CN113805157A - Height measuring method, device and equipment based on target - Google Patents

Height measuring method, device and equipment based on target Download PDF

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CN113805157A
CN113805157A CN202111106924.6A CN202111106924A CN113805157A CN 113805157 A CN113805157 A CN 113805157A CN 202111106924 A CN202111106924 A CN 202111106924A CN 113805157 A CN113805157 A CN 113805157A
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point cloud
target
normal vector
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周正乾
曹凯明
周望
徐爱国
浦凯文
贡文韬
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Aerospace New Weather Technology Co ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
    • G01S17/06—Systems determining position data of a target
    • G01S17/08—Systems determining position data of a target for measuring distance only
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
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Abstract

The invention relates to the field of crop height measurement, in particular to a target-based height measurement method, a target-based height measurement device and target-based height measurement equipment, which comprise the following steps: acquiring a three-dimensional point cloud containing a point cloud of an object to be measured; wherein, the three-dimensional point cloud is provided with a target point cloud; calculating a target normal vector of a plane where a target point cloud is located, and calculating to obtain a three-dimensional rotation matrix based on the target normal vector, a ground normal vector and an included angle between the target normal vector and the ground normal vector; and adjusting the three-dimensional point cloud according to the three-dimensional rotation matrix, and measuring by using the adjusted three-dimensional point cloud to obtain the height of the object to be measured. And the height measurement is carried out by utilizing the three-dimensional point cloud after fine adjustment, so that the measurement deviation caused by errors existing in manual measurement is effectively reduced.

Description

Height measuring method, device and equipment based on target
Technical Field
The invention relates to the field of crop height measurement, in particular to a target-based height measurement method, device and equipment.
Background
Laser radar (LiDAR) has been increasingly used in object detection, which can emit laser pulses and determine the distance of an object to itself by calculating the time interval from emission to return, thereby obtaining high density three-dimensional point cloud data. The three-dimensional point cloud data acquired by the laser radar can better describe the outline and the height of the crop canopy, and is hardly influenced by environmental factors such as solar radiation, air temperature and humidity, background temperature, illumination change and the like, so that the method has a better application prospect in the detection of the height of the crop canopy.
The height measurement needs to obtain the installation height of the laser radar and the included angle between the laser radar and the horizontal plane when the laser radar inclines, but in the installation process, the situation that the installation rod of the radar is not completely vertical and the included angle between the radar and the horizontal plane when the radar is inclined through manual measurement deviates inevitably occurs, and finally, a large error occurs in a measurement result.
Disclosure of Invention
Therefore, the invention aims to solve the technical problem that in the installation process, the installation rod of a radar may not be completely vertical, and the angle between the radar and the horizontal plane is deviated when the radar is inclined through manual measurement, which finally causes a large error in the measurement result, thereby providing a target-based height measurement method, which comprises the following steps:
acquiring a three-dimensional point cloud containing a point cloud of an object to be measured; wherein a target point cloud is in the three-dimensional point cloud;
calculating a target normal vector of a plane where the target point cloud is located, and calculating to obtain a three-dimensional rotation matrix based on the target normal vector, a ground normal vector and an included angle between the target normal vector and the ground normal vector;
and adjusting the three-dimensional point cloud according to the three-dimensional rotation matrix, and measuring by using the adjusted three-dimensional point cloud to obtain the height of the object to be measured.
Preferably, the calculating a target normal vector of a plane where the target point cloud is located includes:
acquiring point cloud data of all points in the target point cloud to obtain n point cloud coordinates; wherein the point cloud coordinates are three-dimensional vectors;
obtaining three rows and three columns of matrixes based on the number of point cloud data in the target point cloud and the dimension values in the point cloud coordinates;
and calculating the minimum eigenvector of the three-row three-column matrix, and taking the minimum eigenvector as the normal vector of the target.
Preferably, the n point cloud coordinates of the target point cloud are { (x)i,yi,zi) I ═ 1,2, …, n }; obtaining the three-row three-column matrix through a first mathematical model, wherein the first mathematical model is as follows:
Figure BDA0003272796980000021
in the formula,. DELTA.xiDenotes xiDifference from the mean value of the x dimension, Δ yiDenotes yiDifference from the mean value of the y dimension, Δ ziDenotes ziDifference from the average of the z dimension.
