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The Perspective-n-Point (PnP) problem has been widely studied in bothcomputer vision and photogrammetry societies. With the development of featureextraction techniques, a large number of feature points might be available in asingle shot. It is promising to devise a consistent estimator, i.e., theestimate can converge to the true camera pose as the number of pointsincreases. To this end, we propose a consistent PnP solver, named \emph{CPnP},with bias elimination. Specifically, linear equations are constructed from theoriginal projection model via measurement model modification and variableelimination, based on which a closed-form least-squares solution is obtained.We then analyze and subtract the asymptotic bias of this solution, resulting ina consistent estimate. Additionally, Gauss-Newton (GN) iterations are executedto refine the consistent solution. Our proposed estimator is efficient in termsof computations -- it has $O(n)$ computational complexity. Experimental testson both synthetic data and real images show that our proposed estimator issuperior to some well-known ones for images with dense visual features, interms of estimation precision and computing time.

CPnP: Consistent Pose Estimator for Perspective-n-Point Problem with Bias Elimination