Efficient Line and Patch Feature Characterization and Management for Real-time Camera Tracking

Abstract

One of the key problems of augmented reality is the tracking of the camera position and viewing direction in real-time. Current vision-based systems mostly rely on the detection and tracking of fiducial markers. Some markerless approaches exist, which are based on 3D line models or calibrated reference images. These methods require a high manual preprocessing work step, which is not applicable for the efficient development and design of industrial AR applications. The problem of the preprocessing overload is addressed by the development of vision-based tracking algorithms, which require a minimal workload of the preparation of reference data. A novel method for the automatic view-dependent generation of line models in real-time is presented. The tracking system only needs a polygonal model of a reference object, which is often available from the industrial construction process. Analysis-by-synthesis techniques are used with the support of graphics hardware to create a connection between virtual model and real model. Point-based methods which rely on optical flow-based template tracking are developed for the camera pose estimation in partially known scenarios. With the support of robust reconstruction algorithms a real-time tracking system for augmented reality applications is developed, which is able to run with only very limited previous knowledge about the scene. The robustness and real-time capability is improved with a statistical approach for a feature management system which is based on machine learning techniques.