WO2022141055A1 - Method and system for measuring volume of grain in granary, electronic device and storage medium - Google Patents

Abstract

A method and system (1000) for measuring the volume of grain in a granary, an electronic device (900), and a storage medium. The method comprises: calculating first IMU data and second IMU data to obtain a corresponding first rotation matrix and second rotation matrix (S10); respectively obtaining first point cloud data and second point cloud data, and obtaining first coordinates P1' with respect to the granary (S11); determining whether the first coordinates P1' and second coordinates P2' are in the granary, deleting noise data outside the granary, and combining and registering the remaining first coordinates P1' and second coordinates P2' to generate a grain simulation map (S12); and dividing the grain simulation map into several grids, and summing the volume of all the grids to obtain the volume of the grain in the granary (S13). During the measurement and calculation processes, the first IMU data and the second IMU data are used, and the first rotation matrix and the second rotation matrix are converted into an overall coordinate system of the granary which has higher accuracy and smaller error, and can calculate the volume of the granary more accurately compared with one-dimensional linear scanning.

Description

Method, system, electronic device and storage medium for measuring grain volume in granary technical field

The invention relates to the technical field of laser radar, in particular to a method, system, electronic device and storage medium for measuring grain volume in a granary.

Background technique

The full name of Lidar in English is Light Detection And Ranging, or LiDAR for short, that is, light detection and measurement. It is a system that integrates three technologies: laser, global positioning system (GPS) and IMU (Inertial Measurement Unit). Used to acquire data and generate accurate DEMs (Digital Elevation Models). The combination of these three technologies can highly accurately locate the spot of the laser beam hitting the object, and the ranging accuracy can reach centimeter level. Based on this advantage, lidar is widely used in 3D reconstruction, vehicle-road collaboration and other application scenarios.

At present, the Internet of Things technology has been widely used in the granary. For example, the temperature sensor deployed in the multi-layer grid is used to monitor the temperature of the granary in real time, and the high-definition camera is used to monitor the image of the granary. At present, there are also methods of using cameras or infrared rays to detect the volume of grain in the granary, but these methods all collect the height of several points on the edge of the granary, which approximates that the granary is in a flat state, with low precision, and requires the installation of guide rails in the granary, and the installation process is complicated.

Another example is the invention patent with the application number "CN201210224186.X" which discloses a method for measuring the volume of large irregular bulk grain piles based on dynamic three-dimensional laser scanning. ; There is a slider controlled by a stepping motor on the guide rail, and the lidar scanner is installed on the slider; the slider moves from one end of the top of the granary to the other end at a constant speed, and the slider drives the lidar scanner to scan the entire surface of the bulk grain pile ; The laser radar device is a one-dimensional scanning device, which realizes line scanning and returns coordinate data; the main control computer is used to transmit pulses to the stepping motor and process the signals; the main control computer obtains the point cloud data on the surface of the bulk grain pile, according to the user For the allowable error value of the grain pile measurement, determine the distribution density of the scanned points used to calculate the volume, and calculate the bulk grain pile weight according to the grain density provided by the user. The patented solution is relatively complex, requires the use of guide rails, and has poor calculation accuracy.

SUMMARY OF THE INVENTION

The technical problem to be solved by the present invention is to overcome the problem of poor volume measurement accuracy of the existing granary.

The present invention realizes and solves the above-mentioned technical problems through the following technical means:

A method for measuring grain volume in a granary, comprising:

Receive the first IMU data and the first position data, the second IMU data and the second position data, and calculate the first IMU data and the second IMU data respectively to obtain the corresponding first rotation matrix and second rotation matrix;

Wherein, the first position data is the coordinates (px1, py1, pz1) of the grain relative to the first radar device 1, and the second position data is the coordinates (px2, py2, pz2) of the grain relative to the second lidar device;

Obtain the first point cloud data and the second point cloud data respectively, and obtain the first coordinate P1' relative to the granary through the first rotation matrix, the first position data and the first point cloud data. The position data and the second point cloud data obtain the second coordinate P2 relative to the granary;

Determine whether the first coordinate P1' and the second coordinate P2' are located in the granary, exclude the coordinates outside the granary, delete the noise data in the first point cloud data and the second point cloud data, and the remaining first coordinate P1 ', the second coordinate P2' is combined and registered to generate a grain simulation map;

Divide the grain simulation graph into several grids, and sum up the volumes of all grids to obtain the volume of all grains in the granary.

The first IMU data and the second IMU data are used in the measurement and calculation process. After the first rotation matrix and the second rotation matrix are calculated, the relative data is obtained through the first rotation matrix, the first position data and the first point cloud data. For the first coordinate P1' of the granary, the second coordinate P2' relative to the granary is obtained through the second rotation matrix, the second position data and the second point cloud data (that is, the point is converted from the coordinate system of the grain relative to the radar to the grain relative coordinate system). Based on the overall coordinate system of the granary), according to the first coordinate P1', the second coordinate P2' combined and registered to generate the grain simulation map to obtain the grain volume, which has higher accuracy and smaller error than one-dimensional linear scanning, and can be calculated more accurately Out of the granary volume.

