CN112233039B - Three-dimensional laser point cloud denoising method based on domain point space characteristics - Google Patents
Three-dimensional laser point cloud denoising method based on domain point space characteristics Download PDFInfo
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Abstract
The invention provides a three-dimensional laser point cloud denoising method based on domain point space characteristics, which comprises the following steps: s1: reading and storing original data point clouds into an n-row 3-column point cloud data set O; s2: dividing the domain point and the target point P into subsets Q; s3: performing plane fitting on all point clouds in the subset Q; s4: translating the plane of the three-dimensional plane equation to a target point P along a normal vector to obtain a new space plane; s5: calculating the average distance d from the rest points in the subset Q except the target point P to the space plane, and storing as the 4 th column of the point cloud data set O, S6: traversing all points, and repeating the steps S2-S5; s7: and (4) counting the distribution characteristics of the data in the 4 th row, selecting a threshold value f according to the distribution range and the confidence interval, and keeping the point cloud data smaller than the threshold value f as a de-noised result. The three-dimensional laser point cloud denoising method based on the domain point space characteristics improves the denoising accuracy and efficiency.
Description
Technical Field
The invention relates to the fields of civil construction and surveying and mapping, in particular to a three-dimensional laser point cloud denoising method based on field point space characteristics.
Background
In tunnel construction, tunnel deformation monitoring plays an important role in ensuring construction safety and determining supporting time. However, the traditional deformation measurement technology has extremely low measurement efficiency, and can only obtain deformation data of a small number of arranged measurement points, so that the deformation of the tunnel cannot be comprehensively reflected. The monitoring work of the whole space is required to be carried out through the three-dimensional laser, due to the special environment in the tunnel, a large number of noise points caused by dust raising and the like are contained in the collected three-dimensional point cloud data, the data are denoised by adopting frequency domain filtering and experience judgment in the past, the denoising effect is poor, and the efficiency is low.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a three-dimensional laser point cloud denoising method based on the domain point spatial characteristics, and the denoising accuracy and efficiency are improved by the method based on the domain point spatial characteristics.
In order to achieve the above object, the present invention provides a three-dimensional laser point cloud denoising method based on domain point space characteristics, comprising the steps of:
s1: reading and storing original data point clouds into a point cloud data set O with n rows and 3 columns, wherein n is a natural number which is more than or equal to 1;
s2: selecting a target point P (x) in the point cloud data set O p ,y p ,z p )，x p 、y p And z p Coordinate values of an x axis, a y axis and a z axis of the target point P are respectively; searching k field points (k is more than or equal to 3) which are closest to the point in the point cloud data set O by using an knn algorithm, and dividing the field points and the target point P into a subset Q;
s3: performing plane fitting on all point clouds in the subset Q to obtain a three-dimensional plane equation:
Ax+By+Cz+D＝0 (1)；
a, B, C is the projection component of the plane normal vector in x, y, z direction, D is constant term;
s4: translating the plane of the three-dimensional plane equation to the position of the target point P along a normal vector to obtain a new space plane, wherein the formula of the space plane is as follows:
Ax+By+Cz+D′＝0 (2)；
wherein, D' ═ - (Ax) p +By p +Cz p ) (3)；
S5: calculating the average distance d from the rest points in the subset Q except the target point P to the space plane, and storing as the 4 th column of the point cloud data set O:
s6: traversing all the points in the point cloud data set O, and repeating the steps from S2 to S5, wherein the point cloud data set O is n rows and 4 columns;
s7: and counting the distribution characteristics of the 4 th row data of the point cloud data set O, selecting a threshold value f according to the distribution range and the confidence interval of the 4 th row data of the point cloud data set O, and keeping the point cloud data of which the 4 th row data is smaller than the threshold value f as a de-noised result.
Due to the adoption of the technical scheme, the invention has the following beneficial effects:
the three-dimensional laser point cloud denoising method based on the domain point space characteristics can rapidly and rapidly remove noise points in three-dimensional point cloud data, provides a basis for tunnel three-dimensional monitoring, and has a good application prospect in tunnel monitoring.
Drawings
FIG. 1 is a flow chart of a three-dimensional laser point cloud denoising method based on a domain point space characteristic according to an embodiment of the present invention;
FIG. 2 is a distribution characteristic diagram of normal vectors of all points in a point cloud before denoising according to an embodiment of the present invention;
FIG. 3 is a distribution feature diagram of a denoised point cloud normal vector according to an embodiment of the present invention.
Detailed Description
The following description of the preferred embodiments of the present invention will be provided in conjunction with the accompanying drawings 1-3, and will make the functions and features of the invention better understood.
