阅读说明：本技术 一种单光子激光雷达空间变换噪声判断、滤波方法及装置 (Single photon laser radar space transformation noise judgment and filtering method and device ) 是由 谌一夫 乐源 王力哲 陈刚 陈伟涛 于 2020-09-02 设计创作，主要内容包括：本发明提供了一种单光子激光雷达空间变换噪声判断、滤波方法及装置,涉及遥感测绘技术领域,包括：获取单光子激光雷达的原始点云数据；根据预设光子数量确定原始点云数据中的每个光子的初始邻域光子区域；对初始邻域光子区域内的光子进行空间PCA变换,确定不同方向的三个主分量矢量；根据三个主分量矢量构建空间中的立体椭圆；根据立体椭圆内和初始邻域光子区域内的光子数量判断原始点云数据中每个光子是否为噪声信号。本发明通过对空间中的每个光子点进行可变半径球体划分并进行空间PCA变换,构建空间立体椭圆；并根据空间立体椭圆和空间球体内光子数量的比值进行滤波,实现自动、快速、高精度的光子点云的有效数据提取。(The invention provides a method and a device for judging and filtering space transformation noise of a single photon laser radar, which relate to the technical field of remote sensing mapping and comprise the following steps: acquiring original point cloud data of the single photon laser radar; determining an initial neighborhood photon region of each photon in the original point cloud data according to a preset photon number; carrying out spatial PCA (principal component analysis) conversion on photons in the initial neighborhood photon sub-region, and determining three principal component vectors in different directions; constructing a three-dimensional ellipse in a space according to the three principal component vectors; and judging whether each photon in the original point cloud data is a noise signal or not according to the quantity of photons in the three-dimensional ellipse and the initial neighborhood light subarea. The method comprises the steps of dividing each photon point in space into variable-radius spheres and performing space PCA (principal component analysis) transformation to construct a space three-dimensional ellipse; and filtering is carried out according to the ratio of the number of photons in the space three-dimensional ellipse to the number of photons in the space sphere, so that the automatic, rapid and high-precision effective data extraction of the photon point cloud is realized.)

1. A method for judging space transformation noise of a single photon laser radar is characterized by comprising the following steps:

acquiring original point cloud data of the single photon laser radar;

determining an initial neighborhood photon region of each photon in the original point cloud data according to a preset photon number;

carrying out spatial PCA (principal component analysis) conversion on photons in the initial neighborhood photon sub-region, and determining three principal component vectors in different directions;

constructing a three-dimensional ellipse in a space according to the three principal component vectors;

and judging whether each photon in the original point cloud data is a noise signal or not according to the quantity of photons in the three-dimensional ellipse and the initial neighborhood light subarea.

2. The method for determining noise in spatial transform of single photon lidar of claim 1, wherein the initial neighborhood photon region is a spatial sphere of variable radius.

3. The method for determining single photon lidar spatial transform noise according to claim 2, wherein determining an initial neighborhood photon region for each photon in the raw point cloud data according to a predetermined number of photons comprises:

and constructing a space sphere for each photon in the original point cloud data, so that the space sphere contains the photons with the preset number of photons.

4. The method for judging the single photon lidar space transformation noise according to any of claims 1 to 3, wherein the performing the spatial PCA transformation on the photons in the initial neighborhood photon sub-region and the determining the three principal component vectors in different directions comprises:

determining a data set matrix according to the coordinate dimension of each photon in the initial neighborhood light subregion;

determining a covariance matrix of the dataset matrix;

performing characteristic decomposition on the covariance matrix to determine an eigenvector matrix;

determining the first three principal components in the feature vector matrix as the three principal component vectors.

5. The method for determining the spatial transform noise of single photon lidar according to any of claims 1-3, wherein said constructing a solid ellipse in space from said three principal component vectors comprises:

constructing a new coordinate system according to the directions of the three principal component vectors;

respectively projecting the original coordinate of each photon in the initial neighborhood photon region onto three coordinate axes of the new coordinate system, and determining three projection intervals according to the projection of the original coordinate of each photon on the three coordinate axes;

and constructing the three-dimensional ellipse by taking the three projection intervals as three radiuses of the three-dimensional ellipse.

6. The method for determining noise in space transformation of single photon lidar according to any of claims 1-3, wherein said determining whether each photon in said original point cloud data is a noise signal according to the number of photons in said stereo ellipse and in said initial neighborhood photon region comprises:

respectively determining the number of photons contained in the solid ellipse and the number of photons contained in the initial neighborhood photon area;

determining the ratio of the number of photons contained in the solid ellipse to the number of photons contained in the initial neighborhood photon region;

judging whether the ratio meets a noise judgment condition, wherein the noise judgment condition is that the ratio is smaller than a preset threshold value;

and when the noise judgment condition is met, determining the central photon of the three-dimensional ellipse as the noise signal.

