Feature matching algorithm of gangue in complex scene based on improved ORB
In the process of coal gangue sorting,traditional methods use visual identification and belt speed to predict the real-time position of coal gangue.However,due to slippage and deviation of the coal gangue during high-speed,long-distance transportation on the belt,the actual position often differs from the predicted position,leading to issues like missed or incorrect grabs by robotic arms,thus affecting sorting efficiency.To address this problem,we proposed an improved ORB matching algorithm for the secondary positioning of coal gangue.Firstly,we introduced local adaptive gamma correction to oFAST feature detection,enhancing matching accuracy under low lighting conditions.Additionally,to counter the dynamic interference caused by high-speed movement of the gangue,we combined BEBLID descriptors and the GMS algorithm for rapid feature matching,and employed the RANSAC algorithm to optimize feature point selection,thereby enhancing the robustness of the algorithm.Ultimately,the minimum bounding rectangle was calculated through matching points to obtain the position.Experimental results show that the proposed algorithm improves the matching accuracy of coal gangue under scale,illumination,and angle changes by 16.7%,36%,and 22%,respectively,compared to the traditional ORB algorithm,with an average error of 1.29 mm and an average matching time within 40 ms.This effectively enables gangue matching and positioning in complex scenarios.