The iterative closest point(ICP)algorithm has the advantages of high accuracy and fast speed for point set registration,but it performs poorly when the point set has a large number of noisy outliers.To solve this prob...The iterative closest point(ICP)algorithm has the advantages of high accuracy and fast speed for point set registration,but it performs poorly when the point set has a large number of noisy outliers.To solve this problem,we propose a new affine registration algorithm based on correntropy which works well in the affine registration of point sets with outliers.Firstly,we substitute the traditional measure of least squares with a maximum correntropy criterion to build a new registration model,which can avoid the influence of outliers.To maximize the objective function,we then propose a robust affine ICP algorithm.At each iteration of this new algorithm,we set up the index mapping of two point sets according to the known transformation,and then compute the closed-form solution of the new transformation according to the known index mapping.Similar to the traditional ICP algorithm,our algorithm converges to a local maximum monotonously for any given initial value.Finally,the robustness and high efficiency of affine ICP algorithm based on correntropy are demonstrated by 2D and 3D point set registration experiments.展开更多
For reverse engineering a CAD model, it is necessary to integrate measured points from several views of an object into a common reference frame. Given a rough initial alignment of point cloud in different views with p...For reverse engineering a CAD model, it is necessary to integrate measured points from several views of an object into a common reference frame. Given a rough initial alignment of point cloud in different views with point-normal method, further refinement is achieved by using an improved iterative closest point (ICP) algorithm. Compared with other methods used for mult-view registration, this approach is automatic because no geometric feature, such as line, plane or sphere needs to be extracted from the original point cloud manually. A good initial alignment can be acquired automatically and the registration accuracy and efficiency is proven better than the normal point-point ICP algorithm both experimentally and theoretically.展开更多
As point cloud of one whole vehicle body has the traits of large geometric dimension,huge data and rigorous reverse precision,one pretreatment algorithm on automobile body point cloud is put forward.The basic idea of ...As point cloud of one whole vehicle body has the traits of large geometric dimension,huge data and rigorous reverse precision,one pretreatment algorithm on automobile body point cloud is put forward.The basic idea of the registration algorithm based on the skeleton points is to construct the skeleton points of the whole vehicle model and the mark points of the separate point cloud,to search the mapped relationship between skeleton points and mark points using congruence triangle method and to match the whole vehicle point cloud using the improved iterative closed point(ICP)algorithm.The data reduction algorithm,based on average square root of distance,condenses data by three steps,computing datasets'average square root of distance in sampling cube grid,sorting order according to the value computed from the first step,choosing sampling percentage.The accuracy of the two algorithms above is proved by a registration and reduction example of whole vehicle point cloud of a certain light truck.展开更多
In order to overcome the defects where the surface of the object lacks sufficient texture features and the algorithm cannot meet the real-time requirements of augmented reality,a markerless augmented reality tracking ...In order to overcome the defects where the surface of the object lacks sufficient texture features and the algorithm cannot meet the real-time requirements of augmented reality,a markerless augmented reality tracking registration method based on multimodal template matching and point clouds is proposed.The method first adapts the linear parallel multi-modal LineMod template matching method with scale invariance to identify the texture-less target and obtain the reference image as the key frame that is most similar to the current perspective.Then,we can obtain the initial pose of the camera and solve the problem of re-initialization because of tracking registration interruption.A point cloud-based method is used to calculate the precise pose of the camera in real time.In order to solve the problem that the traditional iterative closest point(ICP)algorithm cannot meet the real-time requirements of the system,Kdtree(k-dimensional tree)is used under the graphics processing unit(GPU)to replace the part of finding the nearest points in the original ICP algorithm to improve the speed of tracking registration.At the same time,the random sample consensus(RANSAC)algorithm is used to remove the error point pairs to improve the accuracy of the algorithm.The results show that the proposed tracking registration method has good real-time performance and robustness.展开更多
