Visual Servoing Using Cosine Similarity Metric

This article presents a new visual servoing method based on cosine similarity metric, which focuses on utilizing cosine distance defined by cosine similarity as the optimization objective of histogram-based direct visual servoing (HDVS) to design the servoing control law. As a more compact global descriptor, the histogram makes direct visual servoing more robust against noise than directly using image intensity. Cosine similarity is the cosine value between two vectors, which has been widely employed to calculate the similarity between multidimensional information. The cosine distance derived from the cosine similarity is more sensitive to the directional difference between the histograms, making the proposed method have a larger convergence rate than the existing Matusita distance-based servoing method. This advantage is verified by simulations, and experiments are conducted on a manipulator to further verify the effectiveness of the proposed method in practical situations.