3D motion estimation via optimized feature point selection

3D motion estimation via feature point matching is an important issue in machine vision. However, most existing methods are not good enough due to various defects, such as being sensitive to matching error, and hard to give an appropriate threshold for feature point selection. To solve these problems, a mathematical model is obtained from massive experiments to describe how the quantity and quality of matched feature points influence the motion estimation accuracy. This model can be used to select the most appropriate feature points for motion parameter calculation, hence threshold setting is avoided while estimation performance is optimized. Experimental results show that the proposed algorithm can improve the accuracy of 3D motion estimation by up to 50% with little effect on computation time. Furthermore, it is especially suitable for real time applications as there is no need to set threshold manually.