A degradation detection method, system and device for underground tunnel environment

Abstract

The present invention proposes a degradation detection method, system and equipment for underground tunnel environments, which belongs to the field of detection and positioning technology for underground tunnel environments, and includes the following steps: constructing a cylindrical space adapted to the structural characteristics of the tunnel with the origin of the laser radar coordinate system as the center; dividing the cylindrical space into point clouds to obtain multiple block areas; calculating the normal vector of each block area and establishing a local area reference normal vector; correcting the normal vector direction using the local area reference normal vector; establishing a spherical histogram, dividing the intervals by the azimuth and pitch angles of the corrected normal vector, and projecting the normal vector after spherical coordinate system conversion; calculating a degradation judgment factor based on the projection result, and using the degradation judgment factor to judge whether the underground tunnel environment has been degraded. Based on a degradation detection method for an underground tunnel environment, a degradation detection system and equipment for an underground tunnel environment are also proposed. The present invention improves the positioning and mapping accuracy in complex environments.

Description

Technical Field

[0001] The present invention belongs to the technical field of detection and positioning of underground tunnel environments, and in particular relates to a degradation detection method, system and equipment for underground tunnel environments. Background Art

[0002] With the rapid development of autonomous mobile robots, unmanned driving, and smart warehousing, simultaneous localization and mapping (SLAM) technology, a core module for environmental perception and autonomous navigation, is facing increasingly complex application scenarios. Current mainstream SLAM methods can be categorized into two categories: vision and laser. Vision solutions offer the advantages of low cost and lightweight, but are limited by the physical characteristics of visual sensors and perform poorly in environments with low texture and sudden changes in illumination. LiDAR, with its high-precision ranging and strong anti-interference capabilities, is widely used in structured environments such as urban roads, indoor areas, and industrial parks. However, laser SLAM systems still face a significant challenge in practical applications: the degradation problem. When a robot moves in areas with insufficient geometric constraints, the lack of observation information leads to accumulated errors in unobservable directions, resulting in positioning drift and map distortion. This challenge is further exacerbated in extremely complex scenarios, such as mine tunnels. As the core transportation channels for underground resource extraction, their topology is typically narrow, low, and multi-branched. Influenced by geological structure and mining processes, tunnel walls often exhibit localized curvature, irregular deformations, and cracks. This type of environment poses a dual challenge to the laser SLAM system: on the one hand, the main structure of the tunnel is mostly long and straight or has a gently changing curvature. When the laser radar moves axially along the tunnel, it is easy to cause degradation along the forward direction due to insufficient geometric constraint dimensions in the scanning plane; on the other hand, the local irregularities of the tunnel wall will lead to uneven distribution of point cloud density.

[0003] Existing degradation detection methods for underground tunnel environments primarily include impact echo and laser scanning technologies. The impact echo method uses ultrasonic and acoustic waves generated by steel ball impacts to inspect structures. Sensors record multiple reflected waves to analyze structural integrity. Laser scanning technology rapidly acquires three-dimensional data from the tunnel interior and, by comparing data at different time points, can detect changes and degradation in the tunnel structure. However, both methods share common drawbacks, including high cost, complex operation, significant environmental impact, and lengthy data processing times. These shortcomings may limit their widespread adoption in practical applications. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention proposes a degradation detection method, system and device for underground tunnel environments, which improves the positioning and mapping accuracy in complex environments.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A degradation detection method for an underground tunnel environment comprises the following steps:

[0007] A cylindrical space adapted to the tunnel structure characteristics is constructed with the origin of the laser radar coordinate system as the center; the point cloud of the cylindrical space is divided into multiple block areas;

[0008] Calculate the normal vector of each block area and establish the local area reference normal vector; use the local area reference normal vector to correct the normal vector direction;

[0009] A spherical histogram is established, and the normal vector is divided into intervals according to its azimuth and elevation angles after correction, and the normal vector is transformed into a spherical coordinate system and then projected;

[0010] The degradation judgment factor is calculated according to the projection results, and is used to judge whether the underground tunnel environment is degraded.

