The objective of this project is to develop a novel closed form Perspective n Point (PnP) pose estimation algorithm for a monocular camera. A variety of techniques have been developed to estimate the pose using a monocular camera. However, the algorithms developed do not meet the simultaneous requirement of computation time, precision and efficiency. The proposed algorithm, Linear Least Squares (LLS-PnP) would minimize the cost function (non-linear error norm) which can be explicitly expressed as a quadratic function of the rotation matrix. The position is eliminated using least squares formulation. This leads to the possibility to solve the estimation problem in a closed form w.r.t. the rotation matrix. The proposed algorithm will break new grounds in computer vision and can be extended for pose estimation using Stereo vision. The final product (toolkit) will comprise of the new algorithm and the state-of-the-art PnP algorithms, which will provide the end user the choice to choose any given algorithm as well as to enable performance comparison between the various algorithms.