Preferably, the method further comprises: carrying out different sample point processing on the point cloud data of the target;
and after the different sample points are eliminated, calculating to obtain the target normal vector based on the rest point cloud data.
Preferably, the n point cloud coordinates of the target point cloud are { (x)i,yi,zi) 1,2, …, n, the target normal vector being (a, b, c); the point cloud data of the target is subjected to different sampling point processing, and the method comprises the following steps:
and solving the distance between each point cloud coordinate and a plane formed by the n point clouds by using a third mathematical model, wherein the third mathematical model is as follows:
Figure BDA0003272796980000031
in the formula (I), the compound is shown in the specification,
Figure BDA0003272796980000032
the average value of the x dimension is represented,
Figure BDA0003272796980000033
the average value in the y dimension is shown,
Figure BDA0003272796980000034
represents the average of the z dimension;
calculating to obtain a standard deviation by using the distances between all point cloud coordinates and a plane formed by the n point clouds;
and if the distance is greater than the standard deviation of a preset multiple, carrying out different sample point processing on the point cloud corresponding to the distance.
Preferably, the three-dimensional rotation matrix is obtained by calculation through a fourth mathematical model, wherein the fourth mathematical model is as follows:
R＝I+sinθ*A+(1-cosθ)A2
in the formula, I is represented by
Figure BDA0003272796980000035
A is expressed as an antisymmetric matrix of a cross product of the normal vector of the target and the normal vector of the ground, theta is expressed as an included angle between the normal vector of the target and the normal vector of the ground, and the normal vector of the ground is | 100 |.
Preferably, before calculating the target normal vector of the plane where the target point cloud is located, the method further includes: extracting the target point cloud from the three-dimensional point cloud;
and carrying out three-dimensional clustering on the target point cloud, and filtering the target point cloud through reflectivity to obtain the processed target point cloud.
Preferably, said adjusting said three-dimensional point cloud according to said three-dimensional rotation matrix comprises:
and multiplying the three-dimensional rotation matrix with the point cloud coordinates of the three-dimensional point cloud to obtain the adjusted three-dimensional point cloud.
The invention also provides a height measuring device based on the target, which comprises:
the acquisition module is used for acquiring a three-dimensional point cloud containing a point cloud of an object to be measured; wherein a target point cloud is in the three-dimensional point cloud;
the calculation module is used for calculating a target normal vector of a plane where the target point cloud is located, and calculating to obtain a three-dimensional rotation matrix based on the target normal vector, a ground normal vector and an included angle between the target normal vector and the ground normal vector;
and the measuring module is used for adjusting the three-dimensional point cloud according to the three-dimensional rotation matrix and measuring by using the adjusted three-dimensional point cloud to obtain the height of the object to be measured.
The present invention also provides a computer device comprising: the target-based height measuring device comprises a memory and a processor, wherein the memory and the processor are mutually connected in a communication mode, the memory stores computer instructions, and the processor executes the computer instructions so as to execute the target-based height measuring method.
The present invention also provides a computer readable storage medium having stored thereon computer instructions for causing the computer to perform the above-described target-based height measurement method.
The technical scheme of the invention has the following advantages:
1. the target-based height measurement method provided by the invention is used for calculating the target point cloud in the three-dimensional point cloud to obtain the target normal vector, calculating by using the target normal vector, the ground normal vector and an included angle formed by the target normal vector and the ground normal vector to obtain the three-dimensional rotation matrix, finely adjusting the three-dimensional point cloud by using the three-dimensional rotation matrix, and measuring the height of an object to be measured in the adjusted three-dimensional point cloud. And the height measurement is carried out by utilizing the three-dimensional point cloud after fine adjustment, so that the measurement deviation caused by errors existing in manual measurement is effectively reduced.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a target-based height measurement method according to embodiment 1 of the present invention;
FIG. 2 is a flowchart of calculating a target normal vector in embodiment 1 of the present invention;
FIG. 3 is a flowchart of removing outliers in example 1 of the present invention;
fig. 4 is a scene diagram of point cloud data acquisition in embodiment 1 of the present invention;
FIG. 5 is a diagram of an original three-dimensional point cloud and an adjusted three-dimensional point cloud in embodiment 1 of the present invention;
FIG. 6 is a block diagram of a target-based height measuring device according to embodiment 2 of the present invention;
fig. 7 is a schematic block diagram of a computer device according to embodiment 3 of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "first", "second", and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Laser radar, ultrasonic radar and the like are increasingly applied to object detection, three-dimensional point cloud data formed by scanning an object can well describe the height of the object, are hardly influenced by environmental factors such as solar radiation, air temperature and humidity, background temperature and illumination change and are increasingly emphasized in object detection.