7. The method of single photon lidar spatial transformation noise determination of claim 6, wherein the determining the number of photons within the solid ellipse comprises:

determining new coordinates of each photon in the initial neighborhood light subarea after the photon is subjected to space PCA conversion;

and determining whether each photon is positioned in the solid ellipse according to the new coordinates so as to determine the number of photons in the solid ellipse.

8. The utility model provides a single photon laser radar space transformation noise judgement device which characterized in that includes:

the acquisition module is used for acquiring original point cloud data of the single photon laser radar;

the processing module is used for determining an initial neighborhood photon region of each photon in the original point cloud data according to the preset photon number; the system is also used for carrying out spatial PCA conversion on photons in the initial neighborhood photon sub-region and determining three principal component vectors in different directions; the three main component vectors are used for constructing a three-dimensional ellipse in a space;

and the judging module is used for judging whether each photon in the original point cloud data is a noise signal according to the quantity of the photons in the three-dimensional ellipse and the initial neighborhood light subregion.

9. A single photon laser radar space transformation filtering method is characterized by comprising the following steps:

acquiring a noise signal in original point cloud data of a single photon laser radar, wherein whether each photon contained in the original point cloud data is the noise signal is judged by adopting the single photon laser radar space transformation noise judgment method as claimed in any one of claims 1 to 7;

and eliminating the noise signal in the original point cloud data for filtering.

10. A single photon laser radar space transformation filter device is characterized by comprising:

a second obtaining module, configured to obtain a noise signal in original point cloud data of a single photon laser radar, where whether each photon included in the original point cloud data is the noise signal is determined by using the single photon laser radar spatial transformation noise determination method according to any one of claims 1 to 7;

and the filtering module is used for eliminating the noise signals in the original point cloud data so as to carry out filtering.

11. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the single photon lidar spatial transform noise determination method according to any of claims 1-7, or implements the single photon lidar spatial transform filtering method according to claim 9.

12. A computing device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the program when executing the program to implement the single photon lidar spatial transform noise determination method according to any of claims 1-7 or to implement the single photon lidar spatial transform filtering method according to claim 9.

Technical Field

The invention relates to the technical field of remote sensing mapping, in particular to a method and a device for judging and filtering space transformation noise of a single photon laser radar.

Background

The single photon laser radar is a novel laser detection technology developed in recent years, and compared with the traditional full-waveform laser radar, the single photon laser radar has great difference in design idea and data processing method. When the echo signal is obtained, the single photon laser radar does not focus on obtaining the waveform with high signal-to-noise ratio by high-energy emission, but focuses on utilizing limited resources and fully utilizes each photon in the echo signal. The single photon laser radar has higher pulse emission repetition frequency in terms of detection mechanism, adopts a receiving device with extremely high sensitivity, can detect and receive echo envelope amplitude of hundreds or even thousands of photons, and converts the detection into the detection of single photon, thereby having the advantages of long distance, high repetition frequency, high efficiency, light weight and the like, and simultaneously overcoming the problems of large volume, large mass, low reliability, contradiction between pulse energy and repetition frequency and the like of the traditional laser. The data processing for the single-photon laser radar is quite different from that of the traditional full-waveform laser radar, and the effective signal photons are extracted from the signals with low signal-to-noise ratio through probability distribution, so that high-precision measurement is realized.

When different ground object targets or multiple ground object mixed targets exist in a scanning detection target area and the detection environment changes, the point cloud data of the single photon laser radar changes with the space density of different detection areas and different ground object types, namely the point cloud density distribution in the space is uneven. In the prior art, the algorithm for processing the photon data type generally uses equal-interval slice segmentation and then performs filtering by using the same threshold, but the filtering effect of the method is not ideal.

Disclosure of Invention

The invention solves the technical problem of unsatisfactory point cloud data filtering effect of a single photon laser radar in the prior art, and in order to achieve the aim, the invention provides a method for judging the spatial transformation noise of the single photon laser radar in a first aspect, which comprises the following steps:

acquiring original point cloud data of the single photon laser radar;

determining an initial neighborhood photon region of each photon in the original point cloud data according to a preset photon number;

carrying out spatial PCA (principal component analysis) conversion on photons in the initial neighborhood photon sub-region, and determining three principal component vectors in different directions;

constructing a three-dimensional ellipse in a space according to the three principal component vectors;

and judging whether each photon in the original point cloud data is a noise signal or not according to the quantity of photons in the three-dimensional ellipse and the initial neighborhood light subarea.