[0011] The present invention also proposes a degradation detection system for underground tunnel environments, comprising:

[0012] A construction module is used to construct a cylindrical space adapted to the tunnel structure characteristics with the origin of the laser radar coordinate system as the center; the point cloud of the cylindrical space is divided into multiple block areas;

[0013] The correction module is used to calculate the normal vector of each block area and establish a local area reference normal vector; and use the local area reference normal vector to correct the normal vector direction;

[0014] A conversion module is used to establish a spherical histogram, divide the intervals into the azimuth and elevation angles of the corrected normal vector, and transform the normal vector into a spherical coordinate system and then project it;

[0015] The detection module is used to calculate the degradation judgment factor according to the projection result, and use the degradation judgment factor to judge whether the underground tunnel environment is degraded.

[0016] The present invention also proposes a degradation detection device for underground tunnel environments, comprising:

[0017] memory for storing computer programs;

[0018] A processor is used to implement the steps of the degradation detection method for an underground tunnel environment when executing the computer program.

[0019] The effects provided in the summary of the invention are only the effects of the embodiments, not all the effects of the invention. One of the above technical solutions has the following advantages or beneficial effects:

[0020] The present invention proposes a degradation detection method, system, and device for underground tunnel environments, belonging to the field of detection and positioning technology for underground tunnel environments. The method comprises the following steps: constructing a cylindrical space centered on the origin of a laser radar coordinate system that adapts to the structural characteristics of the tunnel; segmenting the cylindrical space into multiple block regions; calculating the normal vector for each block region and establishing a local reference normal vector; correcting the normal vector direction using the local reference normal vector; establishing a spherical histogram, dividing the normal vector into intervals based on the azimuth and elevation angles after correction, and projecting the normal vector after spherical coordinate conversion; calculating a degradation judgment factor based on the projection results, and using the degradation judgment factor to determine whether the underground tunnel environment is degraded. Based on the degradation detection method for underground tunnel environments, a degradation detection system and device for underground tunnel environments are also proposed. The present invention utilizes a fast degradation detection factor based on the normal vector distribution to achieve real-time identification of degradation status by analyzing the statistical characteristics of the normal vector set, achieving information reuse while reducing time complexity. This allows for targeted compensation and improves positioning and mapping accuracy in complex environments.

[0021] The implementation of the present invention has no special requirements on hardware devices, has a wide range of applications, and can cope with various complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of a degradation detection method for an underground tunnel environment proposed in Example 1 of the present invention;

[0023] Figure 2 This is a schematic diagram of a degradation detection system for an underground tunnel environment proposed in Example 2 of the present invention;

[0024] Figure 3 This is a schematic diagram of a degradation detection device for an underground tunnel environment proposed in Example 3 of the present invention. DETAILED DESCRIPTION

[0025] In order to clearly illustrate the technical features of this solution, the present invention is described in detail below through specific implementation methods and in conjunction with the accompanying drawings. The disclosure below provides many different embodiments or examples for realizing different structures of the present invention. In order to simplify the disclosure of the present invention, the components and settings of specific examples are described below. In addition, the present invention may repeat reference numbers and / or letters in different examples. This repetition is for the purpose of simplicity and clarity and does not itself indicate the relationship between the various embodiments and / or settings discussed. It should be noted that the components illustrated in the accompanying drawings are not necessarily drawn to scale. The present invention omits descriptions of well-known components and processing technologies and processes to avoid unnecessary limitations on the present invention.

[0026] Example 1

[0027] Embodiment 1 of the present invention proposes a degradation detection method, system, and device for underground tunnel environments, which is used to solve the technical problems of degradation detection in long straight environments in the prior art. This application utilizes the concave and convex characteristics of mine tunnels to perform targeted feature extraction.

[0028] Figure 1 This is a flow chart of a degradation detection method for an underground tunnel environment proposed in Example 1 of the present invention;

[0029] In step S100, a cylindrical space adapted to the structural characteristics of the tunnel is constructed with the origin of the laser radar coordinate system as the center; the cylindrical space is divided into point clouds to obtain a plurality of block areas.

