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