CN114323145A - Orchard terrain modeling method and system based on multi-sensor information fusion - Google Patents
Orchard terrain modeling method and system based on multi-sensor information fusion Download PDFInfo
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Abstract
The invention discloses a method and a system for modeling a orchard shape based on multi-sensor information fusion, which comprises data acquisition at an acquisition end and modeling by an upper computer, wherein the acquisition end consists of a main control chip, a sensor module, an interaction module, a cache module and a result storage module, and the acquisition of data of the orchard shape sample points is realized; after the upper computer obtains the sampling result of the acquisition end, the multi-sensor information is fused through a Kalman filtering algorithm, outlier data are identified and removed through a single-class support vector machine algorithm, and a Lagrange interpolation method is combined to interpolate sampling points, so that a three-dimensional terrain three-dimensional model of the orchard to be sampled is constructed.
Description
Technical Field
The invention relates to the technical field of sensor modeling, in particular to a method and a system for building a fruit garden terrain based on multi-sensor information fusion.
Background
The terrain modeling is based on computer technology, photoelectric technology, network communication technology, space science and information science, takes a global navigation satellite positioning system, remote sensing and a geographic information system as technical cores, selects the existing characteristic points and boundary lines on the ground and obtains the graph and position information reflecting the current situation of the ground by a measurement means, so as to be used for engineering construction, planning and design and administrative management. At present, instruments such as a three-dimensional laser scanner, a level gauge, a total station and the like are generally adopted for terrain modeling, operation is completed by professional personnel, and professional software is matched for site modeling, so that terrain data with high precision is obtained. However, professional equipment has the disadvantages of high cost, heavy weight and the like, and simultaneously needs a plurality of professionals to cooperate with measurement, so that a great amount of manpower and material resources are consumed when the special equipment is used in an orchard with a large number of areas. Therefore, the research on the low-cost terrain mapping modeling method has important significance for reducing the modeling cost of the farm.
At present, no low-cost large-range mapping modeling instrument and algorithm aiming at the orchard exists, so that the method disclosed by the invention can be used for well supplementing the technical blank in the field.
Disclosure of Invention
In view of the above, the invention provides a method and a system for modeling a fruit garden shape based on multi-sensor information fusion, which comprises data acquisition at an acquisition end and modeling by an upper computer, wherein the acquisition end consists of a main control chip, a sensor module, an interaction module, a cache module and a result storage module, and is used for acquiring data of fruit garden shape sample points; after the upper computer obtains the sampling result of the acquisition end, the multi-sensor information is fused through a Kalman filtering algorithm, outlier data are identified and removed through a single-class support vector machine algorithm, and a Lagrange interpolation method is combined to interpolate sampling points, so that a three-dimensional terrain three-dimensional model of the orchard to be sampled is constructed.
In order to achieve the purpose, the invention adopts the following technical scheme:
a garden terrain modeling method based on multi-sensor information fusion comprises the following steps:
s1, acquiring sensor data and storing the sensor data as topographic data sampling points;
s2, fusing the sampling point data through a Kalman filtering algorithm;
s3, identifying and removing outlier data through a single-class support vector machine algorithm;
and S4, interpolating the sampling point data through a Lagrange interpolation algorithm to construct a three-dimensional terrain model.
Preferably, the topographic data sampling points comprise three-direction magnetic induction intensity, three-direction acceleration, three-direction angular velocity, three-direction angular acceleration, an air pressure value, a laser distance between the sampling instrument and the ground, and longitude and latitude coordinates of sampling point positions.