[0030] In this application, the origin of the laser radar coordinate system is used as the center of the cylindrical space, and the point cloud is divided into multiple non-uniform block areas based on azimuth and altitude. The specific implementation process is as follows:

[0031] ;

[0032] ;

[0033] in, is the interval coordinate in the cylindrical coordinate system; is the interval azimuth span; Indicates the number of azimuth intervals into which the columnar interval is divided; Indicates the number of height intervals that the column interval is divided into; Representative Azimuth intervals; Representative height intervals; Represents the bottom span of the interval; Represents the middle span of the interval; Represents the top span of the interval; is the highest point among the scan points; is the lowest point among the scan points;

[0034] ;

[0035] Get any point The direction angle of ;

[0036] in, Represents any point In the columnar range axis coordinates; Represents any point In the columnar range axis coordinates; Represents any point In the columnar range axis coordinates; Represents any point The corresponding direction angle.

[0037] Because the top (vault) and bottom (ground) have obvious planar features and dense point clouds, the middle area (sidewall) has relatively sparse point clouds. In this application, non-uniform height stratification is used to adapt to the characteristics of the tunnel structure, specifically:

[0038] ; .

[0039] The azimuth angle division is achieved through the atan2 function to achieve full 360° coverage (value range [-180°, 180°]), avoiding the angle jump problem of the traditional arctan function when x<0.

[0040] Dynamic partition example:

[0041] when When the height is divided into [ 、 ,..., ,in, .

[0042] In step S110 , the normal vector of each block region is calculated to establish a local region reference normal vector; and the direction of the normal vector is corrected using the local region reference normal vector.

[0043] Determine the local neighborhood point set of any point in the point cloud, specifically:

[0044] ;

[0045] in, is the pre-processed current scan point cloud set; is the search radius; for middle Neighborhood points of

[0046] Constructing the local neighborhood covariance matrix using local neighborhood points :

[0047] ;

[0048] in, express The average value of

[0049] Perform eigendecomposition on the local neighborhood covariance matrix, and the eigenvector corresponding to the minimum eigenvalue obtained is the normal vector of any point;

[0050] ;

[0051] in, is the local neighborhood covariance matrix The corresponding first eigenvalue; is the local neighborhood covariance matrix The corresponding second eigenvalue; is the local neighborhood covariance matrix The corresponding third eigenvalue; and ; is the eigenvector corresponding to the first eigenvalue; is the eigenvector corresponding to the second eigenvalue; is the eigenvector corresponding to the third eigenvalue; the minimum eigenvalue The corresponding eigenvector That is the point The normal vector of .

[0052] when In the column range When the normal vector of the point cloud is corrected for:

[0053] ;

[0054] in, is the reference normal vector for each cylindrical interval.

[0055] Neighborhood search uses radius For spherical queries, SVD is used to ensure numerical stability during the eigendecomposition of the covariance matrix.

[0056] Normal vector correction is performed by determining the dot product sign to ensure that all normal vectors point to the origin of the LiDAR coordinate system, thus resolving the ambiguity in the direction of the normal vectors on both sides of the plane.

[0057] In step S120 , a spherical histogram is created, and the normal vector is divided into intervals according to the azimuth and elevation angles after correction, and the normal vector is transformed into a spherical coordinate system and then projected.

[0058] A spherical histogram is created, dividing the intervals into the azimuth and elevation angles of the corrected normal vectors. Specifically:

[0059] Spherical histogram spans in azimuth and pitch angle span Divide the sphere area to obtain the interval coordinates of the spherical histogram Expressed as:

[0060]

[0061] in, Indicates the number of azimuth intervals into which the spherical area is divided; Indicates the number of pitch angle intervals into which the sphere area is divided; Indicates the Azimuth intervals; Indicates the pitch angle intervals; represents the azimuth span; represents the pitch angle span; and Represent the first interval coordinates and the second interval coordinates passing through the center of the sphere respectively;

[0062] For the corrected normal vector , pitch angle Calculated as:

[0063] ;

[0064] in, is the spherical area of ​​the corrected normal vector axis coordinates; is the spherical area of ​​the corrected normal vector axis coordinates; is the spherical area of ​​the corrected normal vector Axis coordinates.

[0065] This application uses a dual interval design to achieve seamless spherical coverage and avoid distortion in the extreme areas.