As shown in fig. 4, when the radar 105 scans the object to be measured 102 to form a three-dimensional point cloud, a signal is emitted from a high position to a low position, and the resulting original three-dimensional point cloud is in an inclined state. The original three-dimensional point cloud needs to be adjusted by means of the installation height 106 of the radar 105 and the included angle 107 formed by the radar 105 when the radar 105 inclines with the horizontal plane, so that the subsequent height measurement of the object to be measured 102 is facilitated. As shown in fig. 5, the first point cloud image 201 is an original three-dimensional point cloud obtained by scanning the object to be measured 102 by the radar 105, and the second point cloud image 202 is an adjusted three-dimensional point cloud, and the second point cloud image 202 is used for subsequent operations.
However, during the installation and measurement process, it is inevitable that the mounting rod (not shown) of the radar is not perfectly vertical to the ground 101, and the measured angle 107 formed by the radar 105 and the horizontal plane deviates, which finally results in a large error in the measurement result of the object to be measured 102.
The object 102 may be a crop, a building, a tree, or the like, and the radar 105 may be a LiDAR (ultrasonic radar), an ultrasonic radar, or the like.
Example 1
Fig. 1 is a flow chart illustrating height measurement of an object to be measured based on a target to improve measurement accuracy according to some embodiments of the present invention. Although the processes described below include operations that occur in a particular order, it should be clearly understood that the processes may include more or fewer operations that are performed sequentially or in parallel (e.g., using parallel processors or a multi-threaded environment).
The embodiment provides a target-based height measurement method for measuring the height of an object to be measured, as shown in fig. 1, including the following steps:
s101, acquiring a three-dimensional point cloud containing a point cloud of an object to be measured; wherein, there is the target point cloud in the three-dimensional point cloud.
In the above implementation steps, the three-dimensional point cloud includes a point cloud of the object to be measured and a point cloud of the target. The three-dimensional point cloud is obtained by scanning of a radar, and is adjusted by the installation height of the radar and the included angle formed by the radar and a horizontal plane when the radar inclines. As shown in fig. 4, the target 103 is fixed to a vertical rod 104, and the length of the vertical rod 104 can be adjusted according to the height of the object 102 to be measured. On the shaft of the pole 104 there is a scale indicating the height to obtain the distance from the top of the target 103 to the ground 101.
Before the radar 105 scans to form a three-dimensional point cloud, the upright 104 is inserted on the ground within the radar detection range, the target 103 is perpendicular to a ground plane formed by the ground 101, and an installer can ensure that the target 103 is relatively perpendicular to the ground 101 when installed through a level gauge. The target 103 is usually installed in a direction close to the radar 105, and the height of the target 103 should be higher than the height of the measured object 102, so as to ensure that the point cloud formed by the target 103 does not interfere with the measured object, and facilitate subsequent point cloud extraction on the target 103. For example, when measuring the height of a crop, it is desirable to select a height above ground at which the crop is not too high, to ensure that there is no point cloud of interference from the crop within a certain range above and below the target 103. The target 103 may be white and made of a material that reflects good radar signals.
S102, calculating a target normal vector of a plane where the target point cloud is located, and calculating to obtain a three-dimensional rotation matrix based on the target normal vector, a ground normal vector and an included angle between the target normal vector and the ground normal vector.