Preferably, the step S2 specifically includes:
s21, predicting the next state of the system based on the prediction formula, wherein the formula is as follows:
xs＝F*x[i]+B*u[i]
P[i+1]＝F*P[i]*FT+Q
wherein, F is a system state transmission matrix; x is a system state matrix; fTA transposed matrix representing F; b is a control transmission matrix; u is the system control input; p is a covariance matrix; q is system processing noise; x is the number ofsIs a state estimation value matrix;
s22, updating the system state by an updating formula, which comprises the following 4 steps:
y＝z-H(xs)
wherein z is a measured value of the sensor input; h represents the transfer function of the state variable to the measured value; y is the error between the state estimate and the actual sensor input;
wherein, JHA Jacobian matrix representing H is obtained by solving a partial derivative of the H matrix;
is JHThe transposed matrix of (2); r is a measurement error matrix; s is a temporary variable;
K＝P[i+1]*JH*inv(S)
wherein inv (S) represents an inverse of the S matrix; k is a Kalman gain matrix;
x[i+1]＝x[i]+K*y。
preferably, the step S3 specifically includes: the sampling point data is regarded as the same type and used for training a support vector machine model; after training is finished, data sampling points are sequentially transmitted to a support vector machine model, the model scores the sampling points according to the outlier degree of the data, and the lower the score is, the larger the outlier of the sampling points is; and setting a threshold, traversing each data sample point, and removing the sample points with the scores lower than the threshold from the data set to filter the outlier data.
Preferably, the step S4 specifically includes:
the sampling point interpolation algorithm is realized based on a Lagrange interpolation method, and the algorithm formula is as follows:
wherein, yi＝f(xi) N represents the order of interpolation; x is a point needing interpolation calculation; x is the number ofi、xjAll represent the nearest n +1 real data samples from the horizontal distance x; y isiRepresents point xiA corresponding true height value; f is the interpolated objective function;
the terrain modeling result is expressed by adopting a function, namely:
h＝f(x,y)
and establishing a right-hand rectangular coordinate system by taking the sampling starting point as an origin, the due north direction as a y-axis and the due east direction as an x-axis, wherein the origin of the coordinate is a height 0 point, h represents the height of the position (x, y) relative to the origin, and when the height of the position is higher than the origin, the value of h is positive.
A orchard terrain modeling system based on multi-sensor information fusion comprises an acquisition end, a data transmission unit and an upper computer, wherein the acquisition end is connected with the upper computer through the data transmission unit;
the acquisition end comprises various sensor modules, a main control chip, a result storage module and a cache module, and the sensor modules, the result storage module and the cache module are all connected with the main control chip;
the result storage module comprises a wireless transmission module, and the wireless transmission module performs CRC (cyclic redundancy check) and TEA (TEA encryption) on transmission data;
the cache module is used for storing the acquired data;
and the upper computer performs fusion processing and calculation on the data and establishes a terrain three-dimensional model.
Preferably, the acquisition end further comprises an interaction module, the interaction module comprises a display module and an alarm module, and the interaction module is connected with the main control chip.
Preferably, the plurality of sensor modules includes: the device comprises a magnetic induction sensor, an acceleration sensor, an angular velocity sensor, an angular acceleration sensor, an air pressure sensor, a laser ranging module and a satellite positioning module.
Preferably, the result storage module further comprises an external storage module, and the external storage module is used for connecting an external storage component and performing external storage on the acquired data.
According to the technical scheme, compared with the prior art, the method and the system for building the garden site based on multi-sensor information fusion comprise data acquisition at an acquisition end and modeling by an upper computer, wherein the acquisition end consists of a main control chip, a sensor module, an interaction module, a cache module and a result storage module, and acquisition of data of the garden site sample points is realized; after the upper computer obtains the sampling result of the acquisition end, the multi-sensor information is fused through a Kalman filtering algorithm, outlier data are identified and removed through a single-class support vector machine algorithm, and a Lagrange interpolation method is combined to interpolate sampling points, so that a three-dimensional terrain three-dimensional model of the orchard to be sampled is constructed.
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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, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic diagram of a system structure provided by the invention.
Fig. 2 is a schematic diagram of a synthetic principle of a measurement matrix provided by the invention.
FIG. 3 is a schematic diagram of the extended Kalman filtering principle provided by the present invention.
Fig. 4 is a schematic diagram of an interpolation principle provided by the present invention.
Fig. 5 is a schematic diagram of a wireless data transmission stream according to the present invention.