[0066] After assigning the normal vector sets to corresponding spherical histograms, the number of normal vectors corresponding to each pair of intervals is counted. Histogram projection can present a problem: when a cluster of normal vectors with roughly the same direction falls exactly on the boundary of a partitioned interval, they may be projected into multiple adjacent intervals. To address this issue, this paper proposes the following screening mechanism: First, only intervals with the largest number of normal vectors in the top third of the total interval are retained as candidates; second, the number of normal vectors in the neighborhood of a candidate interval is less than the candidate interval itself; and finally, the ratio of the number of normal vectors in the surrounding neighborhood intervals to the candidate interval must exceed a set threshold, and the neighborhood statistics that meet these criteria are merged into the candidate interval. The axially symmetric structure of the tunnel determines that the degenerate direction is essentially orthogonal to the direction of dense normal vector distribution. The interval corresponding to the degenerate direction does not appear in the interval with the largest number of normal vectors or its neighboring intervals. Therefore, this screening mechanism does not affect degradation judgment.

[0067] In step S130, a degradation judgment factor is calculated based on the projection result, and the degradation judgment factor is used to judge whether the underground tunnel environment is degraded.

[0068] The sum of the number of normal vectors in the two largest intervals in the statistical histogram , the sum of the number of normal vectors in the remaining intervals , calculate the degradation judgment factor for:

[0069] ;

[0070] When the degradation judgment factor When it is less than the preset threshold, the environment is considered to be degraded, and the cross product of the main directions of the two intervals with the most normal vectors is the degraded direction; the degradation factor Reflects the degree of environmental anisotropy: when the tunnel is non-degenerate (e.g., at an intersection), the normal vectors are uniformly distributed; Large; when degenerate (long straight channel), the normal vector is concentrated in the direction of the side wall Approaching 0.

[0071] Main direction of the tunnel and the maximum normal vector converges in the direction ( , ) satisfies the right-hand rule and physically corresponds to the cross direction of the side wall normal vector (i.e., the tunnel extension direction); the degenerate direction The calculation method is:

[0072] ;

[0073] in, Indicates the number of normal vectors in the first interval of the histogram; Indicates the number of normal vectors in the second interval of the histogram; represents the normal vector of the first interval; represents the normal vector of the second interval; Represents the main direction obtained by fitting the normal vector of the first interval; Represents the main direction obtained by fitting the normal vector of the second interval.

[0074] In this application, the parameter settings are recommended as follows:

[0075] Columnar partitioning: (15° intervals), ;

[0076] Spherical histogram: (30° intervals), (30° intervals)

[0077] Threshold selection: , the measured data show that, The positioning error increases significantly.

[0078] The scope of protection of the present invention is not limited to the specific data listed in Example 1, and those skilled in the art can make reasonable choices based on actual conditions.

[0079] Example 1 of the present invention proposes a degradation detection method for underground tunnel environments. Based on a rapid degradation detection factor for normal vector distribution, it analyzes the statistical characteristics of normal vector sets to achieve real-time identification of degradation states. This method reuses information while reducing time complexity, enabling targeted compensation and improving positioning and mapping accuracy in complex environments. Furthermore, the present invention has no specific hardware requirements and is widely applicable to various complex environments.

[0080] The core advantage of the degradation detection method for underground tunnel environments proposed in Example 1 of the present invention is that it converts complex geometric degradation problems into quantifiable statistical distribution characteristics, which not only retains physical interpretability but also meets real-time requirements.

[0081] Example 2

[0082] Based on the degradation detection method for an underground tunnel environment proposed in Example 1 of the present invention, Example 2 of the present invention further proposes a degradation detection system for an underground tunnel environment. Figure 2 This is a schematic diagram of a degradation detection system for an underground tunnel environment proposed in Example 2 of the present invention. The system includes:

[0083] A construction module is used to construct a cylindrical space adapted to the tunnel structure characteristics with the origin of the laser radar coordinate system as the center; the point cloud of the cylindrical space is divided into multiple block areas;

[0084] The correction module is used to calculate the normal vector of each block area and establish a local area reference normal vector; and use the local area reference normal vector to correct the normal vector direction;

[0085] A conversion module is used to establish a spherical histogram, divide the intervals into the azimuth and elevation angles of the corrected normal vector, and transform the normal vector into a spherical coordinate system and then project it;

[0086] The detection module is used to calculate the degradation judgment factor according to the projection result, and use the degradation judgment factor to judge whether the underground tunnel environment is degraded.

