CN116518942A - Mine goaf measurement method and device, unmanned aerial vehicle and readable storage medium - Google Patents
Mine goaf measurement method and device, unmanned aerial vehicle and readable storage medium Download PDFInfo
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Abstract
According to the mine goaf measuring method, the device, the unmanned aerial vehicle and the readable storage medium, the laser goaf measuring technology carried by the unmanned aerial vehicle is improved, obvious floating dust interference is eliminated by comparing the distances of gray image points, and a plurality of pieces of laser acquisition data are summarized in a gray alignment mode to obtain finished laser measurement data. Through this application, reduced unmanned aerial vehicle laser measurement in-process air current dust interference, realized that all unmanned aerial vehicles gathers the operation, improved the security of gathering the work. In addition, this application is with low costs, can increase on original unmanned aerial vehicle laser acquisition equipment corresponding coaxial camera according to laser measuring apparatu acquisition capacity can, the hardware level improves less, with low costs, facilitate promotion.
Description
Technical Field
The invention relates to the technical field of measurement, in particular to a mine goaf measurement method and device, an unmanned aerial vehicle and a readable storage medium.
Background
In the existing mine goaf measurement, the laser measuring instrument has the advantages of high precision, high speed, convenient modeling and the like for goaf measurement, and is a goaf measurement means which is commonly used at present and is also mainstream. With the development of unmanned aerial vehicle technology, the existing goaf is especially the goaf which is not explored for a long time, and the unmanned aerial vehicle is tested to be matched with a laser measuring instrument for data acquisition and measurement for safety.
However, although safety measurement can be realized in the unmanned aerial vehicle-mounted mine goaf scanning process, a large amount of suspension dust is generated below the unmanned aerial vehicle due to rapid rotation of blades in the unmanned aerial vehicle flight process, so that a large amount of miscellaneous points appear in laser head scanning data carried by the unmanned aerial vehicle to influence modeling precision. Therefore, in actual use, the measurement technology of the mine goaf of the pure unmanned aerial vehicle is not trusted, auxiliary measurement is still needed manually, and although the cost of labor time is reduced, the measurement technology has disadvantages in total time spent for pure manual measurement collection and cannot be popularized and used.
Technical staff carry out technical investigation on related documents and patent data in the research and development process, such as an underground coal mining mine accurate monitoring control method of CN115713599A, the scheme theory is feasible, but modeling accuracy is also problematic on the basis of not solving acquisition accuracy; CN111335953a is a safety monitoring system and method for metal mine, which can obtain goaf path shape but can not realize measurement, in addition, GPS can not be normally used in underground signal; CN110147714a is based on unmanned aerial vehicle coal mine goaf crack recognition method and detection system, and infrared image mode is adopted to have advantages in measuring the size and shape of the crack, and has defects in accurate ranging modeling.
Therefore, the problem of air flow dust interference existing in the laser measurement process of the unmanned aerial vehicle is to be solved, and the measurement accuracy is improved so as to promote the practical application of the mine goaf of the unmanned aerial vehicle.
Disclosure of Invention
In order to remedy the defects, the invention provides a mine goaf measuring method, a device, an unmanned aerial vehicle and a readable storage medium.