In the above implementation steps, the target normal vector and the ground normal vector are both three-dimensional vectors, wherein the ground normal vector is a normal vector of the ground, and the numerical value of the ground normal vector is not affected by the inclination angle of the three-dimensional point cloud. And calculating by utilizing the normal vector of the target, the normal vector of the ground and an included angle between the normal vector of the target and the normal vector of the ground to obtain a three-dimensional rotation matrix, wherein the three-dimensional rotation matrix can be used for carrying out angle fine adjustment on the three-dimensional point cloud.
For example, as shown in fig. 4, the radar 105 scans to obtain an original three-dimensional point cloud, and the original three-dimensional point cloud is adjusted by using the installation height 106 of the radar 105 and the included angle 107 formed by the radar 105 when it is inclined with respect to the horizontal plane, so as to obtain an adjusted three-dimensional point cloud. And calculating a target normal vector of the target 103 in the adjusted three-dimensional point cloud, and calculating to obtain a three-dimensional rotation matrix by using the target normal vector, the ground normal vector and an included angle formed by the target normal vector and the ground normal vector.
S103, adjusting the three-dimensional point cloud according to the three-dimensional rotation matrix, and measuring by using the adjusted three-dimensional point cloud to obtain the height of the object to be measured.
In the implementation step, the three-dimensional point cloud is adjusted by using the three-dimensional rotation matrix to obtain the adjusted three-dimensional point cloud, and the height of the object to be measured is measured by using the adjusted three-dimensional point cloud. It should be noted that the height of the object to be measured in the three-dimensional point cloud can be measured by using the prior art, and is not described herein too much.
In one or more embodiments, the adjusted three-dimensional point cloud may be obtained by multiplying the three-dimensional rotation matrix with the point cloud coordinates of the three-dimensional point cloud. In some embodiments, some of the adjusted point cloud coordinates may be adaptively adjusted according to actual conditions.
In the above embodiment, the target point cloud in the three-dimensional point cloud is calculated to obtain the target normal vector, the ground normal vector and an included angle formed by the target normal vector and the ground normal vector are used to calculate to obtain the three-dimensional rotation matrix, the three-dimensional rotation matrix is used to perform fine adjustment on the three-dimensional point cloud, and the height of the object to be measured in the adjusted three-dimensional point cloud is measured. And the height measurement is carried out by utilizing the three-dimensional point cloud after fine adjustment, so that the measurement deviation caused by errors existing in manual measurement is effectively reduced.
In one or more embodiments, as shown in fig. 2, calculating the target normal vector of the plane where the target point cloud is located may include the following steps:
s201, acquiring point cloud data of all points in the target point cloud to obtain n point cloud coordinates; wherein the point cloud coordinates are three-dimensional vectors.
In the above implementation steps, the target point cloud includes n point clouds, and all the point clouds coordinates can be represented by three-dimensional vectors, for example, n point cloud coordinates of the target point cloud are { (x)i,yi,zi),i＝1,2,…,n}。
S202, obtaining three rows and three columns of matrixes based on the number of point cloud data in the target point cloud and the dimension values in the point cloud coordinates.
In the above implementation steps, the number of point cloud data in the target point cloud is the number of point cloud coordinates, that is, if there are n point cloud coordinates, there are n point cloud data in the target point cloud. The point cloud coordinate is a three-dimensional vector and represents that the point cloud coordinate has three dimensions, and numerical values in the dimensions are dimension values.
For example, if the point cloud coordinate is (1,2,3), it means that the dimension value of the x dimension in the point cloud coordinate is 1, the dimension value of the y dimension is 2, and the dimension value of the z dimension is 3.
The three-row three-column matrix may be derived from a first mathematical model, which is:
Figure BDA0003272796980000091
in the formula,. DELTA.xiDenotes xiDifference from the mean value of the x dimension, Δ yiDenotes yiDifference from the mean value of the y dimension, Δ ziDenotes ziDifference from the average of the z dimension. The average value of the x dimension represents the average value of the x dimension values in all the point cloud coordinates, the average value of the y dimension represents the average value of the y dimension values in all the point cloud coordinates, and the average value of the z dimension represents the average value of the z dimension values in all the point cloud coordinates.
