Robust template based corner detection algorithms for robotic vision

Image corners encapsulate gradient changes in multiple directions. Therefore, corners are considered as efficient features for use in robotic navigation algorithms. Template based corner detection has a low computational complexity and is straightforward to implement. With the appropriate design of templates, satisfactory detection accuracy can also be achieved. In this paper, we introduce two new template based corner detection algorithms to be used to assist robot vision: the matching based corner detection, namely, MBCD; and the correlation based corner detection, namely, CBCD. These two approaches outperform existing template based approaches in the means that they reduce detection of spurious corners by considering ideal corners with at least two-pixel length on the corner arm directions. Experimental results show that the proposed algorithms detect essential corners for synthetic images and natural images satisfactorily according to human visual perception. We also examine the robustness of the two corner detection approaches in terms of the average repeatability and localization error. Since our approaches are computationally efficient, it makes these template based corner detection algorithms suitable for real time support in robotic applications. Comparisons with existing corner detection algorithms are also presented.