Wherein, 1 is the collection end, 2 is the host computer, 11 is sensor module, and 12 is main control chip. And 13 is a result storage module. 14 is a buffer module, 15 is an interaction module, 131 is a wireless transmission module, 132 is an external storage module, 151 is a display module, and 152 is an alarm module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
The embodiment of the invention discloses a method for modeling a fruit garden terrain based on multi-sensor information fusion, which comprises the following steps:
s1, acquiring sensor data and storing the sensor data as topographic data sampling points;
s2, fusing the sampling point data through a Kalman filtering algorithm;
s3, identifying and removing outlier data through a single-class support vector machine algorithm;
and S4, interpolating the sampling point data through a Lagrange interpolation algorithm to construct a three-dimensional terrain model.
In order to further optimize the technical scheme, the topographic data sampling points comprise three-direction magnetic induction intensity, three-direction acceleration, three-direction angular velocity, three-direction angular acceleration, an air pressure value, a laser distance between the sampling instrument and the ground and longitude and latitude coordinates of sampling point positions.
To further optimize the above technical solution, step S2 specifically includes:
s21, predicting the next state of the system based on the prediction formula, wherein the formula is as follows:
xs＝F*x[i]+B*u[i]
P[i+1]＝F*P[i]*FT+Q
wherein, F is a system state transmission matrix; x is a system state matrix; fTA transposed matrix representing F; b is a control transmission matrix; u is the system control input; p is a covariance matrix; q is system processing noise; x is the number ofsIs a state estimation value matrix;
s22, updating the system state by an updating formula, which comprises the following 4 steps:
y＝z-H(xs)
wherein z is a measured value of the sensor input; h represents the transfer function of the state variable to the measured value; y is the error between the state estimate and the actual sensor input;
wherein, JHA Jacobian matrix representing H is obtained by solving a partial derivative of the H matrix;
is JHThe transposed matrix of (2); r is a measurement error matrix; s is a temporary variable;
K＝P[i+1]*JH*inv(S)
wherein inv (S) represents an inverse of the S matrix; k is a Kalman gain matrix;
x[i+1]＝x[i]+K*y。
to further optimize the above technical solution, step S3 specifically includes: the sampling point data is regarded as the same type and used for training a support vector machine model; after training is finished, data sampling points are sequentially transmitted to a support vector machine model, the model scores the sampling points according to the outlier degree of the data, and the lower the score is, the larger the outlier of the sampling points is; and setting a threshold, traversing each data sample point, and removing the sample points with the scores lower than the threshold from the data set to filter the outlier data.
To further optimize the above technical solution, step S4 specifically includes:
the sampling point interpolation algorithm is realized based on a Lagrange interpolation method, and the algorithm formula is as follows:
wherein, yi＝f(xi) N represents the order of interpolation; x is a point needing interpolation calculation; x is the number ofi、xjAll represent the nearest n +1 real data samples from the horizontal distance x; y isiRepresents point xiA corresponding true height value; f is the interpolated objective function;
the terrain modeling result is expressed by adopting a function, namely:
h＝f(x,y)
and establishing a right-hand rectangular coordinate system by taking the sampling starting point as an origin, the due north direction as a y-axis and the due east direction as an x-axis, wherein the origin of the coordinate is a height 0 point, h represents the height of the position (x, y) relative to the origin, and when the height of the position is higher than the origin, the value of h is positive.
A orchard terrain modeling system based on multi-sensor information fusion comprises a collection end 1, an upper computer 2, wherein the collection end 1 is connected with the upper computer 2;
the acquisition end 1 comprises a plurality of sensor modules 11, a main control chip 12, a result storage module 13 and a cache module 14, wherein the sensor modules 11, the result storage module 13 and the cache module 14 are all connected with the main control chip 12;
the result storage module 13 includes a wireless transmission module 131, which performs CRC check and TEA encryption on the transmission data;
the cache module 14 is used for storing the acquired data;
the upper computer 2 performs fusion processing and calculation on the data and establishes a terrain three-dimensional model.
In order to further optimize the above technical solution, the acquisition end 1 further includes an interaction module 15, the interaction module 15 includes a display module 151 and an alarm module 152, and the interaction module 15 is connected to the main control chip 12.
