Efficient block matching using improved particle swarm optimization with application to displacement measurement for nano motion systems

This paper is devoted to the micro vision based displacement measurement for a nano-positioning stage with the aid of a single camera. For this purpose, a pinhole imaging model as well as corresponding Jacobin matrix is established, with which an efficient block matching (BM) algorithm is developed based on the particle swarm optimization (PSO) algorithm. In particular, an improved PSO algorithm is developed by proposing a new fitness updating strategy (FUS) such that the particles' migration can be performed with an efficient way in the whole optimization process. The improved PSO can significantly speed up the optimization process, but meanwhile resulting in a coarse-level estimation result. The full search algorithm (FSA) is thus performed on the coarse-level result in order to achieve the final fine-level result. The effectiveness of the presented method was demonstrated using the testing data acquired from an experimental platform, where comparative studies with those existing benchmark methods were also provided. The comparison results show that the method achieves a significant improvement on computational efficiency without loss of accuracy, which implies it has a better balance/trade-off between the measurement accuracy and computation efficiency in real applications.