The first mathematical model is derived as follows:
assuming that point cloud data of a plane is obtained, n data coordinate points { (x)i,yi,zi) And i is 1,2, …, n }, the data coordinate point (x)i,yi,zi) The distance to the plane is:
di＝|axi+bxi+cxi-d|
the fitting plane is to satisfy
Figure BDA0003272796980000101
The function is obtained by utilizing a Lagrange multiplier method for solving the extreme value of the function:
Figure BDA0003272796980000102
and (3) respectively solving partial derivatives of a, b and c in the formula (1) to obtain a first mathematical model. Therefore, the solution of the target normal vector is converted into a matrix solution eigenvector.
S203, solving the minimum eigenvector of the three-row three-column matrix, and taking the minimum eigenvector as the normal vector of the target.
In the implementation step, the three rows and three columns of the matrix are calculated to obtain the minimum eigenvector, wherein the minimum eigenvector is a three-dimensional vector, and the obtained minimum eigenvector is the normal vector of the target.
Because some abnormal data points exist in the point cloud scanned by the radar and the target normal vector is calculated and affected by the abnormal values, the obtained target normal vector has certain deviation, and therefore the abnormal data points need to be processed to ensure the adjusted effect. In one or more embodiments, as shown in FIG. 3, the clearing of outlier data points includes the steps of:
s301, after the standard normal vector of the target point cloud is obtained, if the difference value between the target normal vector and the standard normal vector exceeds a preset error, carrying out different-sample point processing on the point cloud data of the target.
In the above implementation steps, a standard vector of the target point cloud is obtained according to the point cloud data on the target point cloud, and the standard vector is a three-dimensional vector and can be represented as (a, B, C). The standard normal vector is preset by the system according to the data of the target point cloud. The spatial plane formed by the target point cloud can be represented as:
Ax+By+Cz＝d
in the formula, A2+B2+C21, d is the distance from the origin of coordinates to the plane of space.
The standard normal vector can be (a, B, C), the target normal vector can be (a, B, C), if the difference between the target normal vector and the standard normal vector exceeds a preset error, it indicates that there is a different sample point (i.e. an abnormal data point), and the point cloud data of the target needs to be processed, and the different sample point is deleted. If the difference value between the target normal vector and the standard normal vector does not exceed the preset error, the fact that no different sampling points exist is indicated, and the target normal vector can be directly used for subsequent calculation. It should be noted that, a person skilled in the art can reasonably select the preset error according to actual situations, and the preset error is not limited herein.
The n point cloud coordinates of the target point cloud are { (x)i,yi,zi) I ═ 1,2, …, n }, the target normal vector may be (a, b, c), which is calculated using n point cloud coordinates. The point cloud data of the target is subjected to different sample point processing, and the method can be carried out through the following steps:
a. and solving the distance between each point cloud coordinate and a plane formed by the n point clouds by using a third mathematical model, wherein the third mathematical model is as follows:
Figure BDA0003272796980000111
in the formula (I), the compound is shown in the specification,
Figure BDA0003272796980000112
the average value of the x dimension is represented,
Figure BDA0003272796980000113
the average value in the y dimension is shown,
Figure BDA0003272796980000114
represents the average of the z dimension. The average value of the x dimension is the average value of the x dimension values in all the point cloud coordinates, the average value of the y dimension is the average value of the y dimension values in all the point cloud coordinates, and the average value of the z dimension is the average value of the z dimension values in all the point cloud coordinates.
b. And calculating to obtain the standard deviation by using the distances between all the point cloud coordinates and a plane formed by the n point clouds. Wherein the standard deviation can be obtained by the following formula:
Figure BDA0003272796980000121
in the formula (I), the compound is shown in the specification,
Figure BDA0003272796980000122
representing the distance d between all point cloud coordinates and the plane formed by the n point cloudsiAverage value of (a).
c. And if the distance is greater than the standard deviation of a preset multiple, carrying out different sample point processing on the point cloud corresponding to the distance. Wherein the preset multiple can be 2 times, 3 times or 4 times and the like.
