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公开(公告)号：US11556732B2
公开(公告)日：2023-01-17
申请号：US17169509
申请日：2021-02-07
摘要： A method for extracting rivet points in large scale three-dimensional point cloud based on deep learning is provided. Geometric attribute scalar of a point cloud of aircraft skin is calculated point by point, and the scalar attribute domain is mapped to the two-dimensional image to obtain a two-dimensional attribute scalar map of the point cloud. The 2D attribute scalar map is processed using a convolutional neural network and the probability that each point belongs to a rivet point is calculated. The rivet point cloud is divided through a threshold according to the probability; and the point clouds belonging to a same rivet is clustered from the divided rivet point cloud using Euclidean cluster.
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公开(公告)号：US11504731B2
公开(公告)日：2022-11-22
申请号：US17169538
申请日：2021-02-07
发明人： Jun Wang , Jun Zhou , Yuanpeng Liu , Qian Xie
IPC分类号： G06K9/00 , B05B12/08 , G06T7/521 , G06T7/73 , B05B12/00 , G05B19/4097 , G06T7/00 , G06V10/40
摘要： A method for automatic glue-spraying of stringers and inspection of glue-spraying quality based on measured data. Three-dimensional (3D) point cloud data of a stringer-skin assembly is collected by 3D laser scanner, and then processed by denoising and sampling. Feature points of an intersection line of a site to be glued of the stringer-skin assembly are extracted by K-means clustering method based on Gaussian mapping, and a minimum spanning tree is constructed based on a set of the extracted feature points. A connected region is established to obtain an initial feature intersection line of the string-skin assembly, which is optimized by random sample consensus algorithm to remove redundant small branch structures to obtain the actual glue-spraying trajectory. The quality of the glue sprayed on the stringer-skin assembly is inspected by line laser to determine positions of the defects, which are then subjected to secondary glue-spraying.
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公开(公告)号：US11557029B2
公开(公告)日：2023-01-17
申请号：US17574827
申请日：2022-01-13
发明人： Zhongde Shan , Jun Wang , Anyi Huang , Qian Xie
IPC分类号： G06T7/00 , G06T7/50 , G06T7/11 , G06V10/77 , G06V10/774 , G06V20/70 , G06T7/30 , G06V10/776 , G06V10/25 , G06V10/26
摘要： A method for detecting and recognizing surface defects of an automated fiber placement composite based on an image converted from point clouds, including: acquiring a surface point cloud of the automated fiber placement composite; fitting a plane to surface point data; calculating a distance from each point of the surface point cloud to a fitted plane; enveloping the surface point cloud by OBB, and generating a grayscale image according to the OBB and the distance; constructing a pre-trained semantic segmentation network for defect of fiber placement, and inputting the grayscale image to segment and recognize defect areas thereon; mapping a segmentation result output by the semantic segmentation network to the point cloud followed by defect evaluation and visualization.
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公开(公告)号：US11532123B2
公开(公告)日：2022-12-20
申请号：US17574796
申请日：2022-01-13
发明人： Jun Wang , Zikuan Li , Anyi Huang , Qian Xie
IPC分类号： G06T17/00
摘要： A method for visualizing a large-scale point cloud based on normal, including: (S1) according to a spatial structure of a point cloud data, constructing a balanced octree structure of a node point cloud; (S2) according to the balanced octree structure and normal information of a point cloud, constructing an octree structure with the normal information; and constructing a normal level-of-detail (LOD) visualization node through downsampling; and (S3) determining a node scheduling strategy according to a relationship between a viewpoint, a viewing frustum and a normal of a render node; and respectively calling a reading thread and a rendering thread to simultaneously perform reading and rendering according to the node scheduling strategy.
