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.

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Real-time estimation of a camera's pose relative to an object is still an open problem. The difficulty stems from the need for fast and robust detection of known objects in the scene given their 3D models, or a set of 2D images or both. This paper proposes a method that conducts a statistical analysis of the appearance of model patches from all possible viewpoints in the scene and incorporates the 3D geometry during both matching and the pose estimation processes. Thereby the appearance information from the 3D model and real images are combined with synthesized images in order to learn the variations in the multiple view feature descriptors using PCA. Furthermore, by analyzing the computed visibility distribution of each patch from different viewpoints, a reliability measure for each patch is estimated. This reliability measure is used to further constrain the classification problem. This results in a more scalable representation reducing the effect of the complexity of the 3D model on the run-time matching performance. Moreover, as required in many real-time applications this approach can yield a reliability measure for the estimated pose. Experimental results show how the pose of complex objects can be estimated efficiently from a single test image.

Real-time tracking of complex 3D objects has been shown to be a challenging task for industrial applications where robustness, accuracy and run-time performance are of critical importance. This paper presents a fully automated object tracking system which is capable of overcoming some of the problems faced in industrial environments. This is achieved by combining a real-time tracking system with a fast object detection system for automatic initialization and re-initialization at run-time. This ensures robustness of object detection, and at the same time accuracy and speed of recursive tracking. For the initialization we build a compact representation of the object of interest using statistical learning techniques during an off-line learning phase, in order to achieve speed and reliability at run-time by imposing geometric and photometric consistency constraints. The proposed tracking system is based on a novel template management algorithm which is incorporated into the ESM algorithm. Experimental results demonstrate the robustness and high precision of tracking of complex industrial machines with poor textures under severe illumination conditions.