As a further solution of the present invention, the first IMU data and the second IMU data are collected by a first laser radar device and a second laser radar device installed inside the granary, respectively.

As a further scheme of the present invention:

Using the Madgwick algorithm to calculate a quaternion q=[q0, q1, q2, q3] from the first IMU data or the second IMU data;

Substitute the corresponding quaternion calculated based on the first IMU data or the second IMU data into formula (1), and obtain the first rotation matrix or the first rotation matrix by formula (1);

The first IMU data or the second IMU data is N1x, N1y, N1z, N2x, N2y, N2z...Nnx, Nny, Nnz, and n is any positive integer;

Convert R1 or R2 to R1' or R2' correspondingly by formula (2), formula (2) is as follows:

As a further solution of the present invention: obtaining the first coordinate P1' relative to the granary through the first rotation matrix and the first point cloud data includes: the first point cloud data and the second point cloud data include several points, for The coordinates of any point are P1=(x1, y1, z1), then the coordinates of the first lidar device in the granary are relative to the first coordinates of the granary P1'=P*R ₁ '-first position data.

As a further scheme of the present invention: obtaining the second coordinate P2' relative to the granary through the second rotation matrix and the second point cloud data includes: the second point cloud data includes several points, and the coordinates for any point are P2=( x2, y2, z2), the coordinates of the second lidar device in the granary are relative to the second coordinates of the granary P2'=P2*R ₂ '-second position data.

As a further solution of the present invention: the dividing the grain simulation graph into several grids includes: dividing the grain simulation graph into grids according to x*y.

As a further solution of the present invention: summing up the volumes of all grids includes: calculating the grain volume of each grid relative to the height h of the granary by the computing terminal, and calculating the grain volume x*y*h of each grid.

A granary storage capacity measurement system, comprising:

The calculation module is used to receive the first IMU data and the first position data, the second IMU data and the second position data, and calculate the first IMU data and the second IMU data respectively to obtain the corresponding first rotation matrix and second rotation matrix ;

Wherein, the first position data is the coordinates (px1, py1, pz1) of the grain relative to the first radar device 1, and the second position data is the coordinates (px2, py2, pz2) of the grain relative to the second lidar device;

The obtaining module is used to obtain the first point cloud data and the second point cloud data respectively, and obtain the first coordinate P1' relative to the granary through the first rotation matrix, the first position data and the first point cloud data, and obtain the first coordinate P1' relative to the granary through the second The rotation matrix, the second position data and the second point cloud data obtain the second coordinate P2 relative to the granary;

The judgment module is used to judge whether the first coordinate P1' and the second coordinate P2' are located in the granary, exclude the coordinates outside the granary, and delete the noise data in the first point cloud data and the second point cloud data, and the remaining The first coordinate P1' and the second coordinate P2' are combined and registered to generate a grain simulation map;

The summation module is used to divide the grain simulation graph into several grids, count the height of each grid, and sum the volume of all grids to obtain the volume of all grains in the granary.

As a further solution of the present invention, the first IMU data and the second IMU data are collected by a first laser radar device and a second laser radar device installed inside the granary, respectively.

As a further scheme of the present invention:

Using the Madgwick algorithm to calculate a quaternion q=[q0, q1, q2, q3] from the first IMU data or the second IMU data;

Substitute the corresponding quaternion calculated based on the first IMU data or the second IMU data into formula (1), and obtain the first rotation matrix R1 or the first rotation matrix R2 by formula (1);

The first IMU data or the second IMU data is N1x, N1y, N1z, N2x, N2y, N2z...Nnx, Nny, Nnz, and n is any positive integer;

Convert R1 or R2 to R1' or R2' by formula (2), as shown in formula (2):

As a further solution of the present invention: obtaining the first coordinate P1' relative to the granary through the first rotation matrix and the first point cloud data includes: the first point cloud data and the second point cloud data include several points, P1 =(x1, y1, z1), then the coordinates of the first lidar device in the granary are relative to the first coordinates of the granary P1'=P1*R ₁ '-first position data.

As a further scheme of the present invention: obtaining the second coordinate P2' relative to the granary through the second rotation matrix and the second point cloud data includes: the second point cloud data includes several points, and the coordinates for any point are P2=( x2, y2, z2), the coordinates of the second lidar device in the granary are relative to the second coordinates of the granary P2'=P2*R ₂ '-second position data.

As a further solution of the present invention: the dividing the grain simulation graph into several grids includes: dividing the grain simulation graph into grids according to x*y, where x and y are any integers greater than 0.

