Research On Computer Vision Based Large-scale 3d Geometric Measurement Method And Application

Large-scale 3D geometric measurement is an effective and flexible method for precise space coordinates measurement when the object's shape is irregular and the size is relatively large. In recent years, with the evolution in computer technologies and digital image processing, many computer vision based large-scale 3D geometric measurement methods incorporating computer vision technology, digital image processing technology and optics have been developed, which have found wide applications in quality control, virtual reality, reverse engineering and manufacturing. However, these existing methods have limitations when dealing with the portable measurement of large measuring range, high speed and high accuracy under external situation. In this paper, the previously research works and literatures of 3D geometric measurement are analyzed. A novel measurement method with portability, high speed, high accuracy and large measuring range is proposed aims the existing methods'shortages.This dissertation firstly discussed the illumination, lens and image sensor, which are the most relevant factors to the image quality. The mathematical model of the vision sensor as well as the relationship between the sensor's structure and the measurement error were established. The design rules for measurement equipment were provided.The algorithms for feature extraction have been studied in details. The existing corner and light stripe extraction methods have been summarized and compared. Self-adaptive corner detection method and phase congruency based stripe extraction method were proposed which can work steadily in outdoor situation. A Wiener filter based algorithm for defocus blurred image restoration was presented. The experiment results indicate that the new method effectively reduces the ringing effects and restores the defocus blurred image well.The calibration of sensor is the key technique in 3D vision measurement. For structured light sensor, the traditional methods require expensive equipments and complex procedure. Beside, those methods'efficiency is quite low. In our method, a simple 2D planar target is used. The target can be freely moved to different positions in the visible space of the camera if only the target and the structured light plane can intersect to each other. The experiment results show that the proposed method is a universal method with high efficiency and simple procedure.For stereo vision, the search space for matching one point in the left image to its correspondence in the right image is two dimensional which makes the matching quit fallible. In this paper, epipolar constraint and structured light stripe constraint which can make the corresponding points matching become a one to one situation is used. Although similar constraint has been reported by previous literates, the algorithms in these literates only offer pix level accuracy, which prevent a high overall accuracy of the final measurement. We have proposed a corresponding points matching algorithm based on local curve intersection, which provides sub-pixel level accuracy.Finally, an automatic freight train gauge-exceeding detection method based on large scale 3D geometric measurement was presented. A prototype has been developed and the experimental results have shown that the proposed method can detect freight train gauge-exceeding with high accuracy and speed. The mean square error is about 3mm when the measuring range is 10m. The measurement time for one train is within half an hour. The prototype has passed the acceptance of Shanghai railway administration. The appraisal result proved that our technology has reached the leading domestic level.