each of the control points.* The P$n$P problem has been studied for many years. In early studies, direct linear transformation (DLT) [@hartley2003multiple] was used as a solution in a straightforward way by solving a linear system. However, DLT ignores the intrinsic camera parameters, which are assumed to be known, and therefore generally leads to a less stable pose estimate.

In the past decade, researchers have proposed many P$n$P methods to improve speed, accuracy, and robustness to outliers. The P$n$P methods can be roughly classified into non-iterative and iterative methods. Generally speaking, non-iterative methods are more efficient but are unstable under image noise and outliers. Many non-iterative P$n$P methods are based on a set of small number of points ($n=3,4$). They are referred to as P3P [@dementhon1992exact] [@gao2003complete] [@kneip2011novel] or P4P [@abidi1995new] [@bujnak2008general][@horaud1989analytic][@quan1999linear] methods. P3P is the smallest subset of control points that yields a finite number of solutions [@kneip2011novel][@wolfe1991perspective]. When the intrinsic camera parameters are known and we have $n\geq 4$ points, the solution is generally unique. Triggs proposed a P$n$P method with four- or five- correspondences [@triggs1999camera]. These P$n$P methods based on less than four correspondences do not make use of redundant points and are very sensitive to noise 