To further optimize the above technical solution, the various sensor modules 11 include: the device comprises a magnetic induction sensor, an acceleration sensor, an angular velocity sensor, an angular acceleration sensor, an air pressure sensor, a laser ranging module and a satellite positioning module.
In order to further optimize the above technical solution, the result storage module 13 further includes an external storage module 132, and the external storage module 132 is used for connecting an external storage component to externally store the acquired data.
Specifically describing the embodiment with reference to fig. 1, the hardware portion mainly includes a sensor module, a cache module, an interaction module, and a result storage module. The sensor module is responsible for collecting physical information such as magnetic induction intensity, air pressure, acceleration and the like and sending the physical information to the main control chip through the serial port. After the main control chip receives the sensor data, the parallel port control interaction module is used for displaying information such as the current longitude and latitude coordinates, attitude information, acceleration, angular acceleration, magnetic induction intensity, laser distance, air pressure height, wireless communication signal intensity, battery electric quantity, horizontal distance and three-dimensional distance of the current position relative to the original point in real time through the liquid crystal screen.
When sampling work is carried out, the screen can continuously display the working state of the equipment in Chinese, such as 'data storage success', 'xxx equipment abnormity', 'data transmission error' and the like; if the sampler takes place unusually, bee calling organ will send out and cry, and RGB three-colour emitting diode scintillation simultaneously, and the user can judge the fault type fast according to RGB three-colour emitting diode scintillation mode and the difference of colour.
In order to realize Chinese display, when the sampler is initialized, whether a Chinese character library is stored in the ROM storage chip is detected, and if not, the Chinese character library is loaded from the memory card and copied into the ROM storage chip. When the sampling instrument works, the main control chip accesses the cache module through the SPI interface to obtain Chinese fonts.
The FTAFS file system is transplanted inside the main control chip, results and running logs are written into the memory card in a file mode when the sampling instrument works by combining the SDIO interface through the FTAFS file system, and meanwhile, the sampling instrument communicates with the wireless communication module through a serial port to send sampling point information to the remote upper computer. After the collection work is finished, the memory card can be taken down from the sampling instrument, and the data is guided into the upper computer through the card reader. The user can configure the sampling instrument through the remote upper computer, and the remote control instruction is transmitted by the wireless communication module and is sent to the main control chip through the serial port.
The invention is characterized in that the hardware development comprises a sensor module, a cache module, an interaction module and a result storage module, and data exchange is carried out among the modules in serial port, parallel port, SPI and SDIO modes.
The method is further characterized in that upper computer software can read sampling point information, and algorithms including Kalman filtering, outlier data removal, sampling point interpolation and the like are utilized to obtain three-dimensional terrain three-dimensional modeling of the orchard to be sampled.
The last feature of the present invention is that the master control chip and the remote upper computer use CRC check and TEA encryption for the data frame before calling the wireless communication module to exchange information.
Referring to fig. 2 and 3, a right-handed rectangular coordinate system, hereinafter referred to as a "geographical coordinate system", is established with reference to the ground, the north as the y-axis, the east as the x-axis, and the sampling start point as the origin.
Since the system states are independent of each other, the state transmission F of the system is an identity matrix; since the system has no control input, B is a zero matrix.
The state matrix x of the system is:
x＝[locx locy locz vx vy vzΔt]T
wherein, locx、locy、loczRespectively representing the xyz coordinates of the system on a geographic coordinate system; v. ofx、vyAnd vzThe speed of the system in the directions of three coordinate axes of a geographic coordinate system xyz; and delta t is the time difference from the last time the algorithm is executed to this time.
