Correlation-extreme visual navigation of unmanned aircraft systems based on speed-up robust features
The peculiarities of correlation-extreme visual navigation are considered. Descriptors with 64 elements of feature points of surface images are selected on the basis of the speed-up robust feature method. An analysis of possible criteria correlation functions is carried out to find the best match between the template descriptors and current images. The use of normalized correlation function is proposed based on the matrix multiplication properties of descriptors. It allows minimizing the number of false matches in comparison with the Euclidean distance in the descriptor space. The proposed matching strategy sufficiently decreases the computation time.