Further, the initial neighborhood photon region is a spatial sphere with a variable radius.

Further, the determining an initial neighborhood photon region of each photon in the original point cloud data according to a preset photon number comprises:

and constructing a space sphere for each photon in the original point cloud data, so that the preset number of photons are contained in the space sphere.

Further, the performing spatial PCA transformation on the photons in the initial neighborhood photon subregion, and determining three principal component vectors in different directions includes:

determining a data set matrix according to the coordinate dimension of each photon in the initial neighborhood light subregion;

determining a covariance matrix of the dataset matrix;

performing characteristic decomposition on the covariance matrix to determine an eigenvector matrix;

determining the first three principal components in the feature vector matrix as the three principal component vectors.

Further, the constructing a solid ellipse in space from the three principal component vectors comprises:

constructing a new coordinate system according to the directions of the three principal component vectors;

respectively projecting the original coordinate of each photon in the initial neighborhood photon region onto three coordinate axes of the new coordinate system, and determining three projection intervals according to the projection of the original coordinate of each photon on the three coordinate axes;

and constructing the three-dimensional ellipse by taking the three projection intervals as three radiuses of the three-dimensional ellipse.

Further, the determining whether each photon in the original point cloud data is a noise signal according to the number of photons in the solid ellipse and the initial neighborhood light subregion includes:

respectively determining the number of photons contained in the solid ellipse and the number of photons contained in the initial neighborhood photon area;

determining the ratio of the number of photons contained in the solid ellipse to the number of photons contained in the initial neighborhood photon region;

judging whether the ratio meets a noise judgment condition, wherein the noise judgment condition is that the ratio is smaller than a preset threshold value;

and when the noise judgment condition is met, determining the central photon of the three-dimensional ellipse as the noise signal.

Further, the determining the number of photons within the solid ellipse comprises:

determining new coordinates of each photon in the initial neighborhood light subarea after the photon is subjected to space PCA conversion;

and determining whether each photon is positioned in the solid ellipse according to the new coordinates so as to determine the number of photons in the solid ellipse.

In order to achieve the above object, in a second aspect, the present invention provides a single photon laser radar spatial transform noise determination apparatus, including:

the acquisition module is used for acquiring original point cloud data of the single photon laser radar;

the processing module is used for determining an initial neighborhood photon region of each photon in the original point cloud data according to the preset photon number; the system is also used for carrying out spatial PCA conversion on photons in the initial neighborhood photon sub-region and determining three principal component vectors in different directions; the three main component vectors are used for constructing a three-dimensional ellipse in a space;

and the judging module is used for judging whether each photon in the original point cloud data is a noise signal according to the quantity of the photons in the three-dimensional ellipse and the initial neighborhood light subregion.

By using the method or the device for judging the space transformation noise of the single photon laser radar, the region division of neighborhood photons is carried out on each photon point in the space through a three-dimensional variable radius sphere; performing space PCA (principal component analysis) transformation according to the space photon points in the sphere, calculating three principal component vectors in different directions in the corresponding space, constructing a new coordinate system, projecting the space photon points in the sphere onto different vector axes respectively, and calculating the projection distance on each vector axis respectively; constructing a space three-dimensional ellipse according to the direction of the vector axis by taking the calculated projection distance as a radius; and calculating the ratio of the number of photons in the space solid ellipse and the space sphere, and filtering the photons in the space sphere based on the ratio: and when the ratio is smaller than the threshold value, judging the central photon of the ellipse as a noise signal. According to the embodiment of the invention, the density distribution of photons in the space and the directional continuity characteristic of the target ground object are fully considered, and the spatial ellipses with different sizes and directions are constructed and adaptively selected aiming at the photon point clouds with different directions and different densities, so that the noise signals in the photon point cloud data can be automatically, quickly and accurately judged.

In order to achieve the above object, in a third aspect, the present invention provides a single photon lidar spatial transform filtering method, which includes:

acquiring a noise signal in original point cloud data of a single photon laser radar, wherein the noise signal is judged whether each photon contained in the original point cloud data is the noise signal or not by adopting the single photon laser radar space transformation noise judgment method;

and eliminating the noise signal in the original point cloud data for filtering.