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5.发明授权公开(公告)号：US11037346B1
公开(公告)日：2021-06-15
申请号：US17026260
申请日：2020-09-20
发明人： Jun Wang , Yan Wang , Yuanpeng Liu , Qian Xie
摘要： Disclosed a multi-station scanning global point cloud registration method based on graph optimization, including acquiring multi-station original three-dimensional point cloud data; based on initial registration of targets, completing initial registration of point cloud data at adjacent stations by virtue of the target at each angle of view; calculating a point cloud overlap area at adjacent angles of view, and calculating areas of overlap regions of adjacent point cloud by a gridded sampling method; constructing a fine registration graph structure, and constructing a fine registration graph by taking point cloud data of each station as a node of the graph and taking an overlap area of the point cloud data of adjacent stations as a side of adjacent nodes of the graph structure; and based on loop closure fine registration based on graph optimization, gradually completing point cloud fine registration of the whole aircraft according to a specific closure sequence.
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公开(公告)号：US11544837B2
公开(公告)日：2023-01-03
申请号：US17574843
申请日：2022-01-13
发明人： Zhongde Shan , Jun Wang , Zhengyuan Wei , Honghua Chen , Qian Xie
IPC分类号： G06K9/00 , G06T7/00 , G06T7/55 , G06T7/70 , G06V10/74 , G06V10/75 , G06V10/24 , G06V10/10 , G01B11/16 , G01B11/00
摘要： A method for dynamically measuring deformation of a rotating-body mold, including: (S1) subjecting an overall outer surface of the rotating-body mold to three-dimensional measurement to acquire an initial point cloud data; (S2) shooting, by a multi-camera system, the mold from different angles to obtain three-dimensional coordinates of marking points and coding points on the overall outer surface of the rotating-body mold; (S3) rotating the mold, and repeatedly photographing the marking points and the coding points on the mold surface under different angle poses; and calculating three-dimensional coordinates of the marking points and the coding points; and (S4) predicting a point cloud data of the outer surface under different angle poses based on a conversion relationship among the marking points to analyze a deformation degree of the mold during a rotation process.
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公开(公告)号：US11538181B2
公开(公告)日：2022-12-27
申请号：US17026206
申请日：2020-09-19
发明人： Jun Wang , Qian Xie , Dening Lu , Yuan Zhang
摘要： A method for automated flushness measurement of point cloud rivets, including: extracting a rivet outline by adopting an RANSAC circle fitting algorithm, and determining a center, a radius and a normal vector of an outline circle; extracting point cloud of a rivet head for a single rivet outline; extracting point cloud around the rivet for the single rivet outline; and generating a distance color difference map reflecting rivet flushness according to the point cloud of the rivet head and the point cloud around the rivet. According to the present invention, the point cloud of the rivet head and the point cloud around the rivet can be respectively extracted, and the distance color difference map reflecting the rivet flushness is generated according to the point cloud of the rivet head and the point cloud around the rivet, so that the rivet flushness is rapidly and effectively measured.
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公开(公告)号：US11532121B2
公开(公告)日：2022-12-20
申请号：US17169527
申请日：2021-02-07
摘要： A method for measuring a seam on aircraft skin based on a large-scale point cloud is disclosed. A point cloud density of each point in an aircraft skin point cloud is calculated. Seam and non-seam point clouds are divided according to a discrepancy of the calculated point cloud density. A point is selected from the point cloud of the seam area, and a section at the point is extracted. A certain range of the seam and non-seam point clouds is projected to the section and a projected point cloud is acquired. A calculation model of flush and gap is constructed, and the flush and the gap of the aircraft skin seam at the measuring point is calculated according to the projected point cloud and the calculation model.
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公开(公告)号：US11308632B2
公开(公告)日：2022-04-19
申请号：US17169513
申请日：2021-02-07
发明人： Jun Wang , Zeyong Wei , Qian Xie
摘要： Disclosed herein is a code point-driven three-dimensional (3D) point cloud deformation method. In the method, movable code points and fixed code points are respectively pasted on a moving structure and a static structure. Reference poses of the movable code points and fixed code points are obtained by a dual-camera measurement system, and a 3D point cloud reference model containing the moving structure and the static structure is obtained by 3D laser scanning. A transformation matrix of each code point is calculated, and a real-time point cloud model is established based on the transformation matrix to complete the real-time and dynamic measurement of the moving structure.
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