Abstract
An improved SURF (Speeded-Up Robust Feature） algorithm is proposed to deal with the time-consuming and low precision of positioning of industrial robot. Hessian matrix determinant is used to extract feature points from the target image and a multi-scale spatial pyramid is constructed. The location and scale value of feature points are determined by neighbourhood non-maximum suppression method. The direction of feature points is defined as directional feature descriptors by the binary robust independent elementary feature (BRIEF). The progressive sample consensus (PROSAC) is used to carry out second precise matching and remove mismatching points based on the Hamming distance. Then, an affine transformation model is established to describe the relationship between the template and target images. Centroid coordinates of the target can be obtained based on the affine transformation. Comparative tests were carried out to demonstrate that the proposed method can effectively improve the recognition rate and positioning accuracy of the industrial robots. The average time consuming is less than 0.2 s, the matching accuracy is 96 %, and the positioning error of the robot is less than 1.5 mm. Therefore, the proposed method has practical application importance.
Highlights
Traditional industrial robots function directly based on offline programming or teaching control and complete the designated actions by following the preset instructions, so thatManuscript received 2 May, 2019; accepted 19 August, 2019
Aiming at the mismatching points that affect the matching accuracy shown in Fig. 7, the Hamming distance is used as the similarity measure of the feature points and matching points
Suppose and are the two pairs of matching feature points corresponding to the feature set P and Q extracted by the template and target images, respectively, and can be obtained by least squares (LSM) [23]
Summary
Traditional industrial robots function directly based on offline programming or teaching control and complete the designated actions by following the preset instructions, so that. Feature similarity to eliminate false feature matching points in workpiece identification These machine vision technologies make it possible for industrial robots with intelligent vision ability in the complex industrial scene. A job target recognition and location method is proposed to improve the flexibility and robustness of robots in varying tasks. In this new method, the SURF descriptor and the BRIEF descriptor with rotation invariance are used to extract the feature points of the job target. The best matching feature points can be obtained to determine the target coordinates, which can be compared with the calibration coordinates to realize the identification and positioning of industrial robots
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