Ant colony graph matching method based on G-W distance

Abstract

The invention relates to an ant colony graph matching method based on a G-W distance. The method comprises the following steps: selecting feature points of a to-be-matched graph; generating an H matrix under a G-W distance through the sampling information data of the feature points; establishing a secondary convex optimization model for the minimum difference degree of the G-W distance between thefeature points, and proposing the hypothesis of the model in combination with the ant colony; stipulating physical constraint conditions of the ant colony in a G-W distance minimum difference degreeoptimization process, so that the ant colony meets a one-to-one correspondence matching principle; accumulating distances among city nodes traversed by ants; optimizing the quadratic convex optimization problem under the G-W distance, and solving a shortest path and city nodes on the path; and defining the shortest path as the overall difference degree between the two manifold curved surfaces, andcalculating the matching relationship between the feature points by utilizing the relationship between the city nodes on the shortest path and the serial numbers of the feature points to complete graph matching. The method is the optimal solution closest to the real theory, and the dependence of traditional numerical iteration optimization on the initial solution is overcome.

Description

technical field [0001] The invention belongs to the technical field of image processing, specifically a G-W distance-based ant colony pattern matching method, and relates to computer graphics, computer vision, and robotics. Background technique [0002] Graph matching has always been a hot issue in the fields of computer graphics, computer vision, and robotics. At present, the research methods for 3D pattern matching mainly include skeleton matching method, sparse matching method, numerical iterative convergence matching method, and geometric distance optimization matching method. value. For example, when using a robot to create a map, multiple robots need to collect information together, and finally synthesize the sub-maps collected by each robot into a common map, which is a huge and complicated job. In this process, the method of landmark matching is required to realize the fusion of multiple maps, so any landmark matching error will have a great impact on the accuracy ...

AI Technical Summary

Problems solved by technology

The main reason is that the existing numerical iterative convergence optimization depends on the selection of the initial point, and it is easy to fall into a local solution, the calculation cost is high, and the data under noise interference will produce large errors during the calculation process.

This leads to low matching efficiency and unstable optimization results

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