In order to achieve the above object, in a fourth aspect, the present invention provides a single photon lidar spatial transform filter apparatus, including:

the second acquisition module is used for acquiring a noise signal in original point cloud data of the single photon laser radar, wherein the noise signal is judged whether each photon contained in the original point cloud data is the noise signal or not by adopting the single photon laser radar space transformation noise judgment method;

and the filtering module is used for eliminating the noise signals in the original point cloud data so as to carry out filtering.

By using the single photon laser radar space transformation filtering method or device, photons belonging to noise signals are quickly and accurately judged from the original point cloud data through a high-precision noise signal judgment method, and the noise signals are removed from the original point cloud data, so that quick and high-precision filtering of two-dimensional and three-dimensional single photon data is realized.

To achieve the above object, in a fifth aspect, the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the method for determining noise in spatial transform of single photon lidar according to the first aspect of the present invention or implements the method for filtering spatial transform of single photon lidar according to the third aspect of the present invention.

In order to achieve the above object, according to a sixth aspect, the present invention provides a computing device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for determining noise in spatial transform of single photon lidar according to the first aspect of the present invention or implements the method for filtering spatial transform of single photon lidar according to the third aspect of the present invention when executing the program.

The non-transitory computer-readable storage medium and the computing device according to the present invention have similar beneficial effects to the single photon laser radar spatial transform noise determination method according to the first aspect of the present invention or the single photon laser radar spatial transform filtering method according to the third aspect of the present invention, and are not described herein again.

Drawings

FIG. 1 is a schematic flow chart of a method for determining spatial transform noise of a single photon laser radar according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of a single photon lidar raw spatial photon point cloud data according to an embodiment of the invention;

FIG. 3 is a schematic diagram of a building of a spatial sphere according to an embodiment of the invention;

FIG. 4 is a schematic flow chart illustrating the determination of three principal component vectors in different directions according to an embodiment of the present invention;

FIG. 5 is a schematic flow chart for constructing a solid ellipse according to an embodiment of the present invention;

FIG. 6 is a schematic diagram of determining a projection pitch according to an embodiment of the invention;

FIG. 7 is a diagram illustrating a determination of a projected length of a coordinate axis according to an embodiment of the invention;

FIG. 8 is a schematic diagram of constructing a solid ellipse according to an embodiment of the present invention;

FIG. 9 is a flowchart illustrating a method for determining a noise signal according to an embodiment of the present invention;

FIG. 10 is a schematic flow chart illustrating a process for determining the number of photons contained within a solid ellipse, in accordance with an embodiment of the present invention;

FIG. 11 is a schematic structural diagram of a single photon laser radar spatial transform noise determination apparatus according to an embodiment of the present invention;

FIG. 12 is a schematic flow chart of a single photon lidar spatial transform filtering method according to an embodiment of the invention;

FIG. 13 is a schematic illustration of filtered effective signal photons in accordance with an embodiment of the present invention;

FIG. 14 is a schematic structural diagram of a spatial transform filter device of a single photon lidar according to an embodiment of the invention;

FIG. 15 is a schematic diagram of a computing device according to an embodiment of the invention.

Detailed Description

Embodiments in accordance with the present invention will now be described in detail with reference to the drawings, wherein like reference numerals refer to the same or similar elements throughout the different views unless otherwise specified. It is to be noted that the embodiments described in the following exemplary embodiments do not represent all embodiments of the present invention. They are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the claims, and the scope of the present disclosure is not limited in these respects. Features of the various embodiments of the invention may be combined with each other without departing from the scope of the invention.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.

Currently, as a new type of laser three-dimensional radar, photon counting laser radar (also called single photon laser radar) has a short development time. The method is characterized in that the echo signal intensity is low, the noise is larger than the laser echo, and the response rate of the photoelectric detection system is in the few-photon level. However, due to the extremely high sensitivity of the detection system, a large amount of noise generated by the target background environment can still trigger the detection circuit, and a large amount of noise data is generated. The lidar can simultaneously record photon events of target echoes and photon events in background noise, and a threshold discrimination method cannot be utilized. According to experimental experience, the existing feasible laser point cloud data processing method utilizes statistics to filter, so that noise is eliminated.

Compared with the common point cloud denoising processing, the particularity of the data denoising of the single photon laser radar is mainly shown in the following two aspects: first, noisy and unspecified patterns, closely related to the observation environment, and difficult to predict. This is caused by the operating mode of the single photon lidar. Secondly, the single photon laser radar is different from the traditional push-broom type or scanning type laser radar, data of the single photon laser radar are distributed in a narrow-band shape along a flight track, the single photon laser radar belongs to an elevation profile point cloud, and the density of the point cloud is low. Therefore, the existing point cloud denoising algorithm cannot be directly applied to the point cloud data processing of the single photon laser radar.

