Abstract: A method for automatically processing a structure-reinforcing member of an aircraft, including: (S1) acquiring, by a handheld laser scanner, data of an area to be reinforced of the aircraft; (S2) controlling a robotic arm to automatically grab the reinforcing member for automatic scanning; (S3) setting a cutting path in a computer aided design (CAD) digital model followed by registration with real data to obtain an actual cutting path, and cutting the reinforcing member; (S4) controlling the robotic arm to guide a cut reinforcing member to a scanning area for automatic scanning; and (S5) subjecting point cloud data of the cut reinforcing member and the area to be reinforced to virtual assembly and calculating a machining allowance to determine whether an accuracy requirement is met; if yes, ending a task; otherwise, grinding the reinforcing member automatically, and repeating steps (S4)-(S5).

Abstract: The present disclosure provides a point cloud denoising method based on multi-level attention perception, including the following steps: constructing a data set of point cloud denoising; constructing a point cloud denoising neural network, including a patch feature encoder, a global level perception module, a global level attention module, and a multi-offset decoder module, and training a network model by using the data set of point cloud denoising; for input point cloud, separately obtaining a neighborhood patch of a point of each original data point, and inputting coordinates of each data point in the neighborhood patch of a point to a trained denoising neural network to obtain a location offset of each original point; and separately adjusting, based on the obtained location offset, a location corresponding to each original data point in the input point cloud, to complete point cloud denoising.

Abstract: A method for automatically processing a structure-reinforcing member of an aircraft, including: (S1) acquiring, by a handheld laser scanner, data of an area to be reinforced of the aircraft; (S2) controlling a robotic arm to automatically grab the reinforcing member for automatic scanning; (S3) setting a cutting path in a computer aided design (CAD) digital model followed by registration with real data to obtain an actual cutting path, and cutting the reinforcing member; (S4) controlling the robotic arm to guide a cut reinforcing member to a scanning area for automatic scanning; and (S5) subjecting point cloud data of the cut reinforcing member and the area to be reinforced to virtual assembly and calculating a machining allowance to determine whether an accuracy requirement is met; if yes, ending a task; otherwise, grinding the reinforcing member automatically, and repeating steps (S4)-(S5).

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.