The technical scheme of the method comprises the following steps: a mine goaf measurement method comprising:
(1) Simultaneously acquiring a gray level image and a laser measurement data set of a mining area by an unmanned aerial vehicle;
(2) Converting the laser measurement data set into a position-distance bitmap, substituting the position-distance bitmap into a gray level image, and generating a position-distance-gray level matrix of the mining area;
(3) Denoising the matrix according to a preset denoising algorithm f and a denoising threshold value, and replacing the positions of the noisy points with blank points to obtain a comprehensive matrix P1;
(4) Acquiring gray level images and laser measurement data sets of the mining area again by the unmanned aerial vehicle at the same angle, the same height and the same position, and repeating the steps (2) and (3) to obtain a comprehensive matrix P2;
(5) According to the point gray scale rule of the comprehensive matrix P1 and the comprehensive matrix P2, the comprehensive matrix P1 and the comprehensive matrix P2 are aligned in position;
(6) Supplementing blank point data of the comprehensive matrix P2 by the corresponding data of the comprehensive matrix P1, and supplementing blank point data of the comprehensive matrix P1 by the corresponding data of the comprehensive matrix P2;
(7) Calculating whether the distance difference value of each point position of the comprehensive matrix P1 and the comprehensive matrix P2 is within an effective threshold value, if so, recording the distance difference value as an effective point position, and if not, recording the distance difference value as an ineffective point position;
(8) If all the point positions are effective point positions, generating a measurement data set according to the effective point position information and outputting the measurement data set;
if invalid point positions exist, generating a new comprehensive matrix P1 according to all the valid point positions, and repeating the steps (4) - (7) on the basis of the new comprehensive matrix P1.
Further, the unmanned aerial vehicle obtains gray level images and laser measurement data sets through the homodromous camera module and the laser measurement module.
Further, the gray level image is consistent with the collecting center point of the laser measurement data set, and the collecting range of the gray level image is larger than that of the laser measurement data set.
Further, the denoising algorithm f is a difference comparison method, namely, calculating whether the difference value between the distance data and the gray data of each point in the position-distance-gray matrix and the distance data of surrounding points and the gray data is within a denoising threshold value, if so, reserving, and if not, marking that the noise point is a blank point.
Further, the process of aligning the comprehensive matrix P1 with the comprehensive matrix P2 includes extracting a point gray scale rule of a specific area of the comprehensive matrix P1 and aligning the point with a corresponding gray scale rule of the area corresponding to the comprehensive matrix P2.
Further, the process of extracting the point gray scale rule of the specific area of the comprehensive matrix P1 and aligning the point with the corresponding rule of the corresponding area of the comprehensive matrix P2 includes:
extracting N (N is more than or equal to 3) point gray data sets P1 in a central area of the comprehensive matrix P1, obtaining a gray maximum point, a gray minimum point and a gray intermediate value point of the area, and obtaining respective distances and respective gray difference values of the three points;
extracting 3N x 3N point gray data sets P2 of the central area of the comprehensive matrix P2, and selecting three points with corresponding distances and corresponding gray difference values from the gray data sets P2 as three alignment points;
and respectively aligning the gray maximum value point, the gray minimum value point and the gray intermediate value point in the gray data set P1 with corresponding alignment points to realize the alignment of the comprehensive matrix P1 and the comprehensive matrix P2.
The device comprises an image acquisition module and a laser measurement module which are coaxially arranged, and the image acquisition module and the laser measurement module are used for image acquisition and laser measurement in the mine goaf measurement method.
Unmanned aerial vehicle, unmanned aerial vehicle carries foretell device and carries out mine goaf measurement, unmanned aerial vehicle is many rotor unmanned aerial vehicle of rotor under-put.
A computer readable storage medium comprising a stored computer program, wherein the computer program when run controls a device in which the computer readable storage medium is located to perform a mine goaf measurement method as described above.
According to the mine goaf measuring method, device, unmanned aerial vehicle and readable storage medium, the unmanned aerial vehicle is improved in carrying laser goaf measuring technology, obvious floating dust interference is eliminated by comparing the distances of gray image points, and a plurality of pieces of laser acquisition data are subjected to data summarization in a gray alignment mode to obtain finished laser measurement data.
Through this application, reduced unmanned aerial vehicle laser measurement in-process air current dust interference, realized that all unmanned aerial vehicles gathers the operation, improved the security of gathering the work. In addition, this application is with low costs, can increase on original unmanned aerial vehicle laser acquisition equipment corresponding coaxial camera according to laser measuring apparatu acquisition capacity can, the hardware level improves less, with low costs, facilitate promotion.