According to the method, the density distribution of photons in the space and the directional continuity characteristic of the target ground object are fully considered, and spatial ellipses with different sizes and directions are adaptively constructed for filtering aiming at photon point clouds with different directions and different densities. The automatic, fast and efficient photon point cloud effective data extraction is realized, and therefore high-precision point cloud data filtering is realized.

Fig. 1 is a schematic flow chart of a method for determining noise in spatial transform of single photon laser radar according to an embodiment of the present invention, including steps S1 to S5.

In step S1, raw point cloud data of the single photon lidar is acquired. Fig. 2 is a schematic diagram of raw spatial photon point cloud data of a single photon lidar according to an embodiment of the invention, where X, Y is the horizontal direction and Z is the elevation direction. In the embodiment of the invention, the original point cloud data of the single photon laser radar is a three-dimensional point set, as shown in the following formula (1):

wherein the content of the first and second substances,respectively representing the spatial coordinates of each photon in a spatial coordinate system,Nrepresenting the number of photons contained in the original point cloud data.

In step S2, an initial neighborhood photon region of each photon in the original point cloud data is determined according to a preset number of photons. In the embodiment of the invention, the initial neighborhood photon region is a space sphere with variable radiusAnd (3) a body. FIG. 3 is a schematic diagram illustrating a principle of constructing a spatial sphere according to an embodiment of the present invention, in which any photon point in the original point cloud data is targetedpThe space coordinate of which in the space coordinate system is expressed as

. At any one photon pointpAs a center, construct a radius ofRSpace ball ofAs shown in the following formula (2):

in an embodiment of the invention, a radius of each photon in the raw point cloud data is constructed asRSuch that a predetermined number of photons are contained in the space sphere, as shown in the following formula (3):

wherein the content of the first and second substances,S _p representing the set of photons contained within the spatial sphere,iindicating the number of each photon within the sphere,nrepresenting the total number of photons contained within the space sphere. In the embodiment of the invention, each space sphere contains 30 photons, thenn= 30. It is understood that the preset number of photons contained in the space sphere can be set and adjusted according to actual requirements, and the invention is not limited thereto.

In the embodiment of the invention, the space sphere with the variable radius can be constructed according to different densities of the neighborhood photons of each photon, so that the initial neighborhood photon region of each photon comprises a certain number of photons, the radius of the space sphere is adaptively adjusted to select a proper initial neighborhood photon region, and the accuracy and reliability of filtering the subsequent point cloud data are effectively improved.

In step S3, spatial PCA (principal component Analysis) transformation is performed on the photons in the initial neighborhood photon sub-region, and three principal component vectors in different directions are determined. Fig. 4 is a flowchart illustrating a process of determining three principal component vectors in different directions according to an embodiment of the present invention, which includes steps S31 to S34.

In step S31, a dataset matrix is determined from the coordinate dimension of each photon in the initial neighborhood light subregion. In the embodiment of the invention, the photon collection in the space sphere is adoptedS _p Based on the data set matrixSAs shown in the following formula (4):

wherein the content of the first and second substances,nrepresenting the number of photons within the spatial sphere,ma coordinate dimension representing each photon within the spatial sphere, such as a 1, 2, 3, or 4 dimensional space, where the 4 th dimension is typically represented as a time dimension, representing a cloud of photon points obtained at different times and in the same target region.

In step S32, the dataset matrix is determinedSThe covariance matrix C of (a). In an embodiment of the invention, a data set matrix is determinedSIs centralized matrix

As shown in the following formula (5):

wherein the content of the first and second substances,representing the mean of the sample data in different dimensions.

According to a centralized matrix

Determining the covariance matrix C as followsFormula (6):

in step S33, the covariance matrix is subjected to eigen decomposition to determine an eigenvector matrix. In the embodiment of the invention, the covariance matrix C is diagonalized, and an orthogonal eigenvector matrix U is determined to meet the requirementTherefore, the eigenvalue matrix is obtained by performing the characteristic decomposition on the covariance matrix CAnd a feature vector matrix U, as shown in equation (7) below:

wherein the content of the first and second substances,

representing feature vectorsThe corresponding characteristic value.

