Arch point cloud data extraction method and device, electronic equipment, storage medium and program product

Abstract

The embodiment of the invention provides a point cloud data extraction method and device of a lagging jack, electronic equipment, a storage medium and a program product. The method comprises the following steps: acquiring point cloud data and tunnel design data of a tunnel; calculating and generating tunnel point cloud information according to the point cloud data of the tunnel and the tunnel design data; constructing a point cloud expansion coordinate system according to the tunnel point cloud information; eliminating tunnel contour points through normal vectors according to the tunnel point cloud information and the point cloud expansion coordinate system so as to determine initial lagging jack mileage data; and point cloud extraction is performed on the initial lagging jack mileage data to obtain lagging jack point cloud data, so that the accuracy of the extracted point cloud data is improved.

Description

Methods, apparatus, electronic devices, storage media, and software products for extracting point cloud data from arch frames. Technical Field

[0001] This application relates to the field of tunnel construction technology, and in particular to a method, apparatus, electronic device, storage medium and program product for extracting point cloud data of an arch frame. Background Technology

[0002] In tunnel construction, the arch frame is a key support structure, and the spatial location information of the arch frame point cloud needs to be obtained through three-dimensional scanning technology to guide subsequent concrete spraying operations.

[0003] In existing technologies, the main method for extracting point clouds from tunnel arch frames is template matching. The arch frame model is designed as a template and matched with the point cloud scanned in the tunnel. The position of the point cloud is found through coordinate transformation.

[0004] However, in existing technologies, when the acquired point cloud is occluded or missing, the accuracy of the data decreases. Summary of the Invention

[0005] This application provides a method, apparatus, electronic device, storage medium, and program product for extracting point cloud data of an arch frame, in order to solve the problem of decreased data accuracy in the prior art.

[0006] In a first aspect, embodiments of this application provide a method for extracting point cloud data of an arch frame, including:

[0007] Acquire point cloud data and tunnel design data for the tunnel;

[0008] Tunnel point cloud information is generated based on the tunnel point cloud data and the tunnel design data;

[0009] Construct a point cloud unfolding coordinate system based on the tunnel point cloud information;

[0010] The tunnel outline points are eliminated by using the normal vector based on the tunnel point cloud information and the coordinate system of the expanded point cloud to determine the initial arch frame mileage data;

[0011] Point cloud data of the initial arch frame mileage is extracted.

[0012] In one possible implementation, the step of calculating and generating tunnel point cloud information based on the tunnel point cloud data and the tunnel design data includes: comparing the tunnel point cloud data with the tunnel data, and calculating the point cloud mileage of each point cloud; calculating the rotation matrix between the tunnel coordinate system and the working face coordinate system based on the point cloud mileage of each point cloud, and transforming each point cloud into the working face coordinate system; calculating the over-excavation and under-excavation data and the circumferential length data of each point cloud in the working face coordinate system, and recording the point cloud mileage, the over-excavation and under-excavation data, and the circumferential length data of each point cloud as point cloud information.

[0013] In one possible implementation, the step of eliminating tunnel contour points based on the tunnel point cloud information and the expanded coordinate system of the point cloud using normal vectors to determine the initial arch mileage data includes: calculating the normal vector of the tunnel point cloud information in the expanded coordinate system of the point cloud using a tree search algorithm; eliminating tunnel contour points based on the normal vectors to obtain candidate arch points; and performing over-excavation and under-excavation change analysis on the candidate arch points to generate the initial arch mileage data.

[0014] In one possible implementation, the step of analyzing the over-excavation and under-excavation changes of the candidate arch points to generate initial arch mileage data includes: slicing the candidate arch points according to a preset arch thickness and slice thickness to obtain multiple sets of point cloud slices; calculating the over-excavation and under-excavation change rate of the tunnel point cloud along the longitudinal direction of the point cloud unfolding coordinate system based on the multiple sets of point cloud slices to obtain the change rate value of the multiple sets of point cloud slices; and calculating the average value of the change rate values ​​of the multiple sets of point cloud slices to obtain the initial arch mileage data.