[0087] In the construction module: the point cloud of the cylindrical space is divided into blocks to obtain block areas. The specific process is: the origin of the lidar coordinate system is used as the center of the cylindrical space, and the point cloud is divided into multiple non-uniform block areas based on azimuth and altitude.

[0088] ;

[0089] ;

[0090] in, is the interval coordinate in the cylindrical coordinate system; is the interval azimuth span; Indicates the number of azimuth intervals into which the columnar interval is divided; Indicates the number of height intervals that the column interval is divided into; Representative Azimuth intervals; Representative height intervals; Represents the bottom span of the interval; Represents the middle span of the interval; Represents the top span of the interval; is the highest point among the scan points; is the lowest point among the scan points;

[0091] ;

[0092] Get any point The direction angle of ;

[0093] in, Represents any point In the columnar range axis coordinates; Represents any point In the columnar range axis coordinates; Represents any point In the columnar range axis coordinates; Represents any point The corresponding direction angle.

[0094] In the correction module: calculate the normal vector of each block area and establish the local area reference normal vector; use the local area reference normal vector to correct the normal vector direction; specifically: determine the local neighborhood points of any point in the point cloud;

[0095] A local neighborhood covariance matrix is ​​constructed using local neighborhood points; the local neighborhood covariance matrix is ​​eigendecomposed to obtain an eigenvector corresponding to the minimum eigenvalue as the normal vector of any point; and the scanned point cloud is placed in the cylindrical space according to the azimuth and altitude to correct the direction of the normal vector.

[0096] Determine the local neighborhood point set of any point in the point cloud, specifically:

[0097] ;

[0098] in, is the pre-processed current scan point cloud set; is the search radius; for middle Neighborhood points of

[0099] Constructing the local neighborhood covariance matrix using local neighborhood points :

[0100] ;

[0101] in, express The average value of

[0102] Perform eigendecomposition on the local neighborhood covariance matrix, and the eigenvector corresponding to the minimum eigenvalue obtained is the normal vector of any point;

[0103]

[0104] in, is the local neighborhood covariance matrix The corresponding first eigenvalue; is the local neighborhood covariance matrix The corresponding second eigenvalue; is the local neighborhood covariance matrix The corresponding third eigenvalue; and ; is the eigenvector corresponding to the first eigenvalue; is the eigenvector corresponding to the second eigenvalue; is the eigenvector corresponding to the third eigenvalue; the minimum eigenvalue The corresponding eigenvector That is the point The normal vector of ;

[0105] when In the column range When the normal vector of the point cloud is corrected for:

[0106] ;

[0107] in, is the reference normal vector for each cylindrical interval.

[0108] In the conversion module, a spherical histogram is established to divide the intervals by the azimuth and pitch angles of the corrected normal vectors. The specific process is as follows: the spherical histogram is divided into intervals by the azimuth span and pitch angle span Divide the sphere area to obtain the interval coordinates of the spherical histogram Expressed as:

[0109]

[0110] in, Indicates the number of azimuth intervals into which the spherical area is divided; Indicates the number of pitch angle intervals into which the sphere area is divided; Indicates the Azimuth intervals; Indicates the pitch angle intervals;

[0111] and Represent the first interval coordinates and the second interval coordinates passing through the center of the sphere respectively;

[0112] represents the azimuth span; represents the pitch angle span;

[0113] For the corrected normal vector , pitch angle Calculated as:

[0114] ;

[0115] in, is the spherical area of ​​the corrected normal vector axis coordinates; is the spherical area of ​​the corrected normal vector axis coordinates; is the spherical area of ​​the corrected normal vector Axis coordinates.

[0116] The specific process of projecting the normal vector after spherical coordinate conversion is as follows: retain the interval whose number of normal vectors is within a preset proportion of the full interval as the candidate interval; the number of normal vectors in the neighborhood of the candidate interval is less than its own number; the ratio of the number of normal vectors in the surrounding neighborhood intervals to the candidate interval is higher than the set threshold; and the neighborhood statistics that meet the conditions are merged into the candidate interval.