In step S34, the first three principal components in the feature vector matrix are determined as the three principal component vectors. In the embodiment of the present invention, the directions of the first three principal components in the spatial PCA transform, i.e., vectors, are obtained according to equation (7) above。

In the embodiment of the invention, the point cloud data in the space sphere is subjected to space PCA conversion, and the original data is converted into a group of representations which are linearly independent of each dimension through linear transformation, so that the method can be used for extracting main characteristic components of the point cloud data and analyzing and searching rules among a large amount of data. Therefore, the density distribution characteristics of a large number of spatial photon points in different directions in the space can be analyzed through spatial PCA conversion, effective detection of three directions with the maximum photon density is realized, and the accuracy and reliability of filtering the point cloud data subsequently are further improved.

In step S4, a solid ellipse in space is constructed from the three principal component vectors. Fig. 5 is a schematic flow chart illustrating the construction of a solid ellipse according to an embodiment of the present invention, which includes steps S41 to S43.

In step S41, a new coordinate system is constructed based on the directions of the three principal component vectors. In an embodiment of the invention, the first three principal component vectors based on the above determinationThe direction of the space sphere is taken as a coordinate axis, the center of the space sphere is taken as an origin, and a new coordinate system is constructed。

In step S42, the original coordinates of each photon in the initial neighborhood photon region are respectively projected onto three coordinate axes of the new coordinate system, and three projection distances are determined according to the projections of the original coordinates of each photon on the three coordinate axes. FIG. 6 is a schematic diagram illustrating determination of a projection pitch according to an embodiment of the invention. In the embodiment of the present invention, the directions of the first three principal components in the PCA conversion, i.e., vectors, are obtained according to the above equation (7)(ii) a Constructing a new coordinate system based on the three vector directionsThen the original coordinates of all photons in the space sphere are compared

Respectively vertically projected on three axes of a new coordinate systemOne projection length is acquired on each axis, as shown at 2a, 2b and 2c in fig. 6. FIG. 7 is a diagram illustrating a method for determining a projection length of a coordinate axis according to an embodiment of the inventionThe shaft is described as an example. Projecting the original coordinates of each photon in the initial neighborhood photon sub-region to a new coordinate systemOn the axis, its leftmost point PA and rightmost point PB are determined, and the distance between PA and PB is taken as the projection length (i.e., 2 b). In the embodiment of the present invention, half of the three projection lengths, i.e., a, b, and c, are determined as three projection pitches.

In step S43, the solid ellipse is constructed using the three projection pitches as three radii of the space solid ellipse. Fig. 8 is a schematic diagram illustrating the construction of a solid ellipse according to an embodiment of the present invention. In the embodiment of the present invention, the three projection distances a, b, and c obtained by the above calculation are respectively used as three radii of a spatial solid ellipse, and a spatial solid ellipse (as shown in fig. 8) is constructed according to the directions of three principal component vectors, where the mathematical expression of the spatial solid ellipse is shown in the following formula (8):

it can be understood that, in the embodiment of the present invention, the spatial solid ellipses with different sizes and directions can be constructed and adaptively selected through the above steps, characteristics of photon point clouds in different directions and different densities in the space are fully considered, and a reliable basis is provided for realizing automatic, fast and high-precision photon point cloud data extraction.

In step S5, it is determined whether each photon in the original point cloud data is a noise signal according to the number of photons in the solid ellipse and the initial neighborhood light subregion. Fig. 9 is a schematic flow chart illustrating the process of determining a noise signal according to an embodiment of the present invention, which includes steps S51 to S54.

In step S51, the number of photons contained in the solid ellipse and the number of photons contained in the initial neighborhood light subregion are determined, respectively. Fig. 10 is a schematic flow chart illustrating a process of determining the number of photons contained in a solid ellipse according to an embodiment of the present invention, which includes steps S511 to S512.

In step S511, new coordinates of each photon in the initial neighborhood light subregion after the spatial PCA transformation are determined. In an embodiment of the invention, the set of photons contained within the space sphereS _p Coordinates of each photon inAfter the space PCA conversion, the new coordinate systemExpressed as a photon setE _p . The transformation between the two coordinate systems is shown in the following equation (9):

（9）

where P denotes a conversion matrix.

In step S512, it is determined whether each of the photons is located within the solid ellipse according to the new coordinates, so as to determine the number of photons within the solid ellipse. In an embodiment of the invention, the photons are collectedE _p The new coordinates of each photon after spatial PCA transformation are substituted into the following equation (10) for calculation:

（10）

in the embodiment of the invention, ifIndicates that the photon is outside the constructed spatial solid ellipse if

It means that the photon is inside the constructed spatial solid ellipse. Thereby counting the number of all photons falling in the space solid ellipse。

In step S52, a ratio of the number of photons contained within the solid ellipse to the number of photons contained within the initial neighborhood light subregion is determined. In the embodiment of the invention, the number of photons in the initial neighborhood photon sub-region isnThe ratio of the number of photons in the solid ellipse to the number of photons in the initial neighborhood photon regionWherein。

In step S53, it is determined whether the ratio satisfies a noise determination condition. In the embodiment of the invention, the judgment is carried out according to the ratio of the number of photons in the three-dimensional ellipse to the number of photons in the initial neighborhood photon region, namely, the judgment of noise photons is carried out according to the density distribution of photons in the space, so that the accuracy of judging noise signals in point cloud data can be effectively improved.

