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Abstract
Estimating ego motion of monocular visual system from the input image sequence is a critical problem in computer vision. This paper considers this problem from the view of time domain optimisation. At first, based on the frame to frame matches, the traditional monocular visual navigation algorithm is introduced, and this process is also seen as a two-view method. On the basis of the three-view matches, the trifocal tensor based monocular navigation algorithm is described. In addition, this paper proposes a novel moving horizon estimation (MHE) based algorithm to tackle the pose estimation problem. In this algorithm, the epipolar constraints and the closed loop constraints of position along with consecutive time steps are all involved in the global optimisation model. In accordance with this optimisation function, the MHE is introduced to achieve the tradeoff between computation costs and estimation accuracy. Because of the inherent robustness of the referred moving horizon estimator, it is becoming more popular in various applications. Based on the general epipolar constraints, trifocal tensor constraints and step crossed optimisation function during the whole moving window, the corresponding three referred pose estimation algorithms are all implemented comparatively at the end of this paper. The rotation and translation experiments are implemented to validate the improvements of the time domain optimisation methods.