基于双目视觉的空间非合作目标尺度测量技术研究
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摘要
对空间非合作目标的关键结构尺寸进行快速精准测量对于实现对目标的动态跟踪与捕获十分重要。文章针对未知形状的非合作目标,基于双目立体视觉测量原理,提出一种点云轮廓线的直接提取算法,不需要进行目标外形的3D还原,即可直接进行目标尺寸测量,从而可以根据外观轮廓线和外形尺寸来快速预估非合作目标的功能及大小。建立双目视觉立体测量实验平台对一个立方体非合作目标模型进行测量,通过MATLAB软件平台对上述算法进行仿真验证,结果表明测量值与参考值的误差为百μm级。
The rapid and precise determination of the contour of non-cooperative space targets at a close range is essential for dynamic tracking and capturing. Based on the principle of the binocular stereo vision measurement, the spatial point cloud of the target is obtained for the non-cooperative targets with unknown shape. This paper proposes a direct extraction algorithm for deducing the point cloud contours, where a 3 D reduction is not required for defining the shape of a non-cooperative target, and the dimension measurement can be directly performed. This allows us to quickly predict the functionality and the size of the non-cooperative targets based on the outline and the dimension data. A test platform is established to perform the binocular stereo vision measurement of a model cube to verify the above-mentioned algorithm. The simulation validation by the MATLAB software shows that the error between the measured dimension value and the reference values is not more than 100 microns.
The rapid and precise determination of the contour of non-cooperative space targets at a close range is essential for dynamic tracking and capturing. Based on the principle of the binocular stereo vision measurement, the spatial point cloud of the target is obtained for the non-cooperative targets with unknown shape. This paper proposes a direct extraction algorithm for deducing the point cloud contours, where a 3 D reduction is not required for defining the shape of a non-cooperative target, and the dimension measurement can be directly performed. This allows us to quickly predict the functionality and the size of the non-cooperative targets based on the outline and the dimension data. A test platform is established to perform the binocular stereo vision measurement of a model cube to verify the above-mentioned algorithm. The simulation validation by the MATLAB software shows that the error between the measured dimension value and the reference values is not more than 100 microns.
引文
[1]于占海.基于点云的空间非合作目标双目视觉测量研究[D].哈尔滨:哈尔滨工业大学,2016:1
[2]徐培智.基于视觉和激光的空间非合作目标相对位姿测量技术研究[D].南京:南京航空航天大学,2014:1
[3]李艳.双目视觉中半全局匹配算法的硬件架构研究[D].合肥:中国科学技术大学,2017:12
[4]杨景豪,刘巍,刘阳.双目立体视觉测量系统的标定[J].光学精密工程,2016,24(2):300-308YANG J H,LIU W,LIU Y.Calibration of binocular stereo vision measurement system[J].Optical Precision Engineering,2016,24(2):300-308
[5]LUHMANN T,FRASER C,MAAS H G.Sensor modelling and camera calibration for close-range photogrammetry[J].Isprs Journal of Photogrammetry&Remote Sensing,2016,115:37-46
[6]ZHANG Z Y.A flexible new technique for camera calibration[J].IEEE Trans on Pattern Analysis and Machine Intelligence,2000,22(11):1330-1334
[7]宋海涛,何文浩,原魁.一种基于SIFT特征的机器人环境感知双目立体视觉系统[J].控制与决策,2018(6):1SONG H T,HE W H,YUAN K.A stereo vision system based on sift feature for robot environment perception[J].Control and Decision,2018(6):1
[8]殷志峰.散乱点云数据的三维重建方法研究[D].太原:太原理工大学,2015:10
[9]张俊勇.基于双目视觉的零件多尺寸在线测量方法研究[D].武汉:武汉科技大学,2018:44-45
[10]蔡湛,李如仁,李新科,等.一种基于激光点云数据的房屋轮廓线提取方法[J].地理与地理信息科学,2013,29(5):17-21CAI Z,LI R R,LI X K,et al.An extraction method of house contour based on laser point cloud is presented[J].Geography and Geographic Information Science,2013,29(5):17-21
[2]徐培智.基于视觉和激光的空间非合作目标相对位姿测量技术研究[D].南京:南京航空航天大学,2014:1
[3]李艳.双目视觉中半全局匹配算法的硬件架构研究[D].合肥:中国科学技术大学,2017:12
[4]杨景豪,刘巍,刘阳.双目立体视觉测量系统的标定[J].光学精密工程,2016,24(2):300-308YANG J H,LIU W,LIU Y.Calibration of binocular stereo vision measurement system[J].Optical Precision Engineering,2016,24(2):300-308
[5]LUHMANN T,FRASER C,MAAS H G.Sensor modelling and camera calibration for close-range photogrammetry[J].Isprs Journal of Photogrammetry&Remote Sensing,2016,115:37-46
[6]ZHANG Z Y.A flexible new technique for camera calibration[J].IEEE Trans on Pattern Analysis and Machine Intelligence,2000,22(11):1330-1334
[7]宋海涛,何文浩,原魁.一种基于SIFT特征的机器人环境感知双目立体视觉系统[J].控制与决策,2018(6):1SONG H T,HE W H,YUAN K.A stereo vision system based on sift feature for robot environment perception[J].Control and Decision,2018(6):1
[8]殷志峰.散乱点云数据的三维重建方法研究[D].太原:太原理工大学,2015:10
[9]张俊勇.基于双目视觉的零件多尺寸在线测量方法研究[D].武汉:武汉科技大学,2018:44-45
[10]蔡湛,李如仁,李新科,等.一种基于激光点云数据的房屋轮廓线提取方法[J].地理与地理信息科学,2013,29(5):17-21CAI Z,LI R R,LI X K,et al.An extraction method of house contour based on laser point cloud is presented[J].Geography and Geographic Information Science,2013,29(5):17-21