As a further solution of the present invention: summing the volumes of all grids includes: the computing terminal counts the grain of each grid relative to the height h of the granary, calculates the grain volume x*y*h of each grid, and sums it up.

An electronic device comprising a memory and a processor; wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the granary grain as described Volume measurement method.

A storage medium having computer instructions stored thereon, the computer instructions implementing the method for measuring grain volume in a granary when executed by a processor.

The advantages of the present invention are:

1. The present invention uses the first IMU data and the second IMU data. After calculating the first rotation matrix and the second rotation matrix, the relative granary is obtained through the first rotation matrix, the first position data and the first point cloud data. The first coordinate P1', the second coordinate P2' relative to the granary is obtained through the second rotation matrix, the second position data and the second point cloud data (that is, the point is converted from the coordinate system of the grain relative to the radar to the grain relative to the granary The overall coordinate system), according to the first coordinate P1', the second coordinate P2' combined and registered to generate a grain simulation map to obtain the grain volume, which can more accurately calculate the granary volume.

2. Compared with the existing measurement method, since the present invention uses the first IMU data and the second IMU data in the measurement and calculation process, after calculating the first rotation matrix and the second rotation matrix, the first point cloud data . Any point in the second point cloud data is converted into the overall coordinate system of the granary by the first rotation matrix and the second rotation moment (that is, the point is converted from the radar coordinate system to the overall coordinate system of the granary), which is more accurate than one-dimensional linear scanning. high, the error is smaller, and the volume of the granary can be calculated more accurately.

3. Compared with the existing measurement methods, the measurement accuracy of this method is higher due to the centimeter-level positioning accuracy of the laser radar used.

4. Compared with the existing measurement methods, this method installs the first laser radar and the second laser radar at the diagonal positions of the granary, and forms the overall point cloud map of the granary by synthesizing the point cloud data of multiple radars, and carries out the measurement of the granary. Volume calculation, so there is no need to install rails, no need to turn on the light environment in the granary, the deployment is simpler and the cost is lower.

5. Compared with the existing measurement methods, this method adopts the method of meshing the point cloud data in the final calculation of the volume, which is divided into zero, and the final calculation is obtained by superimposing the volume data of each individual mesh. The volume of the overall granary. At the same time, the linear interpolation method is used to smooth the point cloud data in the grid, so that the volume calculation of each grid is more accurate. Compared with the traditional method of directly calculating the overall data, the accuracy is higher and the error is smaller.

Description of drawings

FIG. 1 is a schematic flowchart of a method for measuring a granary silo capacity provided in Embodiment 1 of the present invention.

FIG. 2 is a schematic flowchart of the granary storage capacity measurement system provided in Embodiment 1 of the present invention.

3 is a schematic diagram of the deployment of a first laser radar device, a second laser radar device, and a computing terminal in the method for measuring the capacity of a granary according to Embodiment 1 of the present invention.

FIG. 4 shows a structural block diagram of a device according to an embodiment of the present disclosure.

FIG. 5 is a schematic structural diagram of a computer system suitable for implementing a method for measuring grain volume in a granary according to an embodiment of the present disclosure.

Description of drawings:

1. The first laser radar device; 2. The second laser radar device; 3. The computing terminal.

Detailed ways

In order to make the purposes, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are part of the present invention. examples, but not all examples. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

In some of the processes described in the specification and claims of the present disclosure and the above-mentioned figures, various operations are included in a specific order, but it should be clearly understood that the operations may be performed out of the order in which they appear in the text. Executed or executed in parallel, the sequence numbers of the operations, such as 10, 11, etc., are only used to distinguish different operations, and the sequence numbers themselves do not represent any execution order. Additionally, these flows may include more or fewer operations, and these operations may be performed sequentially or in parallel. It should be noted that the descriptions such as "first" and "second" in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit "first" and "second" are different types.

It should be emphasized that, the computing terminal in the embodiments of the present disclosure may be a personal computer, a laptop portable computer, a desktop computer, or the like.

Example 1

1 is a schematic flowchart of a method for measuring the volume of a granary provided in Embodiment 1 of the present invention. Referring to FIG. 1 , a method for measuring the volume of grain in a granary includes:

S10. The computing terminal 3 receives the first IMU data, the first position data, the second IMU data and the second position data, and calculates the first IMU data and the second IMU data respectively to obtain the corresponding first rotation matrix and second rotation matrix ;

Wherein, the first position data is the coordinates (px1, py1, pz1) of the grain relative to the first radar device 1, and the second position data is the coordinates (px2, py2, pz2) of the grain relative to the second lidar device;

S11. The computing terminal 3 obtains the first point cloud data and the second point cloud data respectively, and obtains the first coordinate P1' relative to the granary through the first rotation matrix, the first position data and the first point cloud data, and obtains the first coordinate P1' relative to the granary through the second The rotation matrix, the second position data and the second point cloud data obtain the second coordinate P2' relative to the granary;

S12. The computing terminal 3 determines whether the first coordinate P1' and the second coordinate P2' are located in the granary, excludes the coordinates outside the granary, and deletes the noise data in the first point cloud data and the second point cloud data, and the remaining The first coordinate P1' and the second coordinate P2' are combined and registered to generate a grain simulation map;

If it is not located in the granary, the coordinates outside the granary will be excluded, and then the coordinates located in the granary will be excluded;

S13. The computing terminal 3 divides the grain simulation graph into several grids, and sums the volumes of all grids to obtain the volume of all grains in the granary.

