基于移动机器人双目视觉的井下三维场景重建方法研究
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摘要
本文对基于移动机器人双目视觉的井下三维场景重建方法进行了研究,对现有的双目视觉系统模型进行了分析,确定了采用平行双目视觉作为机器人的视觉系统,在研究和分析了传统的摄像机标定方法,摄像机自标定方法的基础上,结合井下巷道的特点提出了基于消隐点的双目视觉系统的标定方法,经实验验证此方法是可行的；在图像的立体匹配上,通过研究和分析现有的图像立体匹配方法的特点,提出了适合井下环境应用的基于特征与支持权值相结合的图像匹配方法,在对采集的井下巷道环境图的应用实验中,可以看出此方法可以得到较好的匹配效果：对井下巷道景深较大的特点,本文提出采用分段重建的方式,即对每一次得到的空间点集只保留测量误差较小的一段重建结果,而相邻两次得到的不同空间点集,用坐标变换的方法先统一到一个坐标系下,然后进行拼接。
This paper researches on3D reconstruction of mobile robotic binocular vision. The existing model of binocular vision system has been analyzed, and parallel binocular vision as a robotic vision system is adopted. Taken the underground characteristics into consideration, vanishing point in binocular vision system calibration is proposed after analyzing the traditional camera calibration and it proves to be feasible. In stereoscopic matching, the approach combining feature and weight-support is proposed to suit to coal mining and it performs well. On account of larger depth of images getting from underground, divided reconstruction of scenes is proposed and only3D points with little error are preserved. After transforming3D points getting from two adjacent measurements in the same coordinate system, we connect these points.
This paper researches on3D reconstruction of mobile robotic binocular vision. The existing model of binocular vision system has been analyzed, and parallel binocular vision as a robotic vision system is adopted. Taken the underground characteristics into consideration, vanishing point in binocular vision system calibration is proposed after analyzing the traditional camera calibration and it proves to be feasible. In stereoscopic matching, the approach combining feature and weight-support is proposed to suit to coal mining and it performs well. On account of larger depth of images getting from underground, divided reconstruction of scenes is proposed and only3D points with little error are preserved. After transforming3D points getting from two adjacent measurements in the same coordinate system, we connect these points.
引文
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29. Andrew Howard, denis F. Wolf, Gaurav S. Sukhatme. Towards 3D mapping in large urban environments [C]. In Proceedings of 2004 IEEE/RSJ International Conference on Intelligent robots and Systems, pp:419-424, 2004
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34. S. Thrun, W. Burgard, D. Fox. A real-time algorithm for mobile robot mapping with application to multi-robot and mapping [C]. In IEEE Intemational Conference on Robotics and Automation(ICRA). pp: 31-328,2000
35. C. Fruh, A. Zakhor.3D model generation forgities using aerial photographs and ground level laser scana [J]. Computer Visuon and Pattern Recognition,2(2):31-38.2001
36. H. Zhao, R. Shibasaki Reconstruting textured CAD model for urban environment using vehicle-borne laser range scanners and line cameras [C]. In Second Internaional Workshop on Computer Vision System, pp: 284-295,2001
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40. R. Y. Tsai. An Efficient and Accurate Camera Calibration Technique for 3D Machine Vision[C]. In Proceeding of IEEE Conference of Computer Vision and Pattern Recognition, pp:364-374,1986
41.孟晓桥,胡占义.摄相机自标定方法的研究与进展[J].自动化学报,29(1)：110-112,2003
42. 吴福朝.计算机视觉中的数学方法.科学出版社,2008
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