2 D-3 D Model Correspondence for Camera Pose Estimation using Sensor Fusion

Augmented reality requires accurate position and orientation estimates of a camera. In our approach inertial and visual data are fused using the conditional probability framework to estimate camera pose. We propose a measurement equation for 2D-3D model correspondence, i.e., detecting known 3D features of the scene in a camera image. The probability density of this measurement equation is investigated and results from a first application are reported.

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