Research On Key Technologies Of Measuring Complex3D Surface Shape With Structured Light

With the rapid development of computer technology and data processing technology, three-dimensional profile measurement technology has a great development. But three-dimensional measurement system still exists low accuracy and measuring blind area at present, which cause that surface data can not be extracted completely, and measurement data needs to be repaired artificially and multi-splicing technology is immature. Response to these problems, combining the advantages of multi-sensors measurement and structured light measurement technology, by studying camera calibration and correction techniques, surface shape extraction techniques, exact match and fusion technology and proposed complementary algorithm thought, analyzed and complement the data acquired by multi-sensors (greater than or equal three) automatically, in order to achieve a complete three-dimensional profile data.Tt identified that the research scheme based on the structure light for complex surface shape measurement in this paper, which built a new structure measurement model and set its mathematical model, expounded measurement ideas of the measurement scheme, composition of the measurement system, technical process, and identified the key technologies which should be studied in the measurement scheme, namely:camera calibration and correction technology based on Halcon, extracting surface shape technology base on phase shift method and the improved least square method, the precise matching method base on the SURF algorithm and the complementary technologies.Camera calibration accuracy and correction effects impact on the overall accuracy of the measurement system directly. Based on Halocn system platform, compared and analyzed the calibration accuracy of the polynomial distortion model and distortion model, obtained internal and external parameters of the camera and the mean square error, analyzed factors that affected the accuracy of the calibration; According to the calibration parameters and the corresponding mapping relationship, the tests were conducted on the distortion and position correction of the images have been collected, and obtained good results.Accurate extraction of three-dimensional surface shape is a core part of the measurement system. According to high precision, strong adaptability advantages of the phase shift algorithm in the extraction of surface shape, and solves the spread errors of phase unwrapping effectively by using least squares of phase unwrapping algorithm. In this article, both of the above techniques introduced to the surface shape extraction, meanwhile, tests were conducted on the surface shape information extraction, extracted the facial model’s surface shape data successfully, evaluated and analyzed the measurement accuracy, and proposed improvements.Matching and complementary the measurement data precisely is a critical step in this scheme. As the choice of calibration method is based on two-dimensional, but the objectives are the three-dimensional, with multi-sensors data in addition to the measurement base on the existence of differences, the need to achieve the integration of measurement data by precisely matching algorithm. The data acquired by multi-sensors exist of differences outside the reference plane, In this paper, using an improved SURF algorithm to match the data (or image), combined with camera calibration techniques and complementary ideas proposed in this paper, tests were conducted and achieved matching and fusion image complementary that verified the scheme is feasible and has a certain reference value on measuring of complex surface shape.