The measurement matrix z of the system is as follows:
z＝[sin(yaw) cos(yaw) ax ay azΔt hpre hlaser rtkx rtky]T
wherein, yaw is a rudder angle, and when the y axis of the equipment coordinate system points to the right north, yaw is 0; ax, ay, az are accelerations in three directions of the geographic coordinate system, respectively; Δ t is the time difference from the last execution of the filtering algorithm; h ispreThe height is the air pressure height and the z-axis height of a geographic coordinate system, and before the sampling instrument is used, the height needs to be calibrated and calibrated by a level gauge and a straight ruler; rtkxAnd rtkyThe coordinate position of the geographic coordinate system after coordinate conversion is carried out on longitude and latitude information output by the locator module can also be understood as x-axis and y-axis displacement of the current sampling instrument position relative to a sampling starting point; h islaserFor the laser height, the height is calculated by the following formula:
hlaser＝laser*cos(pit)*cos(roll)
wherein, the laser is the reading of the laser ranging sensor; pit is the pitch angle of the sampling instrument; roll is the roll angle of the sampler.
The state variable to measured value transfer function H is as follows:
the partial derivative of the transfer function H is calculated to obtain a Jacobian matrix JH：
The system processing noise matrix Q and the measurement noise matrix R need to be set according to the specific parameters of the sensor. And after a system state matrix, a system measurement matrix, a state variable to measured value transfer function and a Jacobian matrix are deduced, synthesizing the sensor data by utilizing an extended Kalman filtering algorithm.
The modeling process using the sample data according to the present invention is specifically described with reference to fig. 4, and the sampling results are shown in a perspective view. And executing a single-class support vector machine algorithm on the sampling result, and scoring the outlier degree of each data point after execution to obtain an outlier degree identification result graph, wherein the lower the score in the graph, the larger the outlier degree of the point. And designing a score threshold according to actual conditions to remove outliers, and then obtaining an outlier removal result graph. After interpolation algorithms including but not limited to linear interpolation or cubic interpolation and the like are carried out on the sampling points in the map, the terrain description function can be obtained.
The CRC check algorithm and TEA encryption algorithm used in the wireless transmission of the present invention are described separately.
CRC check refers to cyclic redundancy check, is a channel coding technique that generates a short fixed bit check code from data such as network data packets or computer files, and is mainly used to detect or check errors that may occur after data transmission or storage. The sampling instrument end of the invention uses the built-in CRC module of the main control chip to calculate the check value, and the CRC of the upper computer end is realized by using a software method.
The CRC calculation in the present invention uses a polynomial of: 0x4C11DB7 with a data bit width of 32 bits. The character string to be sent is stored in an operation memory, before CRC calculation is needed, grouping is carried out from a low address to a high address according to 4 bytes as a group, and if the last group is less than 4 bytes, zero filling is carried out at the end for supplementing 4 bytes. Before calculation, a 32-bit remainder register is set to be 0xFFFFFFFF, then a CRC-32 algorithm is called by each group of 4-byte data of the character string, and after the last group is called, a CRC-32 calculation result (namely a value in the remainder register) is taken as a check code and is supplemented to the frame tail of the data frame.
TEA encryption is a block cipher algorithm, and has strong capability of resisting differential analysis and relatively high encryption speed by using a continuously increased Delta (golden section ratio) value as a change. The invention uses TEA encryption as a wireless transmission encryption algorithm.
When the sampling instrument is used for the first time, a user needs to connect the sampling instrument to an upper computer to carry out code matching. Each main control chip has own unique chip ID, when the codes are matched, the main control chip of the sampling instrument sends the own unique chip ID to the upper computer, the upper computer takes the ID and adds a random number to generate a secret key, the secret key is transmitted back to the sampling instrument to complete the code matching, and the secret key is used as a TEA encryption secret key for wireless communication between the sampling instrument and the upper computer
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (9)
1. A orchard terrain modeling method based on multi-sensor information fusion is characterized by comprising the following steps:
s1, acquiring sensor data and storing the sensor data as topographic data sampling points;
s2, fusing the sampling point data through a Kalman filtering algorithm;
s3, identifying and removing outlier data through a single-class support vector machine algorithm;
and S4, interpolating the sampling point data through a Lagrange interpolation algorithm to construct a three-dimensional terrain model.
