The most important applications of many computer vision systems are based on robust features extraction, matching and tracking. Due to their extraction techniques, image features locations accuracy is heavily dependent on the variation in intensity within their neighbourhoods, from which their uncertainties are estimated. In the present work, a robust L∞ optimisation solution for monocular motion estimation systems has been presented. The uncertainty estimation techniques based on SIFT derivative approach and its propagation through the eight-point algorithm, singular value decomposition SVD and the triangulation algorithm have proved an improvement to the global motion estimation. Using monocular systems makes the motion estimation challenging due to the absolute scale ambiguity caused by projective effects. For this, we propose robust tools to estimate both the trajectory of a moving object and the unknown absolute scale ratio between consecutive image pairs. Experimental evaluations showed that robust convex optimisation with the L∞ norm under uncertain data and the Robust Least Squares clearly outperform classical methods based on Least Squares and Levenberg-Marquardt algorithms.

A novel 6-degree-of-freedom (DoF) parallel manipulator with three planar mechanism limbs is proposed and its kinematics and statics are analyzed systematically. First, the characteristics of the proposed manipulator are analyzed and the degree of freedom is calculated. Second, the formulae for solving the displacement, the velocity, and the acceleration are derived. Third, an analytic example is given for solving the kinematics and statics of this manipulator, and the analytic solved results are analyzed and verified by the simulation mechanism. Finally, a workspace is constructed and analyzed based on a comparison between the proposed manipulator and another 6-DoF parallel manipulator.