ORB Feature Uniform Distribution Algorithm Integrating Quadtree and Adaptive Thresholding

Abstract To enhance the uniform distribution characteristic of ORB extraction and leverage the global information of the image for improved positioning accuracy in visual SLAM, a novel ORB feature point extraction method combining quadtree and adaptive threshold was introduced. This method involves building a multi-resolution image pyramid and dividing the images into 30 pixels × 30 pixels grids to determine the number of feature points needed for each layer. The adaptive threshold for detecting feature points is derived from the image’s gray mean and variance. Feature points and their descriptors are then extracted using this adaptive threshold, followed by refinement through quadtree management and non-maximum suppression. Experimental data proves that the proposed solution enhances both the balanced distribution of ORB and the reliability of their tracking.