In the procedure of surface defects detection for large-aperture aspherical optical elements, it is of vital significance to adjust the optical axis of the element to be coaxial with the mechanical spin axis accurately. Therefore, a machine vision method for eccentric error correction is proposed in this paper. Focusing on the severe defocus blur of reference crosshair image caused by the imaging characteristic of the aspherical optical element, which may lead to the failure of correction, an Adaptive Enhancement Algorithm (AEA) is proposed to strengthen the crosshair image. AEA is consisted of existed Guided Filter Dark Channel Dehazing Algorithm (GFA) and proposed lightweight Multi-scale Densely Connected Network (MDC-Net). The enhancement effect of GFA is excellent but time-consuming, and the enhancement effect of MDC-Net is slightly inferior but strongly real-time. As AEA will be executed dozens of times during each correction procedure, its real-time performance is very important. Therefore, by setting the empirical threshold of definition evaluation function SMD2, GFA and MDC-Net are respectively applied to highly and slightly blurred crosshair images so as to ensure the enhancement effect while saving as much time as possible. AEA has certain robustness in timeconsuming performance, which takes an average time of 0.2721s and 0.0963s to execute GFA and MDC-Net separately on ten 200pixels × 200pixels Region of Interest (ROI) images with different degrees of blur. And the eccentricity error can be reduced to within 10um by our method.

This paper presents an improved Oriented Feature from Accelerated Segment Test (FAST) and Rotated BRIEF (ORB) keypoints matching method for pose estimation of automatic battery-replacement systems. The key issue of the system is how to precisely estimate the pose of the camera in respect to the battery. In our system, the pose-estimation hardware module is mounted onto the robot manipulator, composed of double high brightness LED light source, one monocular camera, and two laser rangefinders. The camera is utilized to take an image of the battery, the laser rangefinders on both sides of the camera are utilized to detect the real-time distance between the battery and the pose-estimation system. The estimation result is significantly influenced by the matching result of the keypoints detected by the ORB technique. The modified matching procedure, based on spatial consistency nearest hamming distance searching method, is used to determine the correct correspondences. Meanwhile, the iterative reprojection error minimization algorithm is utilized to discard incorrect correspondences. Verified by the experiments, the results reveal that this method is highly reliable and able to achieve the required positioning accuracy. The positioning error is lower than 1 mm.

In the machine vision inspection of large-aperture aspheric optical components, the limited field of view and micron-level detection accuracy requirements make sub-aperture scanning imaging indispensable. High-precision scanning depends on the alignment of the spin axis of the mechanical system and the optical axis of the component, so the component needs to be centered before scanning. In view of this problem, this paper proposes a high-precision automatic centering method (HPACM) for rotationally symmetric aspheric optical elements based on machine vision. The goal is to adjust two reference points on the optical axis to the mechanical spin axis, which are the sphere center of the upper surface vertex and the sphere center of the lower surface vertex imaged by the upper surface of the element respectively. Adjust the first point to the spin axis is the Eccentric Error Correction (EEC), and then adjust the second point to the spin axis is the tilt error correction (TEC). In EEC and TEC, the component rotates one circle around the spin axis. If there exists eccentric or tilt error, the crosshair on the image plane will move along a circular trajectory which center on the spin axis. Then use pixel coordinates of crosshair center extraction algorithm (PCCEA) to extract the pixel coordinates of the centers of the crosshair images during the circular motion, and apply the least squares circle fitting algorithm to obtain the trajectory circle of the crosshair centers. The center of the crosshair and the center of the track circle on the image plane correspond to the sphere center of the surface vertex and a point on the spin axis of the object side respectively, and the relative positions of these two points on the image plane can be converted to the object side according to the system parameters. Experimental results show that the proposed HPACM can correct the eccentric and tilt error to within 7um and 0.5’.