Method for detecting surface flatness of precast beam based on three-dimensional point cloud model

Patent metadata

Field	Value
Publication number	US-12136206-B2
Application number	US-202117785909-A
Country	US
Kind code	B2
Filing date	Nov 15, 2021
Priority date	Oct 13, 2021
Publication date	Nov 5, 2024
Grant date	Nov 5, 2024

Abstract

The present invention discloses a method for detecting surface flatness of a precast beam based on a three-dimensional point cloud model, including the following steps: (1) performing, according to a specific geometry of a three-dimensional point cloud model of a target component in a three-dimensional coordinate system, coarse calibration and fine calibration on the model sequentially to determine a spatial rotation matrix and perform point cloud coordinate calibration; (2) determining normal vectors at positions of points of the three-dimensional point cloud model of the component according to a principal component analysis method and a K-nearest-neighbor principle, so that a to-be-detected surface is segmented and extracted by defining a normal vector direction and a coordinate interval; and (3) iteratively searching for an optimal reference plane according to a form relationship between the to-be-detected surface and the three-dimensional coordinate system and calculating flatness of the surface.

First claim

What is claimed is:

1. A method for detecting surface flatness of a precast beam based on a three-dimensional point cloud model, comprising the following steps:
(1) performing, according to a specific geometry of a three-dimensional point cloud model of a target component in a three-dimensional coordinate system, coarse calibration and fine calibration on the model sequentially to determine a spatial rotation matrix and perform point cloud coordinate calibration;
(2) determining normal vectors at positions of points of the three-dimensional point cloud model of the component according to a principal component analysis method and a K-nearest-neighbor principle, so that a to-be-detected surface is segmented and extracted by defining a normal vector direction and a coordinate interval; and
(3) iteratively searching for an optimal reference plane according to a form relationship between the to-be-detected surface and the three-dimensional coordinate system and calculating flatness of the surface,
wherein step (1) specifically comprises the following steps:
1.1 setting, for a three-dimensional point cloud model Pt 0 of the precast beam, an origin O of an original coordinate system X 0 Y 0 Z 0 at a centroid of the three-dimensional point cloud model:

Pt
c

=

[

x
1

-

μ
X

y
1

-

μ
Y

z
1

-

μ
Z

x
2

-

μ
X

y
1

-

μ
Y

z
1

-

μ
Z

⋮

x
n

-

μ
X

y
n

-

μ
Y

z
n

-

μ
Z

]

,

wherein Pt c is a centralized three-dimensional point cloud model; x 1 . . . x n are coordinate values of points X 0 in Pt 0 , y 1 . . . y n , are coordinate values of Y 0 , and z 1 . . . z n are coordinate values of Z 0 ; and μ X is an average value of the coordinate values of the points X 0 in Pt 0 , μ Y is an average value of the coordinate values of original Y 0 , and μ z is an average value of the coordinate values of original Z 0 ;
1.2 determining, for the precast beam placed horizontally, that a positive direction of an initial Z 1 coordinate axis is an opposite direction of gravity and is parallel to a direction of the beam height, making a projection of the three-dimensional point cloud model Pt c onto a plane X 0 OY 0 , performing a principal component analysis on the projection to first decentralize a projected point cloud according to a principle of the principal component analysis, calculate a covariance matrix of the decentralized point cloud, and perform singular value decomposition on the covariance matrix to obtain a group of eigenvalues and an eigenvector uniquely corresponding to each eigenvalue, wherein an eigenvector corresponding to the largest eigenvalue is a first principal component, and an eigenvector corresponding to the second largest eigenvalue is a second principal component; and defining a direction of a Y 1 coordinate axis as a direction of the first principal component, and defining a direction of an X 1 coordinate axis as a direction of the second principal component, to complete calibration of an initial coordinate system X 1 Y 1 Z 1 , that is, coarse calibration of coordinates of the three-dimensional point cloud model; and
1.3 respectively making slices at appropriate positions capable of reflecting features of the beam width, the beam length, and the beam height of the component, wherein thicknesses of the slices are twice a point cloud density; making a projection of points contained in the slices to a slice plane and performing the principal component analysis, fitting the projection to a straight line, and comparing angles between the projected straight line and two coordinate axes; and defining a direction of a coordinate axis with a smaller angle with the projected straight line as the direction of the first principal component according to the principle of the principal component analysis, and defining a direction of an other coordinate axis in the slice plane as the direction of the second principal component, to complete calibration of a final coordinate system XYZ, that is, fine calibra

Assignees

• Univ Southeast

Inventors

• Xiong Wen
• Xu Chang
• Zhu Yanjie

Classifications

• G06T2207/30132

  Masonry; Concrete · CPC title

• G06T2207/10028

  Range image; Depth image; 3D point clouds · CPC title

• G06T3/60 Primary

  Rotation of whole images or parts thereof · CPC title

• G06T7/521

  from laser ranging, e.g. using interferometry; from the projection of structured light · CPC title

• G06T7/0006 Primary

  using a design-rule based approach · CPC title