minima situation of Lu’s method and proposed a method to avoid this limitation [@schweighofer2006robust].

P$n$P algorithms are widely used in applications such as structure from motion [@havlena2010efficient] and monocular SLAM [@mur2014fast], which require dealing with hundreds or even thousands of noisy feature points and outliers in real-time. The fact that outliers have a much greater impact on P$n$P accuracy than image Gaussian white noise makes it is necessary for the P$n$P algorithm to handle outliers efficiently. Conventional method to handle outliers is to combine a RANSAC-like scheme with the P3P or P4P algorithms. Besides, L1-norm is also widely used to handle a certain amount of outliers [@kahl2008practical][@ke2007quasiconvex] because the L1-norm penalty is less sensitive to outliers than the L2-norm penalty. Although a L1-norm-based energy function is more robust to outliers, it cannot absolutely get rid of outliers and its computation is more complex.

Ferraz *et al*. proposed a very fast P$n$P method that can handle up to 50% of outliers [@ferraz2014very]. The outlier rejection mechanism is integrated within the pose estimation pipeline with negligible computational overhead. Compared to Ferraz’s method, the R1PP$n$P algorithm proposed in this paper demonstrates much stronger robustness, but is slower.

Fundamental Model
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In this paper 