In step S10, the first IMU data and the second IMU data are collected by the first laser radar device 1 and the second laser radar device 2 installed above the inner wall of the granary, respectively.

Referring to FIG. 3, FIG. 3 is a schematic diagram of the deployment of the first laser radar device, the second laser radar device, and the computing terminal in the method for measuring the capacity of a granary provided in Embodiment 1 of the present invention, the first laser radar device 1, and the second laser radar device. 2. Computing terminal 3, the first laser radar device 1 and the second laser radar device 2 are respectively installed at the diagonal positions of the upper two ends of the interior of the granary by means of bolts or screws. The center of the second laser radar device 2 points to the center of the granary, and the horizontal direction is leveled. A computing terminal 3 is deployed in the office area of the granary park. The first laser radar device 1, the second laser radar device 2, and the computing terminal 3 are in the same local area network. Inside.

The calculation process of the first IMU data and the second IMU data is the same. The following takes the calculation process of the first IMU data (or the second IMU data) as an example:

The first IMU data (or the second IMU data) are N1x, N1y, N1z, N2x, N2y, N2z...Nnx, Nny, Nnz, where n is any positive integer.

Generally speaking, the IMU sensor in the first lidar device mainly includes a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer, so n can be 1 or 2 or 3.

Substitute N1x, N1y, N1z, N2x, N2y, N2z...Nnx, Nny, Nnz into the Madgwick algorithm to calculate a quaternion q=[q0, q1, q2, q3], which is used for a three-axis gyroscope or The rotated position of the triaxial accelerometer or triaxial magnetometer.

Among them, the Madgwick algorithm is an existing algorithm, and the algorithm will not be described in detail here.

Specifically, when N is 1, N1x, N1y, and N1z represent the output of any one of the three-axis gyroscope, three-axis accelerometer, and three-axis magnetometer in the three directions of x, y, and z. data (that is, the angular acceleration in the corresponding three directions during motion).

Among them, when N is 2, at this time, N1x, N1y, N1z, N2x, N2y, N2z represent any two of the three-axis gyroscope, three-axis accelerometer, and three-axis magnetometer in x, y, z three output data in each direction.

Among them, when N is 3, N1x, N1y, N1z, N2x, N2y, N2z, N3x, N3y, N3z represent the three-axis gyroscope, three-axis accelerometer, and three-axis magnetometer at x, y respectively. , the output data in the three directions of z; (the specific correspondence between N1x, N1y, N1z, N2x, N2y, N2z, N3x, N3y, N3z and the three-axis gyroscope, three-axis accelerometer, and three-axis magnetometer can be changed ).

Substitute the corresponding quaternion calculated based on the first IMU data or the second IMU data into formula (1), and obtain the first rotation matrix R1 of the first lidar device 1 (or the second lidar device 2) through formula (1). The second rotation matrix R2); formula (1) is as follows:

Convert R1 (or R2) to R1' (R2') by formula (2), formula (2) is as follows:

get

Among them, R1, R2...R3 in R1' correspond to the data in R respectively.

In the solution of the embodiment of the present disclosure, the first IMU data or the second IMU data is a group of 6, which are respectively N1x, N1y, N1z, N2x, N2y, and N2z, representing the three-axis gyroscope and the three-axis accelerometer at x, For the output data in the three directions of y and z, use these 6 values and the Madgwick algorithm to calculate a quaternion q=[q0, q1, q2, q3]; calculate the specific rotation matrix R through q,

Among them, the gyroscope, accelerometer, and three-axis magnetometer are components in the lidar, so their positional relationship will not be described in detail here.

Similarly, the second rotation matrix R2 of the second lidar device 2 can also be obtained by the same method, by converting the second rotation matrix R2 into R ₂ ':

In step S11, the first point cloud data and the second point cloud data include several points, and the coordinates of any point in the first point cloud data are P1=(x1, y1, z1), and the second point cloud data Any point P2=(x2, y2, z2), then the coordinates of the first lidar device 1 in the granary are relative to the first coordinates of the granary P1'=P1*R ₁ '-first position data;

The coordinates of the second lidar device 2 in the granary are relative to the second coordinates of the granary P2'=P2*R ₂ '-second position data, formulas (3) and (4) are obtained: P1'=(x*R ₁ +y*R ₄ +z*R ₇ -px1, x*R ₂ +y*R ₅ +z*R ₈ -py1, x*R ₃ +y*R ₆ +z*R ₉ -pz1)(3 )P2'=(x2*R ₂₁ +y2*R ₂₄ +z2*R ₂₇ -px1, x2*R ₂₂ +y2*R ₂₅ +z2*R ₂₈ -py1, x2*R ₂₃ +y2*R ₃₆ +z2 *R ₂₉ -pz1)(4);

Through formula (3) and formula (4), the first coordinate P1 and the second coordinate P2 of the grain relative to the radar are converted into the first coordinate P1' and the second coordinate P2' of the grain relative to the granary.

