An Improved ORB Algorithm Based on Optimized Feature Point Extraction

The feature points extracted by the traditional ORB algorithm are not evenly distributed, redundant and have no scale invariance. To solve this problem, this paper improved the traditional ORB algorithm and proposed an optimized feature point extraction method. The image is divided into regions firstly. According to the total number of feature points to be extracted and the number of divided regions, the algorithm calculates the number of feature points to be extracted for each region, which solves the problem of feature point overlap and redundancy in the feature point extraction process. By constructing the image pyramid and extracting feature points on each layer, the problem that the feature points extracted by ORB algorithm do not have scale invariance is solved. The experimental results show that the feature points extracted by our algorithm are more uniform and reasonable without losing the accuracy of image matching, and the extraction speed is about 16% higher than that of the traditional ORB algorithm.