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In this paper, a robust pose (i.e., position and orientation) estimation algorithm using two-views captured by a calibrated monocular camera is presented. A collection of pose hypotheses is obtained when more than the minimum number of feature points required to uniquely identify a pose are available in both the images. The pose hypotheses - unit quaternion and unit translation vectors - lie on the S3 and S2 manifolds in the Euclidean 4-space and 3-space, respectively. Probability density function (pdf) of the rotation and translation pose hypotheses is evaluated by gridding the unit spheres where a robust, coarse pose estimate is identified at the mode of the pdf. Further, a "refining" pdf of the geodesic distance from the coarse pose estimate is constructed for the hypotheses within a grid containing the coarse estimate. A refined pose estimate is obtained by averaging the low-noise hypotheses in the neighbourhood of the mode of refining pdf. Pose estimation results of the proposed method are compared with RANSAC and nonlinear mean-shift (NMS) algorithms using the Oxford Corridor sequence and the robustness to feature outliers, image noise rejection, and scalability to number of features is analyzed using the synthetic data experiments. Processing time comparison with the RANSAC and NMS algorithms indicate that the deterministic time requirement of the proposed and NMS algorithms is amenable to a variety of visual servo control applications.