Referring to fig. 1, a three-dimensional laser point cloud denoising method based on a domain point spatial feature according to an embodiment of the present invention includes:
s1: reading and storing original data point clouds into a point cloud data set O with n rows and 3 columns, wherein n is a natural number which is more than or equal to 1;
s2: selecting a target point P (x) in the point cloud data set O p ，y p ，z p )，x p 、y p And z p Coordinate values of an x axis, a y axis and a z axis of the target point P are respectively; searching k field points (k is more than or equal to 3) nearest to the point in the point cloud data set O by using an knn algorithm, and dividing the field points and the target point P into a subset Q;
s3: performing plane fitting on all point clouds in the subset Q to obtain a three-dimensional plane equation:
Ax+By+Cz+D＝0 (1)；
a, B, C is the projection component of the plane normal vector in x, y, z direction, D is constant term;
s4: translating the plane of the three-dimensional plane equation to the position of the target point P along the normal vector to obtain a new space plane, wherein the formula of the space plane is as follows:
Ax+By+Cz+D′＝0 (2)；
wherein, D' ═ - (Ax) p +By p +Cz p ) (3)；
S5: calculating the average distance d from the rest points in the subset Q except the target point P to the space plane, and storing as the 4 th column of the point cloud data set O:
s6: traversing all the points in the point cloud data set O, and repeating the steps from S2 to S5, wherein the point cloud data set O is n rows and 4 columns;
s7: and counting the distribution characteristics of the 4 th row data of the point cloud data set O, selecting a threshold f according to the distribution range and the confidence interval of the 4 th row data of the point cloud data set O, and keeping the point cloud data of which the 4 th row data is smaller than the threshold f as a de-noised result.
According to the three-dimensional laser point cloud denoising method based on the domain point space characteristics, the average distance from all the domain points of the points in the point cloud space to the fitting surface of the points is calculated and counted as a parameter, the threshold value is set according to the counting result, the noise points are removed, and the effect of automatically denoising the three-dimensional laser point cloud data is achieved.
In specific implementation, firstly, any point is selected as a target point, and then the domain points are searched to form a space subset. And performing plane fitting on the points of the space subset class to obtain a plane, and translating the plane to a target point along the normal vector direction of the plane. The average distance of the domain points to the plane is calculated. Traversing all points in the point cloud, repeating the steps to obtain the statistical characteristics of the average distance from the area point of each point to the plane, and then setting a denoising threshold value according to the statistical characteristics to denoise.
By comparing fig. 2 and fig. 3, it can be seen that the distribution is more concentrated and the noise interference is significantly reduced by denoising.
While the present invention has been described in detail and with reference to the embodiments thereof as illustrated in the accompanying drawings, it will be apparent to one skilled in the art that various changes and modifications can be made therein. Therefore, certain details of the embodiments are not to be interpreted as limiting, and the scope of the invention is to be determined by the appended claims.
Claims (1)
1. A three-dimensional laser point cloud denoising method based on domain point space characteristics comprises the following steps:
s1: reading and storing original data point clouds into a point cloud data set O with n rows and 3 columns, wherein n is a natural number which is more than or equal to 1;
s2: selecting a target point P (x) in the point cloud data set O p ,y p ,z p )，x p 、y p And z p Coordinate values of the x axis, the y axis and the z axis of the target point P are respectively; using knn algorithm to retrieve k field points nearest to the point in the point cloud data set O, wherein k is more than or equal to 3, and dividing the field points and the target point P into a subset Q;
s3: performing plane fitting on all point clouds in the subset Q to obtain a three-dimensional plane equation:
Ax+By+Cz+D＝0 (1)；
a, B, C is the projection component of the plane normal vector in x, y, z direction, D is constant term;
s4: translating the plane of the three-dimensional plane equation to the position of the target point P along a normal vector to obtain a new space plane, wherein the formula of the space plane is as follows:
Ax+By+Cz+D′＝0 (2)；
wherein D' ═ Ax p +By p +Cz p ) (3)；
S5: calculating the average distance d from the rest points in the subset Q except the target point P to the space plane, and storing as the 4 th column of the point cloud data set O:
s6: traversing all the points in the point cloud data set O, and repeating the steps from S2 to S5, wherein the point cloud data set O is n rows and 4 columns;
s7: and counting the distribution characteristics of the 4 th row data of the point cloud data set O, selecting a threshold value f according to the distribution range and the confidence interval of the 4 th row data of the point cloud data set O, and keeping the point cloud data of which the 4 th row data is smaller than the threshold value f as a de-noised result.
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| CN105844600A (en) * | 2016-04-27 | 2016-08-10 | 北京航空航天大学 | Space target three-dimensional point cloud smooth denoising method |
| CN108846809A (en) * | 2018-05-28 | 2018-11-20 | 河海大学 | A kind of noise eliminating method towards point off density cloud |
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| CN105844600A (en) * | 2016-04-27 | 2016-08-10 | 北京航空航天大学 | Space target three-dimensional point cloud smooth denoising method |
| CN108846809A (en) * | 2018-05-28 | 2018-11-20 | 河海大学 | A kind of noise eliminating method towards point off density cloud |
Non-Patent Citations (2)
| Title |
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| 基于APOCS的点云去噪算法;李刚森等;《智能计算机与应用》;20180828(第04期);全文 * |
| 基于点云场景特征配准的构筑物形变监测;陈微;《中国优秀硕士学位论文全文数据库信息科技辑》;20200215;全文 * |
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