In step S12; the cloud outlier removal algorithm is an existing algorithm, and the cloud outlier removal algorithm is: based on the input data set (ie the first point cloud data, the second point cloud data), the point to its neighbors distribution of distances. For each point in the point cloud, calculate its average distance to all neighboring points, by assuming that the resulting distribution is a Gaussian distribution with mean and standard deviation, its mean distance can be calculated as defined by the global mean and standard deviation of distances All points outside the interval are regarded as outliers and deleted from the dataset. Finally, the denoised first IMU data and the second IMU data are combined and registered to generate a grain simulation map.

In step S13, in the solution of the embodiment of the present disclosure, the grain simulation graph is divided into grids according to x*y, and the calculation terminal can count the grain of each grid relative to the height h of the granary, and calculate the grain volume of each grid After x*y*h, sum up to get the volume of all grains in the granary, where x and y are any integers greater than 0.

Among them, any P1=(x1, y1, z1) is a data of x1, y1, z1, where z1 is the coordinate height of the grain relative to the first lidar, and then multiplied by the rotation matrix of the first lidar, we get The grain corresponds to the granary height h (x1*R ₃ +y1*R ₆ +z1*R ₉ in the formula (3)), that is, h=x1*R ₃ +y1*R ₆ +z1*R ₉ .

Exemplarily, the grain simulation map is divided into grids of 0.1m*0.1m, and the grain height of each grid is counted based on the combined grain simulation map. If there is no first point cloud data and second point cloud data in the grid, use the interpolation algorithm to interpolate based on the nearby point clouds; and calculate the grain volume of each grid according to 0.1*0.1* height, and then sum all grid volumes , calculate the volume of all grains in the granary.

The interpolation algorithm is mainly used for calculating the average value, and the interpolation algorithm can be selected according to the actual situation. The interpolation algorithm is a conventional technical means in the field, and the specific process of the interpolation algorithm will not be described here.

Example 2

Referring to FIG. 2, FIG. 2 is a schematic flowchart of a granary silo capacity measurement system provided in Embodiment 1 of the present invention, a granary silo capacity measurement system, including:

The calculation module 501 is used to receive the first IMU data and the first position data, the second IMU data and the second position data, and respectively calculate the first IMU data and the second IMU data to obtain the corresponding first rotation matrix and second rotation matrix. matrix;

Wherein, the first position data is the coordinates (px1, py1, pz1) of the grain relative to the first radar device 1, and the second position data is the coordinates (px2, py2, pz2) of the grain relative to the second lidar device;

The acquisition module 502 is used to acquire the first point cloud data and the second point cloud data, and obtain the first coordinate P1' relative to the granary through the first rotation matrix, the first position data and the first point cloud data, and obtain the first coordinate P1' relative to the granary through the second The rotation matrix, the second position data and the second point cloud data obtain the second coordinate P2' relative to the granary;

The judgment module 503 is used to judge whether the first coordinate P1' and the second coordinate P2' are located in the granary, exclude the coordinates outside the granary, and delete the noise data in the first point cloud data and the second point cloud data, The remaining first coordinates P1' and second coordinates P2' are combined and registered to generate a grain simulation map;

If it is not located in the granary, the coordinates outside the granary will be excluded, and then the coordinates located in the granary will be excluded;

The summation module 504 is used for dividing the grain simulation graph into several grids, and summing the volumes of all grids to obtain the volume of all grains in the granary.

In the computing module, the first IMU data and the second IMU data are collected by the first laser radar device 1 and the second laser radar device 2 installed inside the granary, respectively.

The calculation process of the first IMU data and the second IMU data is the same. The following takes the calculation process of the first IMU data (or the second IMU data) as an example:

The first IMU data (or the second IMU data) are N1x, N1y, N1z, N2x, N2y, N2z...Nnx, Nny, Nnz, where n is any positive integer.

Generally speaking, the IMU sensors in lidar mainly include three-axis gyroscopes, three-axis accelerometers, and three-axis magnetometers, so n can be 1 or 2 or 3.

Substitute N1x, N1y, N1z, N2x, N2y, N2z...Nnx, Nny, Nnz into Madgwick algorithm to calculate a quaternion q=[q0, q1, q2, q3], where q0, q1, q2, q3 represent respectively.

