Abstract

To solve the perspective-n-point problem in visual measurement, we present a camera pose estimation algorithm involving weighted measurement uncertainty based on rotation parameters. The method does not involve the depth factor, and the objective function is converted into a least-squares cost function that contains three rotation parameters. Furthermore, the noise uncertainty model enables a more accurate estimated pose, which can be directly calculated without initial values. Experimental results prove the high accuracy and good robustness of the proposed method. In the space of ${1.5}\;{\rm m} \times {1.5}\;{\rm m} \times {1.5}\;{\rm m}$, the maximum estimation errors of rotation and translation are better than 0.04° and 0.2%.

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