| In recent years, with advances in digital technology, ordinary digital cameraperformance and continuously improve the performance of your computer, digital camerasgained popularity, many photogrammetric systems are ordinary digital camera as the primarysensor data acquisition. Therefore, the digital camera calibration has gradually become one ofthe focus areas of photogrammetry and computer vision.The extraction accuracy and efficiency of image point coordinate is affect cameracalibration automation degree and precision of the important factors, we designed a codedsigns, through the automatic identification and extraction of coded signs, established by directlinear transformation method things-like projection transform relationships and use thatimage as the relationship between the completion point automatic early positioning. By edgedetection, least squares ellipse fitting, linear least squares fitting procedure to completehigh-precision positioning image as the center point coordinates. Image pixel coordinates toachieve the automatic, high-precision measurements.The camera calibration as the research object in this paper, Method of photogrammetryself-calibration bundle adjustment camera calibration were studied and implementation, andanalysis of the characteristics of the method. In-depth study of computer vision algorithmsand LM sparse matrix techniques, and apply self-calibration bundle adjustment, to find asolution to be calculated self-calibration bundle adjustment problems of slow, sparse matrixbased on the realization self-calibration bundle adjustment camera calibration methods.This paper made a series of experiments have verified the efficiency of this method ofcalibration, calibration accuracy and feasibility in the application. Experimental results showthat the proposed method a high degree of automation, high computing efficiency, highprecision calibration, to meet the UAV aerial photogrammetry and general close-range photogrammetry. |
PDF Full Text Request