We introduce a novel sensor fusion approach for automated initialization of marker-less tracking systems. It is not limitated 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. 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 sensor. In order to solve the initialization problem, we adapt, modify and integrate advanced techniques such as plenoptic viewing, intensity-based registration, and ICP. Thereby the registration error is minimized in 3D object space rather than in 2D image. Experimental results show how complex objects can be registered efficiently and accurately to a single image.