城市环境下基于C/S架构的影像空间定位
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摘要
针对城市环境下影像空间定位精度低等问题,提出了一种在C/S架构下基于影像的移动平台空间自定位方法。首先对预采集的城市建筑物序列影像,利用最近邻距离比率(nearest neighbor distance ratio,NNDR)算法和归一化互相关匹配(normalized cross correlation,NCC)算法得到SIFT粗匹配,通过随机抽样一致(random sample consensus,RANSAC)算法对粗匹配点进行优化,通过精确解算基本矩阵F和投影矩阵P,建立建筑物三维点云模型,进而获得由影像特征点、像点坐标以及物方点坐标组成的物方特征库。其次以用户通过手机拍摄的影像作为定位影像,进行特征提取并与物方特征库影像匹配,获取对应物方点坐标。最后通过精确计算定位影像外方位元素,并在手机客户端中显示所拍照瞬间手机的空间位置,实现移动平台空间自定位。实验结果表明,该方法能够达到厘米级定位精度,可作为其他空间定位方法的有效补充。
Aiming at the low positioning accuracy of image space location in urban environments,this paper proposes a new method of space location of image in urban environments based on C/S structure. First,a series of indoor images are taken in advance. We use the nearest neighbor distance ratio( NNDR) and normalized cross correlation( NCC) to get the SIFT coarse matches,then use RANSAC method to optimize it,and calculate the fundamental matrix and projection matrix. Building 3D point cloud model is established,and the object space feature library including image feature points,image point coordinates and object space point coordinates are got. Second,we regard the building images which are taken by user's mobile phone as the location images. Image feature points of location images are extracted and matched with the feature points of object space feature library,then the object space point coordinates are obtained. Finally,we use collinearity equation model to accurately calculate the exterior orientation elements,and display the user's space position on the phone,thus space positioning is realized. The experimental results show that the positioning method in this paper can reach the centimeter level positioning accuracy,and can meet the requirements of the user positioning accuracy.
Aiming at the low positioning accuracy of image space location in urban environments,this paper proposes a new method of space location of image in urban environments based on C/S structure. First,a series of indoor images are taken in advance. We use the nearest neighbor distance ratio( NNDR) and normalized cross correlation( NCC) to get the SIFT coarse matches,then use RANSAC method to optimize it,and calculate the fundamental matrix and projection matrix. Building 3D point cloud model is established,and the object space feature library including image feature points,image point coordinates and object space point coordinates are got. Second,we regard the building images which are taken by user's mobile phone as the location images. Image feature points of location images are extracted and matched with the feature points of object space feature library,then the object space point coordinates are obtained. Finally,we use collinearity equation model to accurately calculate the exterior orientation elements,and display the user's space position on the phone,thus space positioning is realized. The experimental results show that the positioning method in this paper can reach the centimeter level positioning accuracy,and can meet the requirements of the user positioning accuracy.
引文
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[2]Lu Y H,Delp E J.An Overview of Problems in ImageBased Location Awareness and Navigation[C].SPIE Conference on Electronic Imaging,San Jose,California,USA,2004
[3]Zhang W,Kosecka J.Image Based Localization in Urban Environments[C].International Symposium on 3D Data Processing,Visualization and Transmission,Chapel Hill,USA,2006
[4]Feng Chun.Research on Identification and Location of Object Based on Monocular Vision[D].Nanjing:University of Aeronautics and Astronautics,2013(冯春.基于单目视觉的目标识别与定位研究[D].南京:南京航空航天大学,2013)
[5]Li Haifeng.Research on Visual Localization for Mobile Robot in Urban Environment[D].Tianjin:Nankai University,2012(李海丰.城市环境中移动机器人视觉定位研究[D].天津:南开大学,2012)
[6]Wang Baofeng,Zhou Jianliang,Tang Geshi,et al.Research on Visual Localization Method of Lunar Rover[J].Science China:Information Sciences,2014,44(4):452-460(王保丰,周建亮,唐歌实,等.嫦娥三号巡视器视觉定位方法[J].中国科学:信息科学,2014,44(4):452-460)
[7]Dou Yanyan.Research of 3D Reconstruction Based on Image Sequence[D].Nanjing:Nanjing University of Information Science and Technology,2013(窦艳艳.基于图像序列三维重建相关技术研究[D].南京:南京信息工程大学,2013)
[8]Xiao Xiongwu,Guo Bingxuan,Li Deren,et al.A Quick and Affine Invariance Matching Method for Oblique Images[J].Acta Geodaetica et Cartographica Sinica,2015,44(4):414-421(肖雄武,郭丙轩,李德仁,等.一种具有仿射不变性的倾斜影像快速匹配方法[J].测绘学报,2015,44(4):414-421)
[9]David G L.Distinctive Image Features from Scale-Invariant Keypoints[J].International Journal of Computer Vision,2004,60(2):91-110
[10]Klippenstein J,Zhang H.Quantitative Evaluation of Feature Extractors for Visual SLAM[C].The 4th Canadian Conference on Computer and Robot Vision,Montreal,Quebec,Canada,2007
[11]Hartley R,Zisserman A.Multiple View Geometry in Computer Vision[M].Cambridge:Cambridge University Press,2000