Drawings
FIG. 1 is a schematic flow chart of one embodiment of the method of the present invention;
FIG. 2 is a schematic diagram of one embodiment of data acquisition during denoising of the present application;
FIG. 3 is a schematic diagram of an embodiment of the point of the comprehensive matrix P1 in the alignment process of the present application;
fig. 4 is a schematic diagram of an embodiment of the point-taking of the synthesis matrix P2 based on the embodiment of fig. 3.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, 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 the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
In one embodiment of the present application, a method of the present application includes: a mine goaf measurement method comprising:
(1) Simultaneously acquiring a gray level image and a laser measurement data set of a mining area by an unmanned aerial vehicle;
(2) Converting the laser measurement data set into a position-distance bitmap, substituting the position-distance bitmap into a gray level image, and generating a position-distance-gray level matrix of the mining area;
(3) Denoising the matrix according to a preset denoising algorithm f and a denoising threshold value, and replacing the positions of the noisy points with blank points to obtain a comprehensive matrix P1;
(4) Acquiring gray level images and laser measurement data sets of the mining area again by the unmanned aerial vehicle at the same angle, the same height and the same position, and repeating the steps (2) and (3) to obtain a comprehensive matrix P2;
(5) According to the point gray scale rule of the comprehensive matrix P1 and the comprehensive matrix P2, the comprehensive matrix P1 and the comprehensive matrix P2 are aligned in position;
(6) Supplementing blank point data of the comprehensive matrix P2 by the corresponding data of the comprehensive matrix P1, and supplementing blank point data of the comprehensive matrix P1 by the corresponding data of the comprehensive matrix P2;
(7) Calculating whether the distance difference value of each point position of the comprehensive matrix P1 and the comprehensive matrix P2 is within an effective threshold value, if so, recording the distance difference value as an effective point position, and if not, recording the distance difference value as an ineffective point position;
(8) If all the point positions are effective point positions, generating a measurement data set according to the effective point position information and outputting the measurement data set;
if invalid point positions exist, generating a new comprehensive matrix P1 according to all the valid point positions, and repeating the steps (4) - (7) on the basis of the new comprehensive matrix P1.
Due to the development of the current laser measurement technology, the laser measuring instrument is also developed from original point distance measurement to three-dimensional scanning function, the existing laser measuring instrument can realize the measuring speed from millions of points to tens of millions of points per second, and the three-dimensional image modeling of tens of millions of pixels can be realized if each measuring point is equivalent to a pixel point. In the actual use process of the mine goaf, the requirement on the measurement precision is generally in the millions of pixel level. In the application, the shaking and airflow dust interference in the unmanned aerial vehicle laser measurement process is reduced by adopting a mode of matching the gray level image with the laser measurement, and denoising and alignment are realized by adopting a technology of increasing image matching for laser measurement data. In this embodiment, based on the acquired gray-scale image, the position-distance number of the laser measurement data set is converted into megapixel-level image distance data, which is substituted into the two-dimensional coordinates of the gray-scale image, and a position-distance-gray-scale matrix of three-dimensional coordinates+gray-scale value data is generated. The unavoidable interference of air flow dust in the above matrix requires denoising the position-distance-gray matrix. Referring to fig. 2, since the airflow dust 2 floats in the air, there is a significant difference between the distance data of the airflow dust 2 and the distance data of the goaf inner wall 3 from the unmanned aerial vehicle 1, so that the main denoising method is a distance judging method, that is, whether the distance data of each point position has a continuous relationship with the surrounding data, and the upper limit of the difference value of the continuous relationship is the denoising threshold value of the application. After denoising, all dust interferences are eliminated, and the gray value and the distance value of the original point are marked as blank points to generate a comprehensive matrix P1. After a certain time (1-3 seconds), gray level images and laser measurement data sets of the mining area are acquired again at the same angle, the same height and the same position, the denoising steps are repeated, a comprehensive matrix P2 is obtained, and relevant data of the comprehensive matrix P2 and the comprehensive matrix P1 are complemented to achieve the integrity of the measurement data. In this embodiment, when all the points are valid points, a measurement data set is generated according to the valid point information, and when invalid points exist, the shooting process is repeated to ensure the integrity of the scan data.
