We introduce a novel sensor fusion approch for automated initialization of marker-less tracking systems. It is not limited in tracking range and working environment, given a 3D model of the objects or the real scene. This is achieved based on a statistical analysis and probabilistic estimation of the uncertainties of the tracking sensors. The explicit representation of the error distribution allows the fusion of different sensor data, e.g. of mobile tracking sensors with stationary sensors, in order to estimate the initial pose and improve the registration accuracy. This methodology was applied to an augmented reality system, using a mobile camera and several stationary tracking sensors, and can be easily extended to the case of any additional sensors. The initialization consists of an iterative pose estimation and refinement process using both stationary and mobile cameras. Thereby the registration error is minimized in 3D object space rather than in the 2D image. Experimental results show how complex objects can be registered efficiently and accurately to a single initial image.     «

We introduce a novel sensor fusion approch for automated initialization of marker-less tracking systems. It is not limited in tracking range and working environment, given a 3D model of the objects or the real scene. This is achieved based on a statistical analysis and probabilistic estimation of the uncertainties of the tracking sensors. The explicit representation of the error distribution allows the fusion of different sensor data, e.g. of mobile tracking sensors with stationary sensors...     »