For example, if the distance between the point cloud a and the plane formed by the n point clouds is 0.634, the standard deviation is 0.177, and the preset multiple is 2 times, the point cloud a is indicated as a heterogeneous point, and the point cloud a is deleted; if the distance between the point cloud A and the plane formed by the n point clouds is 0.202, the point cloud A is not a heterogeneous point.
And S302, after the different sampling points are eliminated, calculating to obtain the target normal vector based on the rest point cloud data.
In the above implementation steps, the unremoved point cloud is used for calculation to obtain the target normal vector, the target normal vector obtained by recalculation can be compared with the standard normal vector, if the difference between the target normal vector obtained by recalculation and the standard normal vector exceeds the preset error, the different-sample point processing is performed again to delete the different-sample point, that is, step S302 and step S303 are performed again.
It should be noted that, if the difference between the calculated target normal vector and the standard normal vector does not exceed the preset error, it indicates that the target normal vector can represent the standard normal vector more accurately.
In actual operation, after a target normal vector is obtained, point cloud data of the target can be directly subjected to different-sample point processing to judge whether different-sample points exist in the point cloud data, and if the different-sample points exist, the different-sample points are eliminated; and if no different sample points exist, performing subsequent calculation by using the normal vector of the target.
In one or more embodiments, the three-dimensional rotation matrix is calculated by a fourth mathematical model, the fourth mathematical model being:
R＝I+sinθA+(1-cosθ)A2
in the formula, I is represented by
Figure BDA0003272796980000131
A is expressed as an antisymmetric matrix of a cross product of the normal vector of the target and the normal vector of the ground, theta is expressed as an included angle between the normal vector of the target and the normal vector of the ground, and the normal vector of the ground is | 100 |. It should be noted that, in order to adapt to different conditions, the person skilled in the art may adapt the fourth mathematical model, for example, add an adjustment parameter.
In one or more embodiments, before calculating the target normal vector of the plane where the target point cloud is located, the method further includes:
extracting the target point cloud from the three-dimensional point cloud; and carrying out three-dimensional clustering on the target point cloud, and filtering the target point cloud through reflectivity to obtain the processed target point cloud.
After removing the abnormal values which are obvious in height, the target point cloud can be roughly extracted through the height (which refers to the z-dimensional value of the point cloud), then three-dimensional DBSCAN clustering is carried out on the target point cloud, the obvious abnormal point cloud is deleted through filtering reflectivity information (the emissivity of the measured object and the target to radar signals are different), and finally more accurate target point cloud is obtained. The target normal vector is calculated by using more accurate target point cloud, so that the three-dimensional point cloud obtained by final adjustment is more accurate, and the height measurement precision is improved.
The method for extracting the target through the spatial clustering and the reflectivity information has the advantages of high accuracy and robustness, the whole calibration scheme is convenient to operate, and the identification accuracy is improved after calibration fine adjustment.
For example, the target point cloud includes 11 point cloud coordinates: (1.02, -0.01,1.01), (2.02,0.09,0.01), (2.01,0.09,0.01), (1.99, -1.01,0.01), (1.98, -1.01,0.01), (-0.01,1.01,2), (-0.01,1.01,1.99), (0.01, -0.99,2), (0.01, -0.99,2.01), (1.0, -0.01,1.01), and (1.5,0, 1.5).
Substituting the 11 point cloud coordinates into the first mathematical model to obtain a three-row three-column matrix:
Figure BDA0003272796980000141
solving the obtained three-row three-column matrix to obtain a matrix A33The minimum feature vector of (a) is: (0.70248578, -0.02082733,0.71139296), i.e., the target normal vector of the target point cloud is (0.702, -0.02, 0.711).
Substituting the coordinates of the 11 point clouds into the third mathematical model, finding the distance between each point cloud and the plane formed by the 11 point clouds, and calculating the standard deviation to be 0.1733. The distance between the point cloud coordinates (1.5,0,1.5) and the plane formed by 11 point clouds, which are outliers, was found to be 0.634, greater than 2 times the standard deviation, with the point cloud coordinate (1.5,0,1.5) removed.