In the application, in order to reduce the sampling times, the blank point data of the comprehensive matrix P2 is supplemented by the data corresponding to the comprehensive matrix P1, and the blank point data of the comprehensive matrix P1 is supplemented by the data corresponding to the comprehensive matrix P2 to realize complementation, because the shooting conditions of the comprehensive matrix P1 and the comprehensive matrix P2 are different and are spaced for a certain time, most of dust generated during the original P1 is shifted by at least 2 cm in the time, and no repeated invalid point exists between the newly sampled P2 and the newly sampled P1 theoretically. The sampling times are reduced through the complementary steps, and the sampling efficiency is improved.
In the above embodiment, the process of supplementing the blank point data of the synthetic matrix P2 with the data corresponding to the synthetic matrix P1, and supplementing the blank point data of the synthetic matrix P1 with the data corresponding to the synthetic matrix P2 further includes a position complementation and distance checking process, that is, after the synthetic matrix P1 is aligned with the synthetic matrix P2, searching the blank point on the synthetic matrix P1 through the gray image coordinates, extracting the coordinate point distance data of the synthetic matrix P2 according to the blank point coordinates of the synthetic matrix P1, supplementing the coordinate point distance data to the synthetic matrix P1, so as to supplement the laser scanning measurement distance data of the blank point of the synthetic matrix P1, and similarly supplementing the blank point of the synthetic matrix P2. After the data of the comprehensive matrix P1 and the data of the comprehensive matrix P2 are complemented, the denoising process of the step (3) is carried out on the comprehensive matrix P1 and the comprehensive matrix P2 respectively to judge whether the measured distance data of the new complementary point location is reasonable or not, if so, the point location data is reserved, and if not, the point location is still marked as a blank point.
On the basis of the embodiment, the unmanned aerial vehicle is preferably adopted to acquire the gray level image and the laser measurement data set through the homodromous camera module and the laser measurement module. In the application, the gray image center and the laser scanning center need to be aligned, and a software alignment mode or a homodromous shooting mode can be adopted. According to the embodiment, the shooting mode is preferentially adopted, the operation amount is saved when the gray level image and the laser scanning center are found in the later period, the operation difficulty is reduced, the interference of dust particles in the later period gray level processing is effectively reduced by the homodromous shooting, and the later period processing difficulty is reduced.
In one or more embodiments, the gray scale image is consistent with the laser measurement data set acquisition center point, and the gray scale image acquisition range is larger than the laser measurement data set acquisition range. The existing gray level image can easily shoot tens of millions of even hundreds of millions of pixel distances, but the laser scanning measuring instrument generally adopts millions of dot matrix levels for cost and unmanned aerial vehicle carrying consideration, the goaf measurement is relatively low in definition requirement, the goaf space shape is required to be accurate, the rock wall surface definition requirement is general, and the main shape modeling reference is the distance data of laser measurement, so that the gray level image acquisition pixel point position can completely cover the laser measurement data set data in consideration of cost and processing speed. Therefore, in the case of ensuring that the gray level image is consistent with the acquisition center point of the laser measurement data set in actual use, the gray level image acquisition range is larger than the acquisition range of the laser measurement data set, so as to ensure that the point of the laser measurement data is completely within the gray level image range. If the laser measuring instrument measures the lattice to be 1000 x 1000, the gray level image can meet the requirement by adopting 1200 x 1200. Of course, the implementation is not limited to matching only millions of gray level images with millions of laser measurement data sets, and if the applicant has also tested matching with tens of millions of gray level images with laser measurement data sets, in this case, it is necessary to match pixels of gray level images again according to positions of points of the laser measurement data sets, or to match one measurement point of the laser measurement data sets with a plurality of pixels, which results in a great increase in calculation amount of calculation steps in the later calculation, and in this embodiment, it is preferable that the pixel area of the gray level images is 1.2-1.8 times that of the laser measurement data sets.
