A point cloud segmentation method for narrow facets of micro parts based on local features

Abstract

The present invention relates to the field of precision measurement of complex parts, and more specifically, to a method for point cloud segmentation at the narrow edges of micro-parts based on local features. The method comprises the following steps: obtaining the original point cloud data of the part and preprocessing it; using a region growing algorithm based on seed point selection on the preprocessed data to obtain the large area surface of the part excluding the narrow edges; aligning the part model with the segmented large area to obtain the point cloud interval range of the narrow edges of the part adjacent to the large area; restoring the point cloud data within the narrow edge point cloud interval range and calculating the three-dimensional point cloud curvature; constructing a reference point sequence based on the three-dimensional point cloud curvature, and judging whether the sequence can represent the narrow edge plane based on the curvature and normal of the narrow edge; using the finally obtained reference points to obtain the narrow edge and complete the point cloud segmentation. The present invention does not require precise registration and annotation of point cloud data, reduces the need for prior knowledge, improves the degree of automation of point cloud segmentation, and has strong engineering application and promotion value.

Description

Technical Field

[0001] The present invention relates to the field of precision measurement of complex parts, and in particular to a point cloud segmentation method for narrow facets of micro parts based on local features. Background Art

[0002] In actual engineering applications, many micro-complex structural parts are core components. These parts are characterized by small size, precise structure, high technical requirements, and complex manufacturing processes. Their key geometric features, such as the machining accuracy of narrow facets, are crucial to the overall system. Traditional geometric measurement methods for mechanical parts are unable to measure micro-complex structures. Digital measurement technology can discretize the surface geometry of the part to be measured and convert it into a point cloud model. Point cloud processing algorithms can then be used to analyze the dimensional and positional errors of the workpiece.

[0003] Point cloud segmentation is a key step in the point cloud data processing process, and the quality of the segmentation results has a great impact on the subsequent steps. Commonly used point cloud segmentation algorithms can be roughly divided into the following four types: edge-based, region-growing, model geometry-based, and deep learning-based point cloud segmentation algorithms. Edge-based and region-growing methods are suitable for instances where the geometric features or normal vector features of point cloud data have obvious differences and changes. The number of point clouds on the narrow edges of micro parts is small, and they are affected by edge measurement noise. Automatic extraction cannot be achieved based on the above segmentation methods; the point cloud segmentation method based on model geometry elements requires first aligning the point cloud data with the model, and then calculating the distance from each point cloud data to each surface element for classification. Although this segmentation method has obvious basis, it has problems of low accuracy and under-segmentation when segmenting the complex structure of the narrow edges of parts; the point cloud segmentation method based on deep learning requires the use of a large amount of labeled data and needs to be trained according to different scenarios. It is highly complex and takes a long time to calculate, making it unsuitable for engineering applications. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the present invention provides a point cloud segmentation method for narrow facets of micro-complex structure parts based on local features, which can automatically and accurately segment and extract point cloud data at the narrow facets of micro-parts.

[0005] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:

[0006] A point cloud segmentation method for narrow facets of micro parts based on local features includes the following steps:

[0007] 1) Obtain the original point cloud data of the part and preprocess it;

[0008] 2) Using the region growing algorithm based on seed point selection on the preprocessed data to obtain the large area surface of the part except for the narrow edge surface;

[0009] 3) Align the part model with the segmented large area to obtain the point cloud range of the narrow facets of the part adjacent to the large area;

[0010] 4) Restore the point cloud data within the narrow facet point cloud range and calculate the 3D point cloud curvature;

[0011] 5) Based on the curvature of the 3D point cloud, a reference point sequence is constructed, and based on the curvature and normal of the narrow facet, whether the sequence can represent the narrow facet plane is determined;

[0012] 6) Using the final reference points, find the narrow facets and complete the point cloud segmentation.

[0013] The step 1) is specifically as follows:

[0014] The original point cloud data is converted into voxel data through an octree-based voxelization process and then downsampled.

[0015] The step 2) is specifically as follows:

[0016] Calculate the curvature of the point cloud data of multiple points close to the center point in each voxel, sort the curvatures, select the point with the smallest curvature as the seed point, use the K-nearest neighbor or fixed radius search method to search for its neighboring points, and use the growth criterion to compare the neighboring points one by one. If the growth criterion is met, the current neighboring point is added to the same block as the seed point, and if the curvature value of the neighboring point is less than the set curvature threshold, it is added to the current seed point queue; if the growth criterion is not met, continue to the next neighboring point judgment.

[0017] The comparison is performed using the growth criteria for each neighboring point, specifically, determining whether the normal angle between the seed point and the neighboring point is less than a set threshold.

[0018] The step 4) is specifically as follows:

[0019] By using the large area surface and part model that have been segmented and are adjacent to the narrow facet, the normal of the plane where the narrow facet is located is obtained, and the narrow facet point cloud interval is discretized along the normal direction.