Example 2
The present embodiment provides a target-based height measuring device for measuring the height of an object to be measured, as shown in fig. 6, including:
an obtaining module 301, configured to obtain a three-dimensional point cloud including a point cloud of an object to be measured; wherein a target point cloud is in the three-dimensional point cloud; for details, please refer to the related description of step S101 in embodiment 1, which is not repeated herein.
The calculating module 302 is configured to calculate a target normal vector of a plane where the target point cloud is located, and calculate a three-dimensional rotation matrix based on the target normal vector, a ground normal vector and an included angle between the target normal vector and the ground normal vector; for details, please refer to the related description of step S102 in embodiment 1, which is not repeated herein.
And the measuring module 303 is configured to adjust the three-dimensional point cloud according to the three-dimensional rotation matrix, and measure the height of the object to be measured by using the adjusted three-dimensional point cloud. For details, please refer to the related description of step S103 in embodiment 1, which is not repeated herein.
In the above embodiment, the calculation module 302 calculates the target point cloud in the three-dimensional point cloud acquired by the acquisition module 301 to obtain the target normal vector, and calculates the three-dimensional rotation matrix by using the target normal vector, the ground normal vector and the included angle formed by the target normal vector and the ground normal vector, and the measurement module 303 finely adjusts the three-dimensional point cloud by using the three-dimensional rotation matrix, and performs height measurement on the object to be measured in the adjusted three-dimensional point cloud. And the height measurement is carried out by utilizing the three-dimensional point cloud after fine adjustment, so that the measurement deviation caused by errors existing in manual measurement is effectively reduced.
Example 3
The present embodiment provides a computer device, as shown in fig. 7, the computer device includes a processor 401 and a memory 402, where the processor 401 and the memory 402 may be connected by a bus or by other means, and fig. 7 takes the connection by the bus as an example.
Processor 401 may be a Central Processing Unit (CPU). The Processor 401 may also be other general purpose processors, Digital Signal Processors (DSPs), Graphics Processing Units (GPUs), embedded Neural Network Processors (NPUs), or other dedicated deep learning coprocessors, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or any combination thereof.
The memory 402, which is a non-transitory computer-readable storage medium, may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the target-based height measurement method in the embodiment of the present invention (e.g., the acquiring module 301, the calculating module 302, and the measuring module 303 shown in fig. 6). The processor 401 executes various functional applications and data processing of the processor by running non-transitory software programs, instructions and modules stored in the memory 402, that is, implements the target-based height measuring method in the above method embodiment 1.
The memory 402 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 401, and the like. Further, the memory 402 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 402 may optionally include memory located remotely from processor 401, which may be connected to processor 401 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 402 and, when executed by the processor 401, perform a target-based height measurement method as in the embodiment of fig. 1.
In this embodiment, the memory 402 stores a program instruction or a module of a target-based height measurement method, and when the processor 401 executes the program instruction or the module stored in the memory 402, the processor 401 calculates a target point cloud in a three-dimensional point cloud to obtain a target normal vector, calculates a three-dimensional rotation matrix by using the target normal vector, a ground normal vector, and an included angle formed by the target normal vector and the ground normal vector, fine-tunes the three-dimensional point cloud by using the three-dimensional rotation matrix, and performs height measurement on an object to be measured in the adjusted three-dimensional point cloud. And the height measurement is carried out by utilizing the three-dimensional point cloud after fine adjustment, so that the measurement deviation caused by errors existing in manual measurement is effectively reduced.
Embodiments of the present invention further provide a computer-readable storage medium, where computer-executable instructions are stored, and the computer-executable instructions may execute the target-based height measurement method in any of the above method embodiments. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.

Claims (11)

1. A target-based height measurement method is characterized by comprising the following steps:
acquiring a three-dimensional point cloud containing a point cloud of an object to be measured; wherein a target point cloud is in the three-dimensional point cloud;
calculating a target normal vector of a plane where the target point cloud is located, and calculating to obtain a three-dimensional rotation matrix based on the target normal vector, a ground normal vector and an included angle between the target normal vector and the ground normal vector;
and adjusting the three-dimensional point cloud according to the three-dimensional rotation matrix, and measuring by using the adjusted three-dimensional point cloud to obtain the height of the object to be measured.