Among them, the Madgwick algorithm is an existing algorithm, and the algorithm will not be described in detail here.

Specifically, when N is 1, N1x, N1y, and N1z represent the output of any one of the three-axis gyroscope, three-axis accelerometer, and three-axis magnetometer in the three directions of x, y, and z. data (that is, the angular acceleration in the corresponding three directions during motion).

Among them, when N is 2, at this time, N1x, N1y, N1z, N2x, N2y, N2z represent any two of the three-axis gyroscope, three-axis accelerometer, and three-axis magnetometer in x, y, z three output data in each direction.

Among them, when N is 3, N1x, N1y, N1z, N2x, N2y, N2z, N3x, N3y, N3z represent the three-axis gyroscope, three-axis accelerometer, and three-axis magnetometer at x, y respectively. , the output data in the three directions of z; (the specific correspondence between N1x, N1y, N1z, N2x, N2y, N2z, N3x, N3y, N3z and the three-axis gyroscope, three-axis accelerometer, and three-axis magnetometer can be changed ).

Substitute the quaternion into formula (1), and obtain the first rotation matrix R1 of the first lidar device 1 (or the second rotation matrix R2 of the second lidar device 2) through formula (1), formula (1) is as follows:

Convert R1 (or R2) to R1' (R2') by formula (2), formula (2) is as follows:

Among them, R1, R2...R3 in R1' correspond to the data in R respectively.

In the solution of the embodiment of the present disclosure, the first IMU data or the second IMU data is a group of 6, which are respectively N1x, N1y, N1z, N2x, N2y, and N2z, representing the three-axis gyroscope and the three-axis accelerometer at x, For the output data in the three directions of y and z, use these 6 values and the Madgwick algorithm to calculate a quaternion q=[q0, q1, q2, q3]; calculate the specific rotation matrix R through q.

Among them, the gyroscope, accelerometer, and three-axis magnetometer are components in the lidar, so their positional relationship will not be described in detail here.

Likewise, the rotation matrix of the second lidar device 2 can also be obtained by the same method.

In the acquisition module, the first point cloud data and the second point cloud data include several points, and the coordinates of any point in the first point cloud data are P1=(x1, y1, z1), and the second point cloud data Any point P2=(x2, y2, z2), then the coordinates of the first lidar device 1 in the granary are relative to the first coordinates of the granary P1'=P1*R ₁ '-first position data;

The coordinates of the second lidar device 2 in the granary are relative to the second coordinates of the granary P2'=P2*R ₂ '-second position data, formulas (3) and (4) are obtained: P1'=(x*R ₁ +y*R ₄ +z*R ₇ -px1, x*R ₂ +y*R ₅ +z*R ₈ -py1, x*R ₃ +y*R ₆ +z*R ₉ -pz1)(3 )P2'=(x2*R ₂₁ +y2*R ₂₄ +z2*R ₂₇ -px1, x2*R ₂₂ +y2*R ₂₅ +z2*R ₂₈ -py1, x2*R ₂₃ +y2*R ₃₆ +z2 *R ₂₉ -pz1)(4);

Through formula (3) and formula (4), the first coordinate P1 and the second coordinate P2 of the grain relative to the radar are converted into the first coordinate P1' and the second coordinate P2' of the grain relative to the granary.

In the judgment module; the cloud outlier removal algorithm is an existing algorithm, and the cloud outlier removal algorithm is: based on the input data set (ie, the first point cloud data, the second point cloud data) point to its neighbors distribution of distances. For each point in the point cloud, calculate its average distance to all neighboring points, by assuming that the resulting distribution is a Gaussian distribution with mean and standard deviation, its mean distance can be defined by the global distance mean and standard deviation All points outside the interval are regarded as outliers and deleted from the dataset. Finally, the denoised first IMU data and the second IMU data are combined and registered to generate a grain simulation map.

In the summation module, in the solution of the embodiment of the present disclosure, the grain simulation graph is divided into grids according to x*y, and the grains of each grid are counted relative to the height h of the granary, and the grain volume x of each grid is calculated. After *y*h, sum up to get the volume of all grains in the granary, where x and y are any integers greater than 0.

Among them, any P1=(x1, y1, z1) is a data of x1, y1, z1, where z1 is the coordinate height of the grain relative to the first lidar, and then multiplied by the rotation matrix of the first lidar, we get The grain corresponds to the granary height h (x1*R ₃ +y1*R ₆ +z1*R ₉ in the formula (3)), that is, h=x1*R ₃ +y1*R ₆ +z1*R ₉ .

Exemplarily, the grain simulation map is divided into grids of 0.1m*0.1m, and the grain height of each grid is counted based on the combined grain simulation map. If there is no first point cloud data and second point cloud data in the grid, use the interpolation algorithm to interpolate based on the nearby point clouds; and calculate the grain volume of each grid according to 0.1*0.1* height, and then sum all grid volumes , calculate the volume of all grains in the granary.

