Nanjing University of Aeronautics and Astronautics

Dening Lu

Jun Wang

Qian Xie

Kun Long

Yuan Zhang

Dawei Li

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.

Method for extracting rivet points in large scale three-dimensional point cloud base on deep learning

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.

Method for automated flushness measurement of point cloud rivets

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.

Method for measuring a seam on aircraft skin based on large-scale point cloud

The present disclosure provides a point cloud denoising method based on deep learning for an aircraft part, in which different degrees of Gaussian noise are added based on a theoretical data model of the aircraft part, a heightmap for each point in the theoretical data model is generated, and a deep learning training set is constructed. A deep learning network is trained based on the constructed deep learning training set, to obtain a deep learning network model. A real aircraft part is scanned via a laser scanner to obtain measured point cloud data. The normal information of the measured point cloud is predicted based on the trained deep learning network model. Based on the predicted normal information, a position of each point in the measured point cloud data is further updated, thereby completing denoising of the measured point cloud data.

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Jiangsu, China

Dening Lu