[0015] In one possible implementation, the step of extracting point cloud data from the initial arch frame mileage data to obtain arch frame point cloud data includes: obtaining the over-excavation and under-excavation speed variation range of the point cloud slice corresponding to the initial arch frame mileage data; determining the mileage interval of the initial arch frame mileage data based on the arch frame installation spacing and the over-excavation and under-excavation speed variation range of the point cloud slice corresponding to the initial arch frame mileage data; calculating the density mean based on the mileage interval of the initial arch frame mileage data; if the density mean meets a preset condition, determining that the point cloud slice corresponding to the initial arch frame mileage data contains arch frame point cloud data, and extracting point cloud data from the point cloud slice corresponding to the initial arch frame mileage data to obtain arch frame point cloud data.

[0016] In one possible implementation, after extracting point cloud data from the arch frame mileage data to obtain arch frame point cloud data, the method further includes: if there is missing data in the point cloud data to be sampled, recording the location information of the missing point cloud data; obtaining the point cloud data of adjacent arch frames based on the location information of the missing point cloud data; and repairing the missing point cloud data based on the point cloud data of the adjacent arch frames.

[0017] Secondly, embodiments of this application provide a point cloud data extraction device for an arch frame, comprising:

[0018] The first acquisition module is used to acquire point cloud data and tunnel design data of the tunnel;

[0019] The calculation module is used to calculate and generate tunnel point cloud information based on the tunnel point cloud data and the tunnel design data;

[0020] The construction module is used to construct a point cloud unfolding coordinate system based on the tunnel point cloud information;

[0021] The elimination module is used to eliminate tunnel contour points based on the tunnel point cloud information and the expanded coordinate system of the point cloud using normal vectors, so as to determine the initial arch mileage data;

[0022] The extraction module is used to extract point cloud data from the initial arch mileage data to obtain arch point cloud data.

[0023] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0024] The memory stores computer-executed instructions;

[0025] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0026] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0027] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0028] The method, apparatus, electronic device, storage medium, and program product for extracting point cloud data of arch frames provided in this application calculate tunnel point cloud information by acquiring tunnel point cloud data and tunnel design data, construct a point cloud unfolding coordinate system, use normal vectors to remove tunnel contour points, determine initial arch frame mileage data, and extract point cloud data from the initial arch frame mileage data, thereby improving the accuracy of the extracted point cloud data. Attached Figure Description

[0029] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0030] Figure 1 is a schematic diagram of the system structure of the computer device provided in an embodiment of this application;

[0031] Figure 2 is a flowchart illustrating the point cloud data extraction method for the arch frame provided in this application.

[0032] Figure 3 is a flowchart illustrating the point cloud data extraction method for the arch frame provided in this application (II).

[0033] Figure 4 is a schematic diagram of the point cloud data extraction device for the arch frame provided in this application;

[0034] Figure 5 is a schematic diagram of the structure of the electronic device provided in this application.

[0035] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0036] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0037] First, let me explain the terms used in this application:

[0038] Point cloud: is a collection of multiple three-dimensional coordinate points, which is obtained by scanning the tunnel with a scanner.

[0039] The tunnel face is the excavation face that continuously advances during tunnel construction.

[0040] Over-excavation and under-excavation: These are engineering terms used to describe the deviation between the actual excavation profile and the design baseline. According to design requirements, if the actual excavation cross-section exceeds the baseline, it is called over-excavation; if it falls below the baseline, it is called under-excavation.

[0041] In tunnel construction, the arch frame serves as a critical support structure, requiring the acquisition of its spatial location information (point cloud) using 3D scanning technology to guide subsequent concrete spraying operations. Current technologies primarily employ template matching for extracting point cloud data from tunnel arch frames. This involves designing the arch frame model as a template and matching it with the scanned point cloud within the tunnel, using coordinate transformation to locate the point cloud's position. However, existing technologies suffer from decreased data accuracy when the acquired point cloud is obscured or incomplete.

