A Monocular-vision Real-time Matching Algorithm Based on FAST Corners and Affine-improved Random Ferns

For classic calibration methods, there exist problems of low online matching speed and recognition rate in rough matching of natural features. To solve the problem, a random ferns matching algorithm based on FAST (Features from Accelerated Segment Test) corners and affine-improvement is proposed to realize monocular-vision real-time localization.This algorithm is based on a random fern semi-Bayes nonhierarchic classification model, which breaks the symmetrical framework of on-line and off-line processes in classic matching methods. It adopts FAST corners as the environment natural features to accelerate online detection, improves affine strategy for stable point set selection and training fragment generation in random ferns off-line process to improve recognition rate and reduce off-line training time, and scales back the size of random ferns to reduce time consumption of online matching. Indoor and outdoor matching and localization experiments show that the proposed algorithm meets requirements of real-timeness and recognition rate for monocular-vision localization.