Airborne laser scanning(ALS)and terrestrial laser scanning(TLS)has attracted attention due to their forest parameter investigation and research applications.ALS is limited to obtaining fi ne structure information belo...Airborne laser scanning(ALS)and terrestrial laser scanning(TLS)has attracted attention due to their forest parameter investigation and research applications.ALS is limited to obtaining fi ne structure information below the forest canopy due to the occlusion of trees in natural forests.In contrast,TLS is unable to gather fi ne structure information about the upper canopy.To address the problem of incomplete acquisition of natural forest point cloud data by ALS and TLS on a single platform,this study proposes data registration without control points.The ALS and TLS original data were cropped according to sample plot size,and the ALS point cloud data was converted into relative coordinates with the center of the cropped data as the origin.The same feature point pairs of the ALS and TLS point cloud data were then selected to register the point cloud data.The initial registered point cloud data was fi nely and optimally registered via the iterative closest point(ICP)algorithm.The results show that the proposed method achieved highprecision registration of ALS and TLS point cloud data from two natural forest plots of Pinus yunnanensis Franch.and Picea asperata Mast.which included diff erent species and environments.An average registration accuracy of 0.06 m and 0.09 m were obtained for P.yunnanensis and P.asperata,respectively.展开更多
In order to obtain and master the surface thermal deformation of paraboloid antennas,a fast iterative closest point( FICP) algorithm based on design coordinate guidance is proposed,which can satisfy the demands of rap...In order to obtain and master the surface thermal deformation of paraboloid antennas,a fast iterative closest point( FICP) algorithm based on design coordinate guidance is proposed,which can satisfy the demands of rapid detection for surface thermal deformation. Firstly,the basic principle of the ICP algorithm for registration of a free surface is given,and the shortcomings of the ICP algorithm in the registration of surface are analysed,such as its complex computation,long calculation time,low efficiency,and relatively strict initial registration position. Then an improved FICP algorithm based on design coordinate guidance is proposed. Finally,the FICP algorithm is applied to the fast registration test for the surface thermal deformation of a paraboloid antenna. Results indicate that the approach offers better performance with regard to fast surface registration and the algorithm is more simple,efficient,and easily realized in practical engineering application.展开更多
提出了一种分层块状全局搜索到临近点局部搜索的改进迭代最近点(ICP)算法,用于进一步提高ICP算法的配准速度并消除点云缺失对点云配准的影响。该配准方法在粗略配准之后,以点云块为分层单元对模型点集进行选取,并对选取的少量模型点进...提出了一种分层块状全局搜索到临近点局部搜索的改进迭代最近点(ICP)算法,用于进一步提高ICP算法的配准速度并消除点云缺失对点云配准的影响。该配准方法在粗略配准之后,以点云块为分层单元对模型点集进行选取,并对选取的少量模型点进行全局搜索获取其对应最近点;然后,以这些模型点对应的最近点作为搜索中心,在场景点集中进行局部搜索,获取这些模型点的大量临近点的对应最近点;最后,剔除错误对应最近点对,并求取坐标变换。与基于KD-Tree的ICP算法和基于LS+HS(Logarithmic Search Combined with Hierarchical Model Point Selection)的ICP算法相比,该配准算法对Happy bunny扫描数据的配准速度分别提高了78%和24%;对Dragon扫描数据的配准速度分别提高了73%和30%。这些结果表明该算法可以快速、精确地实现三维点云间的配准。展开更多
基金supported in part by the National Natural Science Foundation of China(61627811,61573274,61673126,U1701261)
文摘The iterative closest point(ICP)algorithm has the advantages of high accuracy and fast speed for point set registration,but it performs poorly when the point set has a large number of noisy outliers.To solve this problem,we propose a new affine registration algorithm based on correntropy which works well in the affine registration of point sets with outliers.Firstly,we substitute the traditional measure of least squares with a maximum correntropy criterion to build a new registration model,which can avoid the influence of outliers.To maximize the objective function,we then propose a robust affine ICP algorithm.At each iteration of this new algorithm,we set up the index mapping of two point sets according to the known transformation,and then compute the closed-form solution of the new transformation according to the known index mapping.Similar to the traditional ICP algorithm,our algorithm converges to a local maximum monotonously for any given initial value.Finally,the robustness and high efficiency of affine ICP algorithm based on correntropy are demonstrated by 2D and 3D point set registration experiments.
基金the National Natural Science Foundation of China (59990470) and the NationalOutstanding Young Scientist Foundation of China (
文摘For reverse engineering a CAD model, it is necessary to integrate measured points from several views of an object into a common reference frame. Given a rough initial alignment of point cloud in different views with point-normal method, further refinement is achieved by using an improved iterative closest point (ICP) algorithm. Compared with other methods used for mult-view registration, this approach is automatic because no geometric feature, such as line, plane or sphere needs to be extracted from the original point cloud manually. A good initial alignment can be acquired automatically and the registration accuracy and efficiency is proven better than the normal point-point ICP algorithm both experimentally and theoretically.
基金This project is supported by Provincial Technology Cooperation Program of Yunnan,China(No.2003EAAAA00D043).
文摘As point cloud of one whole vehicle body has the traits of large geometric dimension,huge data and rigorous reverse precision,one pretreatment algorithm on automobile body point cloud is put forward.The basic idea of the registration algorithm based on the skeleton points is to construct the skeleton points of the whole vehicle model and the mark points of the separate point cloud,to search the mapped relationship between skeleton points and mark points using congruence triangle method and to match the whole vehicle point cloud using the improved iterative closed point(ICP)algorithm.The data reduction algorithm,based on average square root of distance,condenses data by three steps,computing datasets'average square root of distance in sampling cube grid,sorting order according to the value computed from the first step,choosing sampling percentage.The accuracy of the two algorithms above is proved by a registration and reduction example of whole vehicle point cloud of a certain light truck.