On the basis of one or more embodiments, the denoising algorithm f described in connection with fig. 2 is a difference comparison method, that is, it calculates whether the difference between the distance data and the gray data of each point in the position-distance-gray matrix and the surrounding point distance data and the gray data is within the denoising threshold, if yes, it remains, if not, it is marked that the noise point becomes a blank point. In this embodiment, dust disturbed by the rotor wing of the unmanned aerial vehicle is distributed in an unordered multipoint manner, and a large difference exists between a ranging value and an actual goaf rock wall, so that after the position-distance-gray matrix is acquired, the embodiment performs denoising through a distance judgment method to remove dust interference. Because the airflow dust floats in the air, the distance data and the image distance data have obvious difference, the main denoising mode is a distance judging mode, namely whether the distance data of each point position and the surrounding data have continuous relation or not is judged, and the upper limit of the difference value of the continuous relation is the denoising threshold value of the application.
On the basis of the above one or more embodiments, the aligning the comprehensive matrix P1 with the comprehensive matrix P2 further includes extracting a point gray rule of a specific area of the comprehensive matrix P1 and aligning a point having a corresponding gray rule with a corresponding area of the comprehensive matrix P2.
On the basis of the above one or more embodiments, further, referring to fig. 3, the process of extracting the dot gray rule of the specific area of the comprehensive matrix P1 and aligning the dot with the corresponding rule of the area corresponding to the comprehensive matrix P2 includes: extracting N (N is more than or equal to 3) point gray data sets P1 in a central area of the comprehensive matrix P1, obtaining a gray maximum point, a gray minimum point and a gray intermediate value point of the area, and obtaining respective distances and respective gray difference values of the three points; extracting 3N x 3N point gray data sets P2 of the central area of the comprehensive matrix P2, and selecting three points with corresponding distances and corresponding gray difference values from the gray data sets P2 as three alignment points; and respectively aligning the gray maximum value point, the gray minimum value point and the gray intermediate value point in the gray data set P1 with corresponding alignment points to realize the alignment of the comprehensive matrix P1 and the comprehensive matrix P2. In this embodiment, due to slight shake during the shooting process of the unmanned aerial vehicle, even if the two measurement processes are the same condition measurement, there may be slight differences in the two measurement data, and thus alignment is required. Since there may be a slight difference in gray scale at the same position of the two gray scale images due to a possible difference in gray scale image white balance, exposure time, etc. during the two photographing processes, the present embodiment adopts feature alignment instead of gray scale-position alignment. On the premise that the shooting conditions of the comprehensive matrix P1 and the comprehensive matrix P2 are the same, the displacement difference is not too large, and the gray value distribution rule of the gray image is basically the same under the condition that the inner wall rock distribution of the two gray image shooting is basically the same. In this embodiment, for the same direction and the similar region as the alignment region, three points with a specific rule, such as a gray maximum point, a gray minimum point and a gray intermediate point, are located in the comprehensive matrix P1, and the gray difference value, the distance interval and the like of the three points are used as alignment features, and three points with the same response features are selected in the comprehensive matrix P2 to be aligned. Referring to fig. 3 and 4, after a certain photographing, 3*3 pixels are selected from the central area of the comprehensive matrix P1 to calculate the gray maximum value point (108), the gray minimum value point (66) and the gray intermediate value point (82), and the distribution rule of the three points is shown in fig. 3. On the basis, as shown in fig. 4, 9*9 pixels are selected from the central area of the comprehensive matrix P2, the gray values and distribution rules of the pixels are obtained, although the gray values in fig. 4 have slight differences from those in fig. 3, the three pixels are obtained to have the same distribution positions as the three pixels of the comprehensive matrix P1, the differences are approximate, and the three points satisfy that the gray values are or are approximate to the gray maximum value, the gray minimum value and the gray intermediate value in the corresponding 3*3 pixel range, and then the three points are considered as alignment points of the comprehensive matrix P2. The alignment mode is used for directional sampling, so that the sampling range is limited, the calculation force is saved, the calculation time is prolonged, and the alignment speed is increased.