The interpolation algorithm is mainly used for calculating the average value, and the interpolation algorithm can be selected according to the actual situation. The interpolation algorithm is a conventional technical means in the field, and the specific process of the interpolation algorithm will not be described here.

FIG. 4 shows a structural block diagram of a device according to an embodiment of the present disclosure.

The foregoing embodiment describes the internal function and structure of the computing terminal 3. In a possible design, the foregoing structure of the computing terminal 3 may be implemented as an electronic device. As shown in FIG. 3, the electronic device 900 may include a processor 901 and a memory 902.

The memory 902 is configured to store a program that supports the processor to execute the method for measuring the grain volume of a granary in any of the above embodiments, and the processor 901 is configured to execute the program stored in the memory 902 .

The memory 902 is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor 901 to implement the steps described in Embodiment 1.

FIG. 5 is a schematic structural diagram of a computer system suitable for implementing a method for measuring grain volume in a granary according to an embodiment of the present disclosure.

As shown in FIG. 5, a computer system 1000 includes a processor (CPU, GPU, FPGA, etc.) 1001 that can be loaded into a random access memory (RAM) according to a program stored in a read only memory (ROM) 1002 or from a storage section 1008 The program in 1003 executes part or all of the processing in the embodiments shown in the above drawings. In the RAM 1003, various programs and data necessary for the operation of the system 1000 are also stored. The processor 1001 , the ROM 1002 and the RAM 1003 are connected to each other through a bus 1004 . An input/output (I/O) interface 1005 is also connected to the bus 1004 .

The following components are connected to the I/O interface 1005: an input section 1006 including a keyboard, a mouse, etc.; an output section 1007 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1008 including a hard disk, etc. ; and a communication section 1009 including a network interface card such as a LAN card, a modem, and the like. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I/O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is mounted on the drive 1010 as needed so that a computer program read therefrom is installed into the storage section 1008 as needed.

In particular, according to embodiments of the present disclosure, the methods described above with reference to the accompanying drawings may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a readable medium thereof, the computer program containing program code for performing the methods of the accompanying drawings. In such an embodiment, the computer program may be downloaded and installed from the network through the communication section 1009, and/or installed from the removable medium 1011.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the diagram or block diagram may represent a module, segment, or portion of code that contains one or more functions for implementing the specified logical function. executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It is also noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented in dedicated hardware-based systems that perform the specified functions or operations , or can be implemented in a combination of dedicated hardware and computer instructions.

The units or modules involved in the embodiments of the present disclosure can be implemented in software or hardware. The described units or modules may also be provided in the processor, and the names of these units or modules do not constitute a limitation on the units or modules themselves in certain circumstances.

As another aspect, the present disclosure also provides a storage medium, where the storage medium is a computer-readable storage medium, and the computer-readable storage medium may be a computer-readable storage medium included in the computing terminal described in the foregoing embodiments ; or a computer-readable storage medium that exists alone, not assembled into the device. The computer-readable storage medium stores one or more programs used by one or more processors to perform the methods described in the present disclosure.

The above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: The recorded technical solutions are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (16)

1. A method for measuring the volume of grain in a granary, comprising the following steps:

   Receive the first IMU data and the first position data, the second IMU data and the second position data, and calculate the first IMU data and the second IMU data respectively to obtain the corresponding first rotation matrix and second rotation matrix;

   Wherein, the first position data is the coordinates (px1, py1, pz1) of the grain relative to the first radar device 1, and the second position data is the coordinates (px2, py2, pz2) of the grain relative to the second lidar device;

   Obtain the first point cloud data and the second point cloud data respectively, and obtain the first coordinate P1' relative to the granary through the first rotation matrix, the first position data and the first point cloud data. The position data and the second point cloud data obtain the second coordinate P2' relative to the granary;

   Determine whether the first coordinate P1' and the second coordinate P2' are located in the granary, exclude the coordinates outside the granary, delete the noise data in the first point cloud data and the second point cloud data, and the remaining first coordinate P1 ', the second coordinate P2' is combined and registered to generate a grain simulation map;

   The grain simulation graph is divided into several grids, and the volume of all grids is summed to obtain the volume of all grains in the granary.
2. The method for measuring grain volume in a granary according to claim 1, wherein the first IMU data and the second IMU data are collected by a first laser radar device and a second laser radar device installed inside the granary, respectively.
3. granary grain volume measurement method according to claim 2, is characterized in that,

   Using the Madgwick algorithm to calculate a quaternion q=[q0, q1, q2, q3] from the first IMU data or the second IMU data;

   Substitute the corresponding quaternion calculated based on the first IMU data or the second IMU data into formula (1), and obtain the first rotation matrix R1 or the first rotation matrix R2 by formula (1);

