Multi-scale normal feature point cloud registering method

A feature point and multi-scale technology, applied in image data processing, 3D image processing, instruments, etc., can solve the problems of insufficient registration accuracy and calculation efficiency

Active Publication Date: 2014-11-12
HARBIN ENG UNIV
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Problems solved by technology

Moreover, the mainstream registration methods are insufficient in terms of

Method used

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  • Multi-scale normal feature point cloud registering method
  • Multi-scale normal feature point cloud registering method
  • Multi-scale normal feature point cloud registering method

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Experimental program
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Embodiment Construction

[0061] The implementation method is given using three sets of point cloud data of bunny, dragon and happy_backdrop, figure 1 is the initial position of three sets of point cloud data, and the same set of point cloud data contains point cloud data of two perspectives.

[0062] Step 1: Input two point clouds (source point cloud, target point cloud). Read in the 3D point cloud acquisition equipment, such as the point cloud data collected by the laser triangulation ranging scanner. The point cloud data must contain two point cloud data from two different perspectives with overlapping parts.

[0063] Step 2: Calculate the curvature of the three-scale radius neighborhood of the point, and extract key points from the target point cloud and the source point cloud according to the objective function;

[0064] Step 2.1: Select an appropriate radius r according to different point cloud data, and calculate the radius as the neighborhood of any point p. In the present invention, r is se...

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Abstract

The invention relates to a multi-scale normal feature point cloud registering method. The multi-scale normal feature point cloud registering method is characterized by including the steps that two-visual-angle point clouds, including the target point clouds and the source point clouds, collected by a point cloud obtaining device are read in; the curvature of radius neighborhoods of three scales of points is calculated, and key points are extracted from the target point clouds and the source point clouds according to a target function; the normal vector angular deviation and the curvature of the key points in the radius neighborhoods of the different scales are calculated and serve as feature components, feature descriptors of the key points are formed, and a target point cloud key point feature vector set and a source point cloud key point feature vector set are accordingly obtained; according to the similarity level of the feature descriptors of the key points, the corresponding relations between the target point cloud key points and the source point cloud key points are preliminarily determined; the wrong corresponding relations are eliminated, and the accurate corresponding relations are obtained; the obtained accurate corresponding relations are simplified with the clustering method, and the evenly-distributed corresponding relations are obtained; singular value decomposition is carried out on the final corresponding relations to obtain a rigid body transformation matrix.

Description

technical field [0001] The invention relates to a multi-scale normal feature point cloud registration method. Background technique [0002] 3D point cloud data registration technology has always been a research hotspot and difficulty in the fields of reverse engineering, computer vision, surface quality inspection, and photogrammetry. As the point cloud data acquisition method becomes simpler and the cost is lower and lower, point cloud processing technology has been continuously practiced and developed in the fields of cultural relics protection, archaeological research, 3D film and television, medicine, industry and so on. In the process of three-dimensional measurement, affected by the spatial position, geometric shape and measurement method of the measured object, single-view measurement is limited. Equipment and instruments need to position and measure objects from different perspectives, and integrate the results of multi-view measurements into the same coordinates In...

Claims

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Application Information

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IPC IPC(8): G06T15/00
Inventor 陆军彭仲涛苏航夏桂华蔡成涛朱齐丹刘伟华威韩吉瑞方莹
Owner HARBIN ENG UNIV
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