The application also discloses a device, the device includes image acquisition module and the laser measurement module of coaxial setting, adopts image acquisition and laser measurement among the above-mentioned mine goaf measuring method.
The application also discloses an unmanned aerial vehicle, unmanned aerial vehicle carries foretell measuring device to carry out mine goaf measurement, unmanned aerial vehicle is many rotor unmanned aerial vehicle of rotor underlying. In this embodiment, the rotor is put down and is as far as possible reduced the rotor and blow dust back dust and fly away to the middle part (many rotors are generally installed the load at the organism middle part), and many rotor unmanned aerial vehicle, rotor quantity is 6 at least, improves unmanned aerial vehicle stability and can realize dispersion rotor wind-force simultaneously, reduces the raise dust.
The application also discloses a readable storage medium comprising a stored computer program, wherein the computer program is used for controlling equipment where the readable storage medium is located to execute any one of the mine goaf measurement methods.
The above technical solution only represents the preferred technical solution of the present invention, and some changes that may be made by those skilled in the art to some parts of the technical solution represent the principles of the present invention, and the technical solution falls within the scope of the present invention.
Claims (9)
1. A mine goaf measurement method, comprising:
(1) Simultaneously acquiring a gray level image and a laser measurement data set of a mining area by an unmanned aerial vehicle;
(2) Converting the laser measurement data set into a position-distance bitmap, substituting the position-distance bitmap into a gray level image, and generating a position-distance-gray level matrix of the mining area;
(3) Denoising the matrix according to a preset denoising algorithm f and a denoising threshold value, and replacing the positions of the noisy points with blank points to obtain a comprehensive matrix P1;
(4) Acquiring gray level images and laser measurement data sets of the mining area again by the unmanned aerial vehicle at the same angle, the same height and the same position, and repeating the steps (2) and (3) to obtain a comprehensive matrix P2;
(5) According to the point gray scale rule of the comprehensive matrix P1 and the comprehensive matrix P2, the comprehensive matrix P1 and the comprehensive matrix P2 are aligned in position;
(6) Supplementing blank point data of the comprehensive matrix P2 by the corresponding data of the comprehensive matrix P1, and supplementing blank point data of the comprehensive matrix P1 by the corresponding data of the comprehensive matrix P2;
(7) Calculating whether the distance difference value of each point position of the comprehensive matrix P1 and the comprehensive matrix P2 is within an effective threshold value, if so, recording the distance difference value as an effective point position, and if not, recording the distance difference value as an ineffective point position;
(8) If all the point positions are effective point positions, generating a measurement data set according to the effective point position information and outputting the measurement data set;
if invalid point positions exist, generating a new comprehensive matrix P1 according to all the valid point positions, and repeating the steps (4) - (7) on the basis of the new comprehensive matrix P1.
2. The mine goaf measurement method of claim 1, wherein the unmanned aerial vehicle acquires the gray scale image and the laser measurement data set through a co-directional camera module and a laser measurement module.
3. The mine goaf measurement method of claim 2, wherein the gray scale image is consistent with the laser measurement data set collection center point, and the gray scale image collection range is greater than the laser measurement data set collection range.
4. The method according to claim 1, wherein the denoising algorithm f is a difference comparison method, that is, whether the difference between the distance data and the gray data of each point in the position-distance-gray matrix and the surrounding point distance data and the gray data is within a denoising threshold value is calculated, if yes, the difference is reserved, and if not, the difference is marked as a noise point to be a blank point.
5. The method according to claim 1, wherein the aligning the comprehensive matrix P1 with the comprehensive matrix P2 includes extracting a point gray scale rule of a specific area of the comprehensive matrix P1 and aligning a point having a corresponding gray scale rule with a corresponding area of the comprehensive matrix P2.