2. The method as claimed in claim 1, wherein the topographic data sampling points include three-directional magnetic induction, three-directional acceleration, three-directional angular velocity, three-directional angular acceleration, air pressure value, laser distance between the sampling instrument and the ground, and longitude and latitude coordinates of sampling point positions.
3. The method for orchard modeling based on multi-sensor information fusion according to claim 1, wherein the step S2 specifically includes:
s21, predicting the next state of the system based on the prediction formula, wherein the formula is as follows:
xs＝F*x[i]+B*u[i]
P[i+1]＝F*P[i]*FT+Q
wherein, F is a system state transmission matrix; x is a system state matrix; fTA transposed matrix representing F; b is a control transmission matrix; u is the system control input; p is a covariance matrix; q is system processing noise; x is the number ofsIs a state estimation value matrix;
s22, updating the system state by an updating formula, which comprises the following 4 steps:
y＝z-H(xs)
wherein z is a measured value of the sensor input; h represents the transfer function of the state variable to the measured value; y is the error between the state estimate and the actual sensor input;
wherein, JHA Jacobian matrix representing H is obtained by solving a partial derivative of the H matrix;
is JHThe transposed matrix of (2); r is a measurement error matrix; s is a temporary variable;
K＝P[i+1]*JH*inv(S)
wherein inv (S) represents an inverse of the S matrix; k is a Kalman gain matrix;
x[i+1]＝x[i]+K*y。
4. the method for orchard modeling based on multi-sensor information fusion according to claim 1, wherein the step S3 specifically includes: the sampling point data is regarded as the same type and used for training a support vector machine model; after training is finished, data sampling points are sequentially transmitted to a support vector machine model, the model scores the sampling points according to the outlier degree of the data, and the lower the score is, the larger the outlier of the sampling points is; and setting a threshold, traversing each data sample point, and removing the sample points with the scores lower than the threshold from the data set to filter the outlier data.
5. The method for orchard modeling based on multi-sensor information fusion according to claim 1, wherein the step S4 specifically includes:
the sampling point interpolation algorithm is realized based on a Lagrange interpolation method, and the algorithm formula is as follows:
wherein, yi＝f(xi) N represents the order of interpolation; x is a point needing interpolation calculation; x is the number ofi、xjAll represent the nearest n +1 real data samples from the horizontal distance x; y isiRepresents point xiA corresponding true height value; f is the interpolated objective function;
the terrain modeling result is expressed by adopting a function, namely:
h＝f(x,y)
and establishing a right-hand rectangular coordinate system by taking the sampling starting point as an origin, the due north direction as a y-axis and the due east direction as an x-axis, wherein the origin of the coordinate is a height 0 point, h represents the height of the position (x, y) relative to the origin, and when the height of the position is higher than the origin, the value of h is positive.
6. A garden terrain modeling system based on multi-sensor information fusion is characterized by comprising a collection end (1) and an upper computer (2), wherein the collection end (1) is connected with the upper computer (2);
the acquisition end (1) comprises a plurality of sensor modules (11), a main control chip (12), a result storage module (13) and a cache module (14), wherein the sensor modules (11), the result storage module (13) and the cache module (14) are all connected with the main control chip (12);
the result storage module (13) comprises a wireless transmission module (131) which carries out CRC check and TEA encryption on transmission data;
the cache module (14) is used for storing the acquired data;
and the upper computer (2) performs fusion processing and calculation on the data and establishes a terrain three-dimensional model.
7. The fruit garden land shape modeling system based on multi-sensor information fusion of claim 6, wherein the collection end (1) further comprises an interaction module (15), the interaction module (15) comprises a display module (151) and an alarm module (152), and the interaction module (15) is connected with the main control chip (12).
8. The system of claim 6, wherein the plurality of sensor modules (11) comprises: the device comprises a magnetic induction sensor, an acceleration sensor, an angular velocity sensor, an angular acceleration sensor, an air pressure sensor, a laser ranging module and a satellite positioning module.
9. The orchard modeling system based on multi-sensor information fusion as claimed in claim 6, wherein the result storage module (13) further comprises an external storage module (132), and the external storage module (132) is used for connecting an external storage component and externally storing the collected data.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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