[0020] The step 5) is specifically as follows:

[0021] Calculate the curvature of the 3D point cloud in each discretization interval, select the point with the smallest curvature as the initial reference point ki, and put it into the initial reference point sequence K in sequence;

[0022] Using the large area surface and part model adjacent to the narrow facet that have been segmented, the plane normal threshold CT and curvature threshold ST of the narrow facet are set. If the plane normal and curvature of the initial reference point in the sequence K are both greater than the threshold, the reference point is removed from the sequence, otherwise it is retained.

[0023] The step 6) is specifically as follows:

[0024] Determine whether the number of remaining reference points after removing the sequence is less than 3. If so, it is determined that there are insufficient valid reference points on the narrow facet plane and the plane cannot be segmented and extracted. If the number of remaining reference points is greater than or equal to 3, the 3 reference points with the smallest curvature are selected as the final anchor points.

[0025] Assume the plane equation is ax+by+cz+d=0, the average normal direction of the three anchor points with the smallest curvature is taken as the normal direction of the split surface, the normal vector n=(a,b,c), and a family of parallel planes is obtained;

[0026] Substitute the coordinates of the three anchor points into the plane equation to obtain the parameters d1, d2, and d3. Use the average value of d1, d2, and d3 as the d value of the plane equation to obtain the narrow facet to be determined.

[0027] The present invention has the following beneficial effects and advantages:

[0028] 1. To address the complex structure of narrow facets on parts, the limited number of point clouds, and the influence of edge measurement noise, this invention effectively combines the local features of the narrow facets themselves and the surrounding area to establish reference point selection with geometric constraints. This can achieve high-quality segmentation of the part point cloud and ensure the subsequent acquisition of high-precision results for dimensional error and form and position error.

[0029] 2. The present invention does not require precise registration and annotation of point cloud data, reduces the need for prior knowledge, improves the degree of automation of point cloud segmentation, and has strong engineering application and promotion value. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] FIG1 is a schematic diagram of a narrow facet of a complex part of the present invention and the corresponding triangulated mesh data;

[0031] Figure 2 Flowchart of the point cloud segmentation method for narrow facets of micro-complex structure parts designed for the present invention. DETAILED DESCRIPTION

[0032] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0033] Figure 1 is a schematic diagram of the narrow facets of a complex part of the present invention, a) is a model of the narrow facets of the complex part, and b) is the triangulated data result obtained by encapsulating the point cloud file obtained by three-dimensional structured light scanning of the complex part, wherein plane 1 is the narrow facet to be measured. By comparing a) and b), it can be seen that the facet is too narrow, and the edge measurement noise seriously affects the measurement accuracy, and the facet information cannot be obtained using existing segmentation methods.

[0034] Reference Figure 2 , the method implementation steps of the embodiment of the present invention are as follows:

[0035] Step 1: Start the part point cloud segmentation by Figure 1b ) The original point cloud data obtained is used as input, and no alignment operation is required;

[0036] Step 2: Convert the input raw point cloud data into voxel data through an octree-based voxelization process and perform downsampling. Voxels are small cubes with a fixed length. Since point clouds are randomly distributed and have no regular topological structure, voxelizing the point cloud data into a regular grid structure can make the processing of point cloud data more convenient.

[0037] Step 3: Use the region growing algorithm based on seed point selection to obtain the large surface of the part. The specific method is to calculate the curvature of the point cloud data close to the center position in each voxel, sort it and select the point with the smallest curvature as the seed point, use the K-nearest neighbor or fixed radius search method to search its neighboring points, and use the growth criterion (according to the angle between the seed point and the normal of the neighboring point is less than the set threshold) to compare them one by one. If the growth criterion is met, the current neighboring point is added to the same block as the seed point, and if the curvature value of the neighboring point is less than the set curvature threshold, it is added to the current seed point queue; if the growth criterion is not met, continue to the next neighboring point judgment.

[0038] In step 4, after step 3, the surface of most areas of the part can be automatically obtained. However, due to the small amount of point cloud data and the influence of edge measurement noise, the narrow edge faces cannot be effectively segmented into planes by step 3. The part model is aligned with the segmented large area, and the point cloud range of the narrow edge faces adjacent to the large area is calculated. This method is used to initially filter out irrelevant discrete points.

[0039] Step 5: Determine the range of the narrow facet point cloud interval, restore the point cloud data within this range, ensure sufficient feature point cloud data, calculate the 3D point cloud curvature, and implement the narrow facet point cloud segmentation method based on local features. Select the narrow facet reference point sequence and set the rules as follows:

[0040] Combined with the segmented adjacent faces and the model, the normal line of the narrow facet plane is calculated. The narrow facet point cloud interval is discretized along the normal direction. The discretization range is set according to the actual geometric feature parameters of the part (set to 2mm*1mm*0.5mm in this example). The data is not downsampled.