6. The mine goaf measurement method as claimed in claim 5, wherein the process of extracting the point gray scale rule of the specific region of the comprehensive matrix P1 and aligning the point with the corresponding rule of the region corresponding to the comprehensive matrix P2 comprises the following steps:
extracting N (N is more than or equal to 3) point gray data sets P1 in a central area of the comprehensive matrix P1, obtaining a gray maximum point, a gray minimum point and a gray intermediate value point of the area, and obtaining respective distances and respective gray difference values of the three points;
extracting 3N x 3N point gray data sets P2 of the central area of the comprehensive matrix P2, and selecting three points with corresponding distances and corresponding gray difference values from the gray data sets P2 as three alignment points;
and respectively aligning the gray maximum value point, the gray minimum value point and the gray intermediate value point in the gray data set P1 with corresponding alignment points to realize the alignment of the comprehensive matrix P1 and the comprehensive matrix P2.
7. An apparatus for carrying out the measuring method according to any one of claims 1 to 6, characterized in that the apparatus comprises an image acquisition module and a laser measuring module arranged coaxially.
8. An unmanned aerial vehicle, characterized in that the unmanned aerial vehicle carries the device of claim 7 and carries out mine goaf measurement, unmanned aerial vehicle is many rotor unmanned aerial vehicle that the rotor was put down.
9. A readable storage medium, characterized in that the readable storage medium comprises a stored computer program, wherein the computer program, when run, controls a device in which the readable storage medium is located to perform the mine goaf measurement method according to any one of claims 1 to 6.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202310752224.7A CN116518942B (en) | 2023-06-26 | 2023-06-26 | Mine goaf measurement method and device, unmanned aerial vehicle and readable storage medium |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202310752224.7A CN116518942B (en) | 2023-06-26 | 2023-06-26 | Mine goaf measurement method and device, unmanned aerial vehicle and readable storage medium |
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| CN116518942A true CN116518942A (en) | 2023-08-01 |
| CN116518942B CN116518942B (en) | 2023-10-20 |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002077646A (en) * | 2000-08-24 | 2002-03-15 | Fuji Xerox Co Ltd | Apparatus and method of image processing |
| CN106969751A (en) * | 2017-03-13 | 2017-07-21 | 西安科技大学 | A kind of method of the coal mining subsidence amount monitoring calculation based on unmanned aerial vehicle remote sensing |
| CN107990876A (en) * | 2017-11-20 | 2018-05-04 | 北京科技大学 | The quick scanning means in underground mine goaf and method based on unmanned vehicle |
| CN108680137A (en) * | 2018-04-24 | 2018-10-19 | 天津职业技术师范大学 | Earth subsidence detection method and detection device based on unmanned plane and Ground Penetrating Radar |
| CN109143257A (en) * | 2018-07-11 | 2019-01-04 | 中国地质调查局西安地质调查中心 | Unmanned aerial vehicle onboard radar mining land change monitors system and method |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002077646A (en) * | 2000-08-24 | 2002-03-15 | Fuji Xerox Co Ltd | Apparatus and method of image processing |
| CN106969751A (en) * | 2017-03-13 | 2017-07-21 | 西安科技大学 | A kind of method of the coal mining subsidence amount monitoring calculation based on unmanned aerial vehicle remote sensing |
| CN107990876A (en) * | 2017-11-20 | 2018-05-04 | 北京科技大学 | The quick scanning means in underground mine goaf and method based on unmanned vehicle |
| CN108680137A (en) * | 2018-04-24 | 2018-10-19 | 天津职业技术师范大学 | Earth subsidence detection method and detection device based on unmanned plane and Ground Penetrating Radar |
| CN109143257A (en) * | 2018-07-11 | 2019-01-04 | 中国地质调查局西安地质调查中心 | Unmanned aerial vehicle onboard radar mining land change monitors system and method |
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