《PPT 英文presentation.ppt》由会员分享，可在线阅读，更多相关《PPT 英文presentation.ppt（12页珍藏版）》请在三一文库上搜索。
1、,Registration of Mobile LiDAR and Spherical Panoramas Shi Zhenwei,INTRODUCTION,Image-to-range registration is prerequisite for many applications. The registration result is critical not only for texture-mapping 3D models of large-scale scenes, but also for applications such as image based up samplin
2、g of range data, image-guided range segmentation, and 3D scene modeling. Providing valuable data for many applications such as street-scenes navigation and location based services.,CONTENTS,Data AcquisitionData segmentationData rarefyingLiDAR data projected to the panoramic image,Data AcquisitionSeg
3、mentationRarefying Projection,“Optech Lynx” mobile surveying system,Optechs Lynx Mobile Mapper represents the next generation in the rapid collection of survey-grade 3D data. This revolutionary mobile mapping system integrates the latest innovations in lidar sensors with best-in-class imaging, navig
4、ation, product warranty and support.,360mapping sensor with iFLEX technology,Full 360 mapping of highway features using MPL technology,LIDAR point cloud data,Video,Data AcquisitionSegmentationRarefying Projection,The test data were included residential, urban streets, and highway scenes. The data is
5、 in binary format containing around 2.24 GB LiDAR data (about 126 million points) and 97MB panoramic images (200 spherical panoramas). Because of large amount of point cloud data are not suitable for experiments, the point cloud data have to be segmented. For qualitative analysis, we selected 10 rep
6、resentative urban scenes for the evaluation using the two different representations of the LiDAR data described earlier.,Data AcquisitionSegmentationRarefying Projection,Higher density point cloud data are not suitable for direct use ,but also the data need to be rarefied. In this experiment, I have
7、 taken per 5 set of coordinates to extract one set of coordinates.,Data AcquisitionSegmentationRarefyingProjection,The method Under this camera model, a point in space is mapped to the point on the image plane by P = KRI-C. Where is the camera center, I is the identity matrix, and R is the camera ro
8、tation matrix. The matrix K is the camera calibration matrix. Each LiDAR point in LTP coordinates is converted tospherical coordinates by Equation ,where H and W are the height and width of the panoramic images respectively.,Data AcquisitionSegmentationRarefyingProjection,Wang R, Ferrie F P, Macfarl
9、ane J. Automatic registration of mobile LiDAR and spherical panoramasC/Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on. IEEE, 2012: 33-40.,Data AcquisitionSegmentationRarefyingProjection,a1=cos(k1)*cos(k3)-sin(k1)*sin(k2)*sin(k3);a2=-cos(k1)*sin(k3
10、)-sin(k1)*sin(k2)*cos(k3);a3=-sin(k1)*cos(k2);b1=cos(k2)*sin(k3);b2=cos(k2)*cos(k3);b3=-sin(k2);c1=sin(k1)*cos(k3)+cos(k1)*sin(k2)*sin(k3);c2=-sin(k1)*sin(k3)+cos(k1)*sin(k2)*cos(k3);c3=cos(k1)*cos(k2);,where k1,k2,k3 is the camera calibration, this three datas have been known.,is the camera rotatio
11、n matrix.,Photogrammetry by Wang Peijun, Xu yaming,Data AcquisitionSegmentationRarefyingProjection,The algorithms were implemented in C language programing in visual studio 2010 platform.,(a) Panoramic images,（b）LiDAR data projected to the panoramic imaging model,Data AcquisitionSegmentationRarefyingProjection,Thanks,