In step S54, when the noise determination condition is satisfied, the central photon of the solid ellipse is determined as the noise signal. In the embodiment of the present invention, the noise determination condition is that the ratio is smaller than a preset threshold, that is, when the ratio is smaller than the preset thresholdAnd when the central photon of the three-dimensional ellipse is smaller than a preset threshold value, judging that the central photon of the three-dimensional ellipse is a noise signal.

By adopting the method for judging the space transformation noise of the single photon laser radar, the region division of neighborhood photons is carried out on each photon point in the space through a three-dimensional variable radius sphere; performing space PCA (principal component analysis) transformation according to the space photon points in the sphere, calculating three principal component vectors in different directions in the corresponding space, constructing a new coordinate system, projecting the space photon points in the sphere onto different vector axes respectively, and calculating the projection distance on each vector axis respectively; constructing a space three-dimensional ellipse according to the direction of the vector axis by taking the calculated projection distance as a radius; and calculating the ratio of the number of photons in the space solid ellipse and the space sphere, and filtering the photons in the space sphere based on the ratio: and when the ratio is smaller than the threshold value, judging the central photon of the ellipse as a noise signal. According to the embodiment of the invention, the density distribution of photons in the space and the directional continuity characteristic of the target ground object are fully considered, and the spatial ellipses with different sizes and directions are constructed and adaptively selected aiming at the photon point clouds with different directions and different densities, so that the noise signals in the photon point cloud data can be automatically, quickly and accurately judged.

It can be understood that the method for judging the space transformation noise of the single photon laser radar in the embodiment of the invention can be suitable for judging the noise of point cloud data of satellite-borne and airborne single photon laser radars in three-dimensional and two-dimensional spaces, and can realize high-precision signal photon extraction. For two-dimensional data, the spatial sphere and the solid ellipsoid will become the circular and elliptical detection windows of the two-dimensional space for filtering.

The embodiment of the second aspect of the invention also provides a device for judging the spatial transformation noise of the single photon laser radar. Fig. 11 is a schematic structural diagram of a single photon lidar spatial transform noise determination apparatus 1100 according to an embodiment of the present invention, which includes an obtaining module 1101, a processing module 1102, and a determining module 1103.

The obtaining module 1101 is configured to obtain original point cloud data of the single photon laser radar.

The processing module 1102 is configured to determine an initial neighborhood photon region of each photon in the original point cloud data according to a preset number of photons; the system is also used for carrying out spatial PCA conversion on photons in the initial neighborhood photon sub-region and determining three principal component vectors in different directions; and also for constructing a solid ellipse in space from the three principal component vectors.

The judging module 1103 is configured to judge whether each photon in the original point cloud data is a noise signal according to the number of photons in the stereo ellipse and the initial neighborhood light subregion.

In this embodiment of the present invention, the processing module 1102 is further configured to determine a data set matrix according to a coordinate dimension of each photon in the initial neighborhood light subregion; determining a covariance matrix of the dataset matrix; performing characteristic decomposition on the covariance matrix to determine an eigenvector matrix; determining the first three principal components in the feature vector matrix as the three principal component vectors.

In this embodiment of the present invention, the processing module 1102 is further configured to construct a new coordinate system according to the directions of the three principal component vectors; respectively projecting the original coordinate of each photon in the initial neighborhood photon region onto three coordinate axes of the new coordinate system, and respectively determining the projection distance of the original coordinate of each photon on the three coordinate axes; and constructing the three-dimensional ellipse by taking the three projection intervals as three radiuses of the three-dimensional ellipse.

The more specific implementation manner of each module of the single photon laser radar spatial transformation noise determination apparatus 1100 may refer to the description of the single photon laser radar spatial transformation noise determination method of the present invention, and has similar beneficial effects, and is not described herein again.

The embodiment of the third aspect of the invention also provides a single photon laser radar spatial transform filtering method. Fig. 12 is a schematic flow chart of a single photon lidar spatial transform filtering method according to an embodiment of the present invention, which includes steps S101 to S102, where:

in step S101, a noise signal in the original point cloud data of the single photon laser radar is obtained, wherein whether each photon included in the original point cloud data is the noise signal is determined by using the above-mentioned single photon laser radar spatial transformation noise determination method.