2. The target-based height measurement method of claim 1, wherein calculating the target normal vector for the plane of the target point cloud comprises:
acquiring point cloud data of all points in the target point cloud to obtain n point cloud coordinates; wherein the point cloud coordinates are three-dimensional vectors;
obtaining three rows and three columns of matrixes based on the number of point cloud data in the target point cloud and the dimension values in the point cloud coordinates;
and calculating the minimum eigenvector of the three-row three-column matrix, and taking the minimum eigenvector as the normal vector of the target.
3. The target-based height measurement method of claim 2, whereinThen, the n point cloud coordinates of the target point cloud are { (x)i,yi,zi) I ═ 1,2, …, n }; obtaining the three-row three-column matrix through a first mathematical model, wherein the first mathematical model is as follows:
Figure FDA0003272796970000021
in the formula,. DELTA.xiDenotes xiDifference from the mean value of the x dimension, Δ yiDenotes yiDifference from the mean value of the y dimension, Δ ziDenotes ziDifference from the average of the z dimension.
4. A target-based height measurement method according to claim 2 or 3, further comprising:
carrying out different sample point processing on the point cloud data of the target;
and after the different sample points are eliminated, calculating to obtain the target normal vector based on the rest point cloud data.
5. The target-based height measurement method of claim 4, wherein the n point cloud coordinates of the target point cloud are { (x)i,yi,zi) 1,2, …, n, the target normal vector being (a, b, c); the point cloud data of the target is subjected to different sampling point processing, and the method comprises the following steps:
and solving the distance between each point cloud coordinate and a plane formed by the n point clouds by using a third mathematical model, wherein the third mathematical model is as follows:
Figure FDA0003272796970000022
in the formula (I), the compound is shown in the specification,
Figure FDA0003272796970000023
the average value of the x dimension is represented,
Figure FDA0003272796970000024
the average value in the y dimension is shown,
Figure FDA0003272796970000025
represents the average of the z dimension;
calculating to obtain a standard deviation by using the distances between all point cloud coordinates and a plane formed by the n point clouds;
and if the distance is greater than the standard deviation of a preset multiple, carrying out different sample point processing on the point cloud corresponding to the distance.
6. The target-based height measurement method of any one of claims 1-5, wherein the three-dimensional rotation matrix is calculated by a fourth mathematical model, the fourth mathematical model being:
R＝I+sinθA+(1-cosθ)A2
in the formula, I is represented by
Figure FDA0003272796970000031
A is expressed as an antisymmetric matrix of a cross product of the normal vector of the target and the normal vector of the ground, theta is expressed as an included angle between the normal vector of the target and the normal vector of the ground, and the normal vector of the ground is | 100 |.
7. The target-based height measurement method of any one of claims 1-6, further comprising, prior to calculating the target normal vector for the plane of the target point cloud:
extracting the target point cloud from the three-dimensional point cloud;
and carrying out three-dimensional clustering on the target point cloud, and filtering the target point cloud through reflectivity to obtain the processed target point cloud.
8. The target-based height measurement method of any one of claims 1-7, wherein said adjusting the three-dimensional point cloud according to the three-dimensional rotation matrix comprises:
and multiplying the three-dimensional rotation matrix with the point cloud coordinates of the three-dimensional point cloud to obtain the adjusted three-dimensional point cloud.
9. A target-based height measuring device, comprising:
the acquisition module is used for acquiring a three-dimensional point cloud containing a point cloud of an object to be measured; wherein a target point cloud is in the three-dimensional point cloud;
the calculation module is used for calculating a target normal vector of a plane where the target point cloud is located, and calculating to obtain a three-dimensional rotation matrix based on the target normal vector, a ground normal vector and an included angle between the target normal vector and the ground normal vector;
and the measuring module is used for adjusting the three-dimensional point cloud according to the three-dimensional rotation matrix and measuring by using the adjusted three-dimensional point cloud to obtain the height of the object to be measured.
10. A computer device, comprising: a memory and a processor communicatively coupled to each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the target-based height measurement method of any of claims 1-8.
11. A computer-readable storage medium having stored thereon computer instructions for causing a computer to perform the target-based height measurement method of any one of claims 1-8.
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