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With the development of machine vision technology, in the process of visual navigation with images, it is necessary to match the local geometric features or global features of the images; however, the matching of local geometric features is low in accuracy and difficult to be used in tracking. In contrast, template-based global feature matching can directly use the information of the entire image, and it has high robustness to illumination variations and occlusions, so it has attracted widespread attention. At present, the classical matching algorithms based on templates mainly include Sum of Absolute Differences (SAD), Sum of Squared Differences (SSD), Normalized Cross Correlation (NCC), and Mutual Information (MI). In order to make it more reasonable to evaluate and compare the performance of the algorithms, in this paper, we decided to compare Mean Absolute Differences (MAD), Mean Square Differences (MSD), Zero-mean Normalized Cross Correlation (ZNCC), and Normalized Mutual Information (NMI). During the experiment, the Gaussian noise, illumination variations and occlusion were applied to the current image to simulate complex navigation scenes, and then matched it with the template images. The matching values obtained by the above four matching algorithms in different scenes were collectively called as alignment metric values. The matching effects of the four algorithms were evaluated from the following aspects including the smoothness of the metric value, the number of local extremums and whether the best position was in the correct alignment position. The results showed that the accuracy of MSD was greatly affected by noise and was not suitable for scenes interfered by noise, the number of local extremums of ZNCC changed greatly under the conditions of noise, illumination changes, and occlusion, the alignment metric values became unsmooth. In comparison, the NMI showed good robustness and accuracy in different conditions.
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