In step S102, the noise signal in the original point cloud data is removed for filtering. Fig. 13 is a schematic diagram of filtered effective signal photons, which is obtained by accurately judging and eliminating noise photons to extract effective signal photons to implement high-precision filtering according to an embodiment of the present invention.

By adopting the single photon laser radar spatial transform filtering method provided by the embodiment of the invention, photons belonging to the noise signal are quickly and accurately judged from the original point cloud data through a high-precision noise signal judgment method, and the noise signal is removed from the original point cloud data, so that quick and high-precision filtering of two-dimensional and three-dimensional single photon data is realized.

The embodiment of the fourth aspect of the invention also provides a single photon laser radar spatial transformation filtering device. Fig. 14 is a schematic structural diagram of a single photon lidar spatial transform filter apparatus 1400 according to an embodiment of the present invention, which includes a second obtaining module 1401 and a filtering module 1402.

The second obtaining module 1401 is configured to obtain a noise signal in original point cloud data of a single photon laser radar, where whether each photon included in the original point cloud data is the noise signal is determined by using the above-described single photon laser radar spatial transformation noise determination method.

The filtering module 1402 is configured to remove the noise signal from the original point cloud data for filtering.

The more specific implementation manner of each module of the single photon laser radar spatial transform filtering device 1400 may refer to the description of the single photon laser radar spatial transform filtering method of the present invention, and has similar beneficial effects, and is not described herein again.

An embodiment of the fifth aspect of the invention proposes a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method for noise determination for single photon lidar spatial transformation according to the first aspect of the invention or implements the method for filtering for single photon lidar spatial transformation according to the third aspect of the invention.

Generally, computer instructions for carrying out the methods of the present invention may be carried using any combination of one or more computer-readable storage media. Non-transitory computer readable storage media may include any computer readable medium except for the signal itself, which is temporarily propagating.

A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages, and in particular may employ Python languages suitable for neural network computing and TensorFlow, PyTorch-based platform frameworks. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

An embodiment of a sixth aspect of the present invention provides a computing device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the method for determining noise in spatial transform of single photon lidar according to the first aspect of the present invention or implements the method for filtering spatial transform of single photon lidar according to the third aspect of the present invention when executing the program.

The non-transitory computer-readable storage medium and the computing device according to the fifth and sixth aspects of the present invention may be implemented with reference to the contents specifically described in the embodiments of the first aspect or the third aspect of the present invention, and have similar beneficial effects to the method for determining noise in spatial transform of single photon laser radar according to the embodiments of the first aspect of the present invention or the method for filtering spatial transform of single photon laser radar according to the embodiments of the third aspect of the present invention, and are not described herein again.

FIG. 15 illustrates a block diagram of an exemplary computing device suitable for use to implement embodiments of the present disclosure. The computing device 12 shown in FIG. 15 is only one example and should not impose any limitations on the functionality or scope of use of embodiments of the disclosure.

As shown in FIG. 15, computing device 12 may be implemented in the form of a general purpose computing device. Components of computing device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.

Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. These architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus, to name a few.

Computing device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computing device 12 and includes both volatile and nonvolatile media, removable and non-removable media.

The system Memory 28 may include computer system readable media in the form of volatile Memory, such as Random Access Memory (RAM) 30 and/or cache Memory 32. Computing device 12 may further include other removable/non-removable, volatile/nonvolatile computer-readable storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown, but commonly referred to as a "hard drive"). Although not shown in FIG. 15, a disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a Compact disk Read Only Memory (CD-ROM), a Digital versatile disk Read Only Memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the disclosure.

A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally perform the functions and/or methodologies of the embodiments described in this disclosure.

Computing device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with computing device 12, and/or with any devices (e.g., network card, modem, etc.) that enable computing device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Moreover, computing device 12 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public Network such as the Internet via Network adapter 20. As shown, network adapter 20 communicates with the other modules of computing device 12 via bus 18. It is noted that although not shown, other hardware and/or software modules may be used in conjunction with computing device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, disk drives, and data backup storage systems, among others.

The processing unit 16 executes various functional applications and data processing, for example, implementing the methods mentioned in the foregoing embodiments, by executing programs stored in the system memory 28.

The computing device of the invention can be a server or a terminal device with limited computing power.

Although embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are illustrative and not to be construed as limiting the present invention, and that changes, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art within the scope of the present invention.