基金This work was supported by National Natural Science Foundation of China(No.61125101).
文摘In order to overcome the defects where the surface of the object lacks sufficient texture features and the algorithm cannot meet the real-time requirements of augmented reality,a markerless augmented reality tracking registration method based on multimodal template matching and point clouds is proposed.The method first adapts the linear parallel multi-modal LineMod template matching method with scale invariance to identify the texture-less target and obtain the reference image as the key frame that is most similar to the current perspective.Then,we can obtain the initial pose of the camera and solve the problem of re-initialization because of tracking registration interruption.A point cloud-based method is used to calculate the precise pose of the camera in real time.In order to solve the problem that the traditional iterative closest point(ICP)algorithm cannot meet the real-time requirements of the system,Kdtree(k-dimensional tree)is used under the graphics processing unit(GPU)to replace the part of finding the nearest points in the original ICP algorithm to improve the speed of tracking registration.At the same time,the random sample consensus(RANSAC)algorithm is used to remove the error point pairs to improve the accuracy of the algorithm.The results show that the proposed tracking registration method has good real-time performance and robustness.
基金supported by the National Natural Science Foundation of China,Grant Number 41961060by the Program for Innovative Research Team (in Science and Technology) in the University of Yunnan Province,Grant Number IRTSTYN+1 种基金by the Scientific Research Fund Project of the Education Department of Yunnan Province,Grant Numbers 2020J0256 and 2021J0438by the Postgraduate Scientific Research and Innovation Fund Project of Yunnan Normal University,Grant Number YJSJJ21-A08
文摘Airborne laser scanning(ALS)and terrestrial laser scanning(TLS)has attracted attention due to their forest parameter investigation and research applications.ALS is limited to obtaining fi ne structure information below the forest canopy due to the occlusion of trees in natural forests.In contrast,TLS is unable to gather fi ne structure information about the upper canopy.To address the problem of incomplete acquisition of natural forest point cloud data by ALS and TLS on a single platform,this study proposes data registration without control points.The ALS and TLS original data were cropped according to sample plot size,and the ALS point cloud data was converted into relative coordinates with the center of the cropped data as the origin.The same feature point pairs of the ALS and TLS point cloud data were then selected to register the point cloud data.The initial registered point cloud data was fi nely and optimally registered via the iterative closest point(ICP)algorithm.The results show that the proposed method achieved highprecision registration of ALS and TLS point cloud data from two natural forest plots of Pinus yunnanensis Franch.and Picea asperata Mast.which included diff erent species and environments.An average registration accuracy of 0.06 m and 0.09 m were obtained for P.yunnanensis and P.asperata,respectively.
基金Supported by the National Natural Science Foundation of China(No.51474217,41501562)the Open Fund Program of Henan Engineering Laboratory of Pollution Control and Coal Chemical Resources Comprehensive Utilization(No.502002-B07,502002-A04)
文摘In order to obtain and master the surface thermal deformation of paraboloid antennas,a fast iterative closest point( FICP) algorithm based on design coordinate guidance is proposed,which can satisfy the demands of rapid detection for surface thermal deformation. Firstly,the basic principle of the ICP algorithm for registration of a free surface is given,and the shortcomings of the ICP algorithm in the registration of surface are analysed,such as its complex computation,long calculation time,low efficiency,and relatively strict initial registration position. Then an improved FICP algorithm based on design coordinate guidance is proposed. Finally,the FICP algorithm is applied to the fast registration test for the surface thermal deformation of a paraboloid antenna. Results indicate that the approach offers better performance with regard to fast surface registration and the algorithm is more simple,efficient,and easily realized in practical engineering application.
文摘提出了一种分层块状全局搜索到临近点局部搜索的改进迭代最近点(ICP)算法,用于进一步提高ICP算法的配准速度并消除点云缺失对点云配准的影响。该配准方法在粗略配准之后,以点云块为分层单元对模型点集进行选取,并对选取的少量模型点进行全局搜索获取其对应最近点;然后,以这些模型点对应的最近点作为搜索中心,在场景点集中进行局部搜索,获取这些模型点的大量临近点的对应最近点;最后,剔除错误对应最近点对,并求取坐标变换。与基于KD-Tree的ICP算法和基于LS+HS(Logarithmic Search Combined with Hierarchical Model Point Selection)的ICP算法相比,该配准算法对Happy bunny扫描数据的配准速度分别提高了78%和24%;对Dragon扫描数据的配准速度分别提高了73%和30%。这些结果表明该算法可以快速、精确地实现三维点云间的配准。
