welding Gun to help welders in the automotive industry shoot studs with high precision in experimental vehicles. A presentation of the stud welding scenario and the identified system requirements is followed by a thorough exploration of the design space of potential system setups, analyzing the feasibility of different options to place sensors, displays and landmarks in the work area. The setup yielding the highest precision for stud welding purposes is the Intelligent Welding Gun -a regular welding gun with a display attachment, a few buttons for user interactions, and reflective markers to track the gun position from stationary cameras. While welders operate and move the gun, the display shows threedimensional stud locations on the car frame relative to the current gun position. Navigational metaphors, such as notch and bead and a compass, are used to help welders place the gun at the planned stud positions with the required precision. The setup has been tested by a number of welders. It shows significant time improvements over the traditional stud welding process. It is currently in the process of being modified and installed for productional use.
Augmented reality (AR) is a technology in which a user's view of the real world is enhanced or augmented with additional information generated from a computer model. Using AR technology, users can interact with a combination of real and virtual objects in a natural way. This paradigm constitutes the core of a very promising new technology for many applications. However, before it can be applied successfully, AR has to fulfill very strong requirements including precise calibration, registration and tracking of sensors and objects in the scene, as well as a detailed overall understanding of the scene. We see computer-vision and image-processing technology playing an increasing role in acquiring appropriate sensor and scene models. To balance robustness with automation, we integrate automatic image analysis with both interactive user assistance and input from magnetic trackers and CAD models. Also, in order to meet the requirements of the emerging global information society, future human-computer interaction will be highly collaborative and distributed. We thus conduct research pertaining to distributed and collaborative use of AR technology. We have demonstrated our work in several prototype applications, such as collaborative interior design and collaborative mechanical repair. This paper describes our approach to AR with examples from applications, as well as describing the underlying technology.
Abstract. For current surgical navigation systems optical tracking is state of the art. The accuracy of these tracking systems is currently determined statically for the case of full visibility of all tracking targets. We propose a dynamic determination of the accuracy based on the visibility and geometry of the tracking setup. This real time estimation of accuracy has a multitude of applications. For multiple camera systems it allows reducing line of sight problems and guaranteeing a certain accuracy. The visualization of these accuracies allows surgeons to perform the procedures taking to the tracking accuracy into account. It also allows engineers to design tracking setups interactively guaranteeing a certain accuracy.Our model is an extension to the state of the art models of Fitzpatrick et al. [1] and Hoff et al. [2]. We model the error in the camera sensor plane. The error is propagated using the internal camera parameter, camera poses, tracking target poses, target geometry and marker visibility, in order to estimate the final accuracy of the tracked instrument.
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