Traditionally, optical positioning has been an area that attracted significant interest for specialised applications such as robotic navigation. With the recent development and integration of cameras in mobile devices, optical positioning is gaining further interest in indoor positioning applications targeting human navigation. Furthermore, the release of the Google Tango platform has attracted the interest of several researchers aiming to improve optical positioning using 3D mapping. However, estimating a 3D pose based on PnP problem has been a challenge which affects positioning accuracy. This paper proposes two novel strategies to improve PnP algorithms, and hence accuracy. The first "scaling" strategy, is based on minimising model size, with the second, "sub-model" strategy, involving the selection of only the related area of the model to be used. The proposed strategies also have the advantage of limiting the error to the set scale size. The scale and sub-model strategies show an average improvement of 1.61 and 3.33 m, respectively.