结合图论的异源影像点云配准方法

A Cross-Source Image Point Cloud Registration Method Combined with Graph Theory

结合图论思想,提出一种高效的异源影像点云配准方法。该方法首先利用点云几何特征寻找点云中的地平面方向,将点云中建筑物的布局关系构建成图形式,使点云配准问题转化为图匹配问题;然后,提出一种图匹配方法,基于几何约束条件构建核三角形作为配准基元,利用高阶相似度信息寻找图的全局最优匹配,实现点云间的快速、稳健初配准;最后,结合迭代最近点(ICP)算法进行精配准,获得高精度异源点云配准结果。为了验证所提方法的有效性,选取河南省3个不同区域的高分七号卫星影像点云和无人机近景影像点云进行实验。实验结果表明,所提方法不受噪声点和异常值的影响,能够克服不同的点云密度差异、消除约939倍的坐标尺度差异,整体配准速度相较于对比方法提升了51~184倍,全自动地实现了异源影像点云鲁棒、高效配准。

Objective With the development of optical photogrammetry technologies,there are more and more means to perceive threedimensional(3D)point clouds that describe the same object or scene.Through satellite photogrammetry,we can quickly obtain dense urban point clouds in a wide area,but the surface information of the target is not clear because the sensor is too far away,and even the data collected by the closerange platform has fine structure and texture information.However,when there is no precise positioning system or absolute control points,the generated point cloud is in an arbitrary model coordinate system.The rapid and highprecision registration between largescale point clouds of closerange images and point clouds of satellite images has great potential for applications such as smart city construction,disaster relief,and emergency response.However,there are many problems in this task,which makes it difficult to achieve efficient registration between the two.For example,the resolution of satellite images and closerange images is different,which leads to a large difference in the point density between the two point clouds.As the sensor's line of sight is blocked,there are many holes in point clouds of images.The scale difference in the coordinate system between the closerange point cloud and the satellite point cloud is arbitrary.The image point cloud contains a large number of noise points and outliers because of defects in the dense imagematching algorithm.To this end,an efficient crosssource image point cloud registration method is proposed on the basis of graph theory,which is automatic,fast,and robust.It is believed that the proposed basic registration strategy and graphmatching method can be helpful for the data fusion and reconstruction of largescale satellite image point clouds and closerange image point clouds.Methods First of all,the ground plane direction in the point cloud is found through the geometric features of the point cloud.The rotation angle of the planar normal vector with respect to the vertical direction of the satellite point cloud is calculated so that the closerange point cloud is roughly aligned with the satellite point cloud on the ground.Then,the centers of the buildings are taken as the nodes,and the layout relationship of buildings in the point cloud is constructed into a graph,which transforms the point cloud registration problem into a graphmatching problem.Afterward,kernel triangles are constructed according to geometric constraints as registration primitives,and higherorder similarity information is used to find the global optimal match of graphs.Finally,the ICP algorithm is adopted for fine registration to obtain highprecision and crosssource point cloud registration results.Results and Discussions The point cloud of the Gaofen7 satellite images and the point cloud of UAV closerange images in three regions of Henan Province are selected for experiments to verify the effectiveness of the proposed method.There are 22 pairs,13 pairs,and 11 pairs of nodes that are matched in the three experiments(Fig.3).In the three experiments with different numbers of holes and noise points,the graph with higherorder similarity information can accurately obtain a sufficient number of matching nodes,overcoming the density and scale differences.Upon the application of the ICP algorithm,the integrated point clouds are obtained,which not only show rich geometric structure and texture details but also have real geographic coordinates(Fig.4).The coarse registration algorithm based on graph matching enables the ICP algorithm to avoid falling into a local optimal solution,which has good registration accuracy on three datasets of different scales,densities,and noise.The rootmeansquare errors of the three experiments are only 5.16 m,6.39 m,and 9.02 m(Table 2).Finally,the existing four algorithms are used to register three experimental datasets in this paper(Fig.5)for the performance comparison with the proposed registration method.The experimental results show that the proposed method is independent of noise points and outliers.It can overcome the density differences of different point clouds and eliminate coordinate scale differences of about 939 times.The overall registration speed is improved by a factor of 51-184 compared to that of the comparison methods,and the proposed method is automatic,robust,and efficient.Conclusions This paper mainly studies the scale differences,density differences,noise points,and outlier problems in the registration of satelliteimage point clouds and closerange image point clouds.A novel point cloud registration method is proposed,which transforms the point cloud registration problem into a graphmatching problem according to graph theory.The centers of buildings are taken as the nodes,and the layout relationship of buildings in the point cloud is constructed into a graph.Kernel triangles are constructed pursuant to geometric constraints as registration primitives.Then,a graphmatching method using the higherorder similarity information of the graph is presented to obtain the spatial transformation model,and the ICP algorithm is used for fine registration.Finally,experiments are conducted on highresolution satelliteimage point clouds and closerange image point clouds in three different regions of Henan Province.The closerange point cloud containing structure and texture details is successfully converted to the spatial coordinate system of the satellite point cloud,and a refined 3D point cloud with real geographic coordinates is obtained.Multiple sets of experimental data show that the proposed method can robustly and quickly register crosssource image point clouds in contrast with other methods.