[0117] In the detection module, the degradation judgment factor is calculated based on the projection results, and the degradation judgment factor is used to determine whether the underground tunnel environment is degraded; specifically, the sum of the number of normal vectors in the two largest intervals in the statistical histogram is calculated. , the sum of the number of normal vectors in the remaining intervals , calculate the degradation judgment factor as:

[0118] ;

[0119] in, is the degradation judgment factor;

[0120] When the degradation judgment factor is less than the preset threshold, the environment is considered to be degraded, and the cross product of the main directions of the two intervals with the largest normal vectors is the degradation direction; The calculation method is:

[0121] ;

[0122] in, Indicates the number of normal vectors in the first interval of the histogram; Indicates the number of normal vectors in the second interval of the histogram; represents the normal vector of the first interval; represents the normal vector of the second interval; Represents the main direction obtained by fitting the normal vector of the first interval; Represents the main direction obtained by fitting the normal vector of the second interval.

[0123] Example 2 of the present invention proposes a degradation detection system for underground tunnel environments. Based on a rapid degradation detection factor for normal vector distribution, it analyzes the statistical characteristics of normal vector sets to achieve real-time identification of degradation states. This system reuses information while reducing time complexity, enabling targeted compensation and improving positioning and mapping accuracy in complex environments. Furthermore, the system requires no specialized hardware, has a wide range of applications, and can handle a variety of complex environments.

[0124] The core advantage of a degradation detection system for underground tunnel environments proposed in Example 2 of the present invention is that it converts complex geometric degradation problems into quantifiable statistical distribution characteristics, which not only retains physical interpretability but also meets real-time requirements.

[0125] Example 3

[0126] The present invention also proposes a device, Figure 3 This is a schematic diagram of a degradation detection device for an underground tunnel environment proposed in Example 3 of the present invention, including:

[0127] memory for storing computer programs;

[0128] The processor is used to implement the following method steps when executing the computer program:

[0129] In step S100, a cylindrical space adapted to the structural characteristics of the tunnel is constructed with the origin of the laser radar coordinate system as the center; the cylindrical space is divided into point clouds to obtain a plurality of block areas.

[0130] In step S110 , the normal vector of each block region is calculated to establish a local region reference normal vector; and the direction of the normal vector is corrected using the local region reference normal vector.

[0131] In step S120 , a spherical histogram is created, and the normal vector is divided into intervals according to the azimuth and elevation angles after correction, and the normal vector is transformed into a spherical coordinate system and then projected.

[0132] In step S130, a degradation judgment factor is calculated based on the projection result, and the degradation judgment factor is used to judge whether the underground tunnel environment is degraded.

[0133] Example 3 of the present invention proposes a degradation detection device for underground tunnel environments. Based on a rapid degradation detection factor for normal vector distribution, it analyzes the statistical characteristics of normal vector sets to achieve real-time identification of degradation states. This enables information reuse while reducing time complexity, enabling targeted compensation and improving positioning and mapping accuracy in complex environments. Furthermore, the present invention has no specific hardware requirements and is widely applicable to various complex environments.

[0134] The core advantage of a degradation detection device for underground tunnel environments proposed in Example 3 of the present invention is that it converts complex geometric degradation problems into quantifiable statistical distribution characteristics, which not only retains physical interpretability but also meets real-time requirements.

[0135] It should be noted that the technical solution of the present invention also provides an electronic device, including: a communication interface capable of exchanging information with other devices such as network devices; a processor connected to the communication interface to realize information exchange with other devices, and used to execute a degradation detection method for an underground tunnel environment provided by one or more of the above technical solutions when running a computer program, and the computer program is stored on a memory. Of course, in actual application, the various components in the electronic device are coupled together through a bus system. It can be understood that the bus system is used to realize connection and communication between these components. In addition to the data bus, the bus system also includes a power bus, a control bus, and a status signal bus. The memory in the embodiment of the present application is used to store various types of data to support the operation of the electronic device. Examples of these data include: any computer program for operating on the electronic device. It can be understood that the memory can be a volatile memory or a non-volatile memory, or it can include both volatile and non-volatile memories. Among them, non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disk, or compact disc read-only memory (CD-ROM); magnetic surface memory can be magnetic disk memory or magnetic tape memory. Volatile memory can be random access memory (RAM), which is used as an external cache.By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronized dynamic random access memory (SLDRAM), direct RAM bus random access memory (DRRAM). The memory described in the embodiments of the present application is intended to include but is not limited to these and any other suitable types of memory. The method disclosed in the above embodiments of the present application can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above-described method can be completed by hardware integrated logic circuits or software instructions within a processor. The processor can be a general-purpose processor, a DSP (Digital Signal Processing), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc. The processor can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules within the decoding processor. The software module can be located in a storage medium located in memory. The processor reads the program from the memory and, in conjunction with its hardware, completes the steps of the aforementioned method. When the processor executes the program, the corresponding processes of the various methods in the embodiments of this application are implemented. For the sake of brevity, these steps are not further described here.