   The first IMU data or the second IMU data is N1x, N1y, N1z, N2x, N2y, N2z...Nnx, Nny, Nnz, and n is any positive integer;

   

   Convert R1 or R2 to R1' or R2' correspondingly by formula (2), formula (2) is as follows:

   
4. The method for measuring the grain volume of a granary according to claim 3, wherein obtaining the first coordinate P1' relative to the granary through the first rotation matrix and the first point cloud data comprises: the first point cloud data, the second The point cloud data includes several points, P1=(x1, y1, z1), then the coordinates of the first lidar device in the granary are relative to the first coordinates of the granary P1'=P1*R ₁ '-first position data .
5. The granary grain volume measurement method according to claim 3, wherein obtaining the second coordinate P2' relative to the granary through the second rotation matrix and the second point cloud data comprises:

   The second point cloud data includes several points, the coordinates of any point are P2=(x2, y2, z2), the coordinates of the second lidar device in the granary are relative to the second coordinates of the granary P2'=P2*R ₂ ' - Second position data.
6. The method for measuring grain volume in a granary according to claim 1, wherein the dividing the grain simulation graph into several grids comprises: dividing the grain simulation graph into grids according to x*y, where x and y are any greater than 0 the integer.
7. The method for measuring the grain volume of a granary according to claim 6, wherein the summation of the volumes of all grids comprises: the computing terminal counts the grain of each grid relative to the height h of the granary, and calculates the grain volume x of each grid Sum after *y*h.
8. A granary storage capacity measurement system, characterized in that it includes:

   The calculation module is used to receive the first IMU data and the first position data, the second IMU data and the second position data, and calculate the first IMU data and the second IMU data respectively to obtain the corresponding first rotation matrix and second rotation matrix ;

   Wherein, the first position data is the coordinates (px1, py1, pz1) of the grain relative to the first radar device 1, and the second position data is the coordinates (px2, py2, pz2) of the grain relative to the second lidar device;

   The obtaining module is used to obtain the first point cloud data and the second point cloud data respectively, and obtain the first coordinate P1' relative to the granary through the first rotation matrix, the first position data and the first point cloud data, and obtain the first coordinate P1' relative to the granary through the second The rotation matrix, the second position data and the second point cloud data obtain the second coordinate P2 relative to the granary;

   The judgment module is used to judge whether the first coordinate P1' and the second coordinate P2' are located in the granary, exclude the coordinates outside the granary, and delete the noise data in the first point cloud data and the second point cloud data, and the remaining The first coordinate P1' and the second coordinate P2' are combined and registered to generate a grain simulation map;

   The summation module is used to divide the grain simulation graph into several grids, and sum the volumes of all grids to obtain the volume of all grains in the granary.
9. The granary grain volume measurement system according to claim 7, wherein the first IMU data and the second IMU data are collected by a first laser radar device and a second laser radar device installed inside the granary, respectively.
10. The granary grain volume measurement system according to claim 8, wherein:

    Using the first IMU data or the second IMU data to calculate a quaternion q=[q0, q1, q2, q3] by using the Madgwick algorithm;

    Substitute the corresponding quaternion calculated based on the first IMU data or the second IMU data into formula (1), and obtain the first rotation matrix R1 or the first rotation matrix R2 by formula (1);

    The first IMU data or the second IMU data is N1x, N1y, N1z, N2x, N2y, N2z...Nnx, Nny, Nnz, and n is any positive integer;

    

    Convert R1 or R2 to R1' or R2' by formula (2), as shown in formula (2):

    
11. The granary grain volume measurement system according to claim 11, wherein obtaining the first coordinate P1' relative to the granary through the first rotation matrix and the first point cloud data comprises: the first point cloud data, the second The point cloud data includes several points, P1=(x1, y1, z1), then the coordinates of the first lidar device in the granary are relative to the first coordinates of the granary P1'=P1*R ₁ '-first position data .
12. The granary grain volume measurement system according to claim 11, wherein obtaining the second coordinate P2' relative to the granary through the second rotation matrix and the second point cloud data comprises: the second point cloud data includes several points , the coordinates of any point are P2=(x2, y2, z2), the coordinates of the second lidar device in the granary are relative to the second coordinates of the granary P2'=P2*R ₂ '-second position data.
13. The granary grain volume measurement system according to claim 7, wherein the dividing the grain simulation graph into several grids comprises: dividing the grain simulation graph into grids according to x*y, where x and y are any greater than 0 the integer.
14. The granary grain volume measurement system according to claim 13, wherein summing the volumes of all grids comprises: the computing terminal counts the grain of each grid relative to the granary height h, and calculates the grain volume x of each grid Sum after *y*h.
15. An electronic device, comprising a memory and a processor; wherein, the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement claims 1-7 The method of any one.
16. A storage medium having computer instructions stored thereon, the computer instructions implementing the method according to any one of claims 1 to 7 when executed by a processor.

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