[0041] Step 6: Based on the local features of the narrow facet, determine whether the current reference point sequence can represent the narrow facet plane. The judgment conditions are as follows:

[0042] Calculate the curvature of the three-dimensional point cloud of each discretization interval, select the point with the smallest curvature as the initial reference point ki, and put it into the initial reference point queue K in turn

[0043] Combined with the segmented adjacent faces and models, set the narrow facet plane normal threshold CT and curvature threshold ST. If the initial reference point in queue K is larger than the threshold, it is removed from the queue; otherwise, it is retained.

[0044] Step 7: If the number of remaining reference points after step 6 is less than 3, it is determined that there are insufficient valid reference points on the narrow facet plane and the plane cannot be segmented and extracted. If the number of remaining reference points is greater than or equal to 3, the three reference points with the smallest curvature are selected as the final anchor points and step 8 is executed.

[0045] Step 8. Assume that the plane equation is ax+by+cz+d=0, and the average normal direction of the three anchor points with the smallest curvature is used as the normal direction of the segmentation surface, and the normal vector n=(a, b, c). This allows us to obtain a family of parallel planes. Substitute the coordinates of the three anchor points into the plane equation to obtain d1, d2, and d3. The average value of d1, d2, and d3 is used as the d value of the plane equation. At this point, a narrow facet is obtained and the segmentation is completed.

Claims

1. A point cloud segmentation method for narrow facets of micro parts based on local features, characterized in that: The following steps are involved: 1) Obtain the original point cloud data of the part and preprocess it; 2) Using the region growing algorithm based on seed point selection on the preprocessed data, the large area surface of the part except for the narrow edge faces is obtained; 3) Align the part model with the segmented large area to obtain the point cloud range of the narrow facets of the part adjacent to the large area; 4) Restore the point cloud data within the narrow facet point cloud range and calculate the 3D point cloud curvature; 5) Based on the curvature of the 3D point cloud, a reference point sequence is constructed, and based on the curvature and normal of the narrow facet, whether the sequence can represent the narrow facet plane is determined; 6) Using the final reference points, find the narrow facets and complete the point cloud segmentation; The step 4) further includes: Using the large area surface and part model that have been segmented and are adjacent to the narrow facet, the normal of the plane where the narrow facet is located is obtained, and the narrow facet point cloud interval is discretized along the normal direction. The step 5) is specifically as follows: Calculate the curvature of the 3D point cloud in each discretization interval, select the point with the smallest curvature as the initial reference point ki, and put it into the initial reference point sequence K in sequence; Using the large area surface and part model adjacent to the narrow facet that have been segmented, the plane normal threshold CT and curvature threshold ST of the narrow facet are set. If the plane normal and curvature of the initial reference point in the sequence K are both greater than the threshold, the reference point is removed from the sequence, otherwise it is retained.

2. The method for segmenting point clouds at narrow facets of micro parts based on local features according to claim 1, characterized in that: The step 1) is specifically as follows: The original point cloud data is converted into voxel data through an octree-based voxelization process and then downsampled.

3. The method for segmenting point clouds at narrow facets of micro parts based on local features according to claim 1, characterized in that: The step 2) is specifically as follows: Calculate the curvature of the point cloud data of multiple points close to the center point in each voxel, sort the curvatures, select the point with the smallest curvature as the seed point, use the K-nearest neighbor or fixed radius search method to search for its neighboring points, and use the growth criterion to compare the neighboring points one by one. If the growth criterion is met, the current neighboring point is added to the same block as the seed point, and if the curvature value of the neighboring point is less than the set curvature threshold, it is added to the current seed point queue; if the growth criterion is not met, continue to the next neighboring point judgment.

4. The method for segmenting point clouds at narrow facets of micro parts based on local features according to claim 3, characterized in that: The comparison is performed using the growth criteria for each neighboring point, specifically, determining whether the normal angle between the seed point and the neighboring point is less than a set threshold.

5. The method for segmenting point clouds at narrow facets of micro parts based on local features according to claim 1, characterized in that: The step 6) is specifically as follows: Determine whether the number of remaining reference points after removing the sequence is less than 3. If so, it is determined that there are insufficient valid reference points on the narrow facet plane and the plane cannot be segmented and extracted. If the number of remaining reference points is greater than or equal to 3, the 3 reference points with the smallest curvature are selected as the final anchor points. Assume that the plane equation is ax+by+cz+d=0, and the average normal direction of the three anchor points with the smallest curvature is taken as the normal direction of the split surface, and the normal vector n=(a,b,c). A family of parallel planes is obtained. Substitute the coordinates of the three anchor points into the plane equation to obtain the parameters d1, d2, and d3. Use the average value of d1, d2, and d3 as the d value of the plane equation to obtain the narrow facet to be determined.

Images