[0136] The description of the relevant parts of the degradation detection device for an underground tunnel environment provided in Example 3 of the present application can be found in the detailed description of the corresponding parts of the degradation detection method for an underground tunnel environment provided in Example 1 of the present application, and will not be repeated here.

[0137] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device comprising a series of elements are inherent to the elements. In the absence of further restrictions, the elements limited by the statement "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device comprising the elements. In addition, the above-mentioned technical solutions provided in the embodiments of the present application are not described in detail in accordance with the corresponding technical solutions in the prior art to achieve the same principle, so as to avoid excessive elaboration.

[0138] Although the above description is of specific embodiments of the present invention in conjunction with the accompanying drawings, it does not limit the scope of protection of the present invention. For those skilled in the art, other different forms of modifications or variations can be made based on the above description. It is not necessary and impossible to list all embodiments here. Based on the technical solution of the present invention, various modifications or variations that can be made by those skilled in the art without expending creative effort are still within the scope of protection of the present invention.

Claims

1. A degradation detection method for underground tunnel environment, characterized in that: The following steps are involved: A cylindrical space adapted to the tunnel structure characteristics is constructed with the origin of the laser radar coordinate system as the center; Divide the point cloud of the cylindrical space into multiple block areas; Calculate the normal vector of each block area and establish the reference normal vector of the local area; Correct the normal vector direction using the local area reference normal vector; A spherical histogram is established, and the normal vectors are divided into intervals according to the azimuth and elevation angles after correction, and the normal vectors are transformed into a spherical coordinate system and then projected. Specifically, the normal vectors are transformed into a spherical coordinate system and then projected. Specifically, intervals with a preset proportion of normal vectors before the full interval are retained as candidate intervals. The number of normal vectors in the neighborhood of the candidate interval is less than the number of normal vectors in the candidate interval itself. The ratio of the number of normal vectors in the surrounding neighborhood intervals to the candidate interval is higher than a set threshold. The neighborhood statistics that meet the conditions are merged into the candidate interval. The degradation judgment factor is calculated based on the projection results, and the degradation judgment factor is used to judge whether the underground tunnel environment is degraded; specifically, the sum of the number of normal vectors in the two largest intervals in the statistical histogram is , the sum of the number of normal vectors in the remaining intervals , calculate the degradation judgment factor as: ； in, is the degradation judgment factor; When the degradation judgment factor is less than the preset threshold, the environment is considered to be degraded, and the cross product of the main directions of the two intervals with the largest normal vectors is the degradation direction; Degeneration direction The calculation method is: ； in, Indicates the number of normal vectors in the first interval of the histogram; Indicates the number of normal vectors in the second interval of the histogram; represents the normal vector of the first interval; represents the normal vector of the second interval; Represents the main direction obtained by fitting the normal vector of the first interval; Represents the main direction obtained by fitting the normal vector of the second interval.

2. The degradation detection method for underground tunnel environment according to claim 1, characterized in that: The point cloud of the cylindrical space is divided into blocks to obtain the block areas, specifically: The origin of the lidar coordinate system is used as the center of the cylindrical space, and the point cloud is divided into multiple non-uniform block areas based on azimuth and altitude.

