LAUSR

Abstract. Robot intelligent inspection is widely used in the positioning of various pointer instruments in power, petroleum, chemical, and other industries. Aiming at the technical problems of poor adaptability, poor real-time performance, and low positioning accuracy of the pointer instrument positioning method in the existing substation intelligent inspection robot system, we propose a simple and effective pointer instrument positioning detection algorithm. The algorithm first extracts locally adaptive regression kernels (LARK) features of the input image, and the dimension of the LARK feature is reduced using the principal components analysis algorithm. Then, the template image is slid in the input image, the cosine similarity is used as an evaluation index, and the Fourier transform is used to accelerate the convolution operation in the cosine similarity calculation. Finally, the accelerated-KAZE algorithm is used to extract the feature points of the pointer-type instrument area image and the template image, and the statistical method of grid motion was used to eliminate the wrong matching points. The remaining matching points were processed by random sample consensus algorithm, and the homography matrix was obtained. The image registration was completed by the homography matrix, and the pointer-type instrument region positioning was realized. The experimental results show that the proposed method has good adaptability, strong real-time performance, and high accuracy of pointer-type instrument positioning.