3. The degradation detection method for underground tunnel environment according to claim 2, characterized in that: Calculate the normal vector of each block area and establish the local area reference normal vector; use the local area reference normal vector to correct the normal vector direction; specifically: Determine the local neighborhood points of any point in the point cloud; Construct the local neighborhood covariance matrix using local neighborhood points; Perform eigendecomposition on the local neighborhood covariance matrix, and obtain the eigenvector corresponding to the minimum eigenvalue as the normal vector of any point; The scanned point cloud is placed into the cylindrical space according to the azimuth and altitude to correct the normal vector direction.

4. The degradation detection method for underground tunnel environment according to claim 3, characterized in that: With the origin of the LiDAR coordinate system as the center of the cylindrical space, the point cloud is divided into multiple non-uniform block areas based on azimuth and altitude, specifically: ； ； in, is the interval coordinate in the cylindrical coordinate system; is the interval azimuth span; Indicates the number of azimuth intervals into which the columnar interval is divided; Indicates the number of height intervals that the column interval is divided into; Representative Azimuth intervals; Representative height intervals; Represents the bottom span of the interval; Represents the middle span of the interval; Represents the top span of the interval; is the highest point among the scan points; is the lowest point among the scan points; ； Get any point The direction angle of ; in, Represents any point In the columnar range axis coordinates; Represents any point In the columnar range axis coordinates; Represents any point In the columnar range axis coordinates; Represents any point The corresponding direction angle.

5. The degradation detection method for underground tunnel environment according to claim 4, characterized in that: Determine the local neighborhood point set of any point in the point cloud, specifically: ； in, is the pre-processed current scan point cloud set; is the search radius; for middle Neighborhood points of Constructing the local neighborhood covariance matrix using local neighborhood points : ； in, express The average value of Perform eigendecomposition on the local neighborhood covariance matrix, and the eigenvector corresponding to the minimum eigenvalue obtained is the normal vector of any point; in, is the local neighborhood covariance matrix The corresponding first eigenvalue; is the local neighborhood covariance matrix The corresponding second eigenvalue; is the local neighborhood covariance matrix The corresponding third eigenvalue; and ; is the eigenvector corresponding to the first eigenvalue; is the eigenvector corresponding to the second eigenvalue; is the eigenvector corresponding to the third eigenvalue; the minimum eigenvalue The corresponding eigenvector That is the point The normal vector of ; when In the column range When the normal vector of the point cloud is corrected for: ； in, is the reference normal vector for each cylindrical interval.

6. The degradation detection method for underground tunnel environment according to claim 5, characterized in that: A spherical histogram is created, dividing the intervals into the azimuth and elevation angles of the corrected normal vectors. Specifically: Spherical histogram spans in azimuth and pitch angle span Divide the sphere area to obtain the interval coordinates of the spherical histogram Expressed as: in, Indicates the number of azimuth intervals into which the spherical area is divided; Indicates the number of pitch angle intervals into which the sphere area is divided; Indicates the Azimuth intervals; Indicates the pitch angle intervals; and Represent the first interval coordinates and the second interval coordinates passing through the center of the sphere respectively; represents the azimuth span; represents the pitch angle span; For the corrected normal vector , pitch angle Calculated as: ； in, is the spherical area of ​​the corrected normal vector axis coordinates; is the spherical area of ​​the corrected normal vector axis coordinates; is the spherical area of ​​the corrected normal vector Axis coordinates.

7. A degradation detection system for an underground tunnel environment, used to execute the degradation detection method for an underground tunnel environment according to any one of claims 1 to 6, characterized in that: include: A construction module is used to construct a cylindrical space adapted to the structural characteristics of the tunnel with the origin of the laser radar coordinate system as the center; Divide the point cloud of the cylindrical space into multiple block areas; The correction module is used to calculate the normal vector of each block area and establish the reference normal vector of the local area; Correct the normal vector direction using the local area reference normal vector; A conversion module is used to establish a spherical histogram, divide the intervals into the azimuth and elevation angles of the corrected normal vector, and transform the normal vector into a spherical coordinate system and then project it; The detection module is used to calculate the degradation judgment factor according to the projection result, and use the degradation judgment factor to judge whether the underground tunnel environment is degraded.

8. A degradation detection device for underground tunnel environment, characterized in that: include: memory for storing computer programs; A processor is configured to implement the steps of a degradation detection method for an underground tunnel environment as described in any one of claims 1 to 6 when executing the computer program.

Images