[0042] To address the aforementioned technical problems, this application proposes the following technical concept: The inventors, considering the acquisition of tunnel point cloud data and tunnel design data, calculate and generate tunnel point cloud information, construct a point cloud unfolding coordinate system based on the tunnel point cloud information, eliminate tunnel contour points using normal vectors, determine initial arch mileage data, and extract point cloud data from the initial arch mileage data, thereby improving the accuracy of the extracted point cloud data. Detailed embodiments are described below.

[0043] Figure 1 is a schematic diagram of the system structure of the computer device provided in an embodiment of this application. As shown in Figure 1, the computer device includes: a receiving device 101, a processing device 102, and a display device 103.

[0044] It is understood that the structure illustrated in the embodiments of this application does not constitute a specific limitation on the point cloud data extraction method for the arch frame. In other feasible embodiments of this application, the above architecture may include more or fewer components than illustrated, or combine some components, or split some components, or arrange different components, which can be determined according to the actual application scenario and is not limited here. The components shown in Figure 1 can be implemented in hardware, software, or a combination of software and hardware.

[0045] In the specific implementation process, the receiving device 101 can be an input / output interface or a communication interface, which can acquire the point cloud data and tunnel design data of the tunnel.

[0046] The processing device 102 can extract point cloud data from the initial arch frame mileage data to obtain arch frame point cloud data.

[0047] The display device 103 can be used to display the above-mentioned arch point cloud data, etc.

[0048] The display device can also be a touch screen, used to receive user commands while displaying the above content, so as to realize the operation interaction with the user.

[0049] It should be understood that the above-mentioned processing device can be implemented by a processor reading instructions from memory and executing those instructions, or it can be implemented by a chip circuit.

[0050] Furthermore, the network architecture and business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0051] Figure 2 is a flowchart illustrating the point cloud data extraction method for the arch frame provided in this application. As shown in Figure 2, the method includes:

[0052] S201: Obtain point cloud data and tunnel design data for the tunnel.

[0053] In this embodiment, a target is set in the tunnel, and the coordinates of the target in the tunnel coordinate system are measured. The vehicle-mounted scanner obtains the signal emitted by the target for positioning, calculates the conversion relationship between the scanner and the tunnel, and scans the tunnel according to the conversion relationship to obtain the point cloud data of the tunnel in the tunnel coordinate system.

[0054] In this embodiment, the point cloud data and tunnel design data of the tunnel are obtained by scanning with an on-board scanner.

[0055] In this embodiment, the tunnel design data includes, but is not limited to, tunnel line data and tunnel profile data.

[0056] S202: Calculate and generate tunnel point cloud information based on tunnel point cloud data and tunnel design data.

[0057] Specifically, the point cloud data of the tunnel is compared with the tunnel data, each point cloud is transformed into the working face coordinate system, the over-excavation and under-excavation data and circumferential length data of each point cloud are calculated in the working face coordinate system, and the point cloud mileage, over-excavation and under-excavation data and circumferential length data of each point cloud are recorded as point cloud information.

[0058] S203: Construct a point cloud unfolding coordinate system based on tunnel point cloud information.

[0059] In this embodiment, in the point cloud unfolding coordinate system, the X direction is the tunnel excavation forward direction, the Y direction is the contour circumferential unfolding direction, and the Z direction is the over-excavation and under-excavation of the point at the contour circumferential position.

[0060] S204: By using the normal vector to eliminate tunnel contour points based on tunnel point cloud information and the point cloud unfolded coordinate system, the initial arch frame mileage data is determined.

[0061] Specifically, the normal vector of the tunnel point cloud information in the point cloud unfolded coordinate system is calculated by a tree search algorithm, the tunnel outline points are eliminated, the over-excavation and under-excavation change analysis is performed on the candidate arch points, and the initial arch mileage data is generated.

[0062] S205: Extract point cloud data from the initial arch frame mileage data to obtain arch frame point cloud data.

[0063] Specifically, the over- and under-excavation speed variation range of the point cloud slices corresponding to the initial arch frame mileage data is obtained. The mileage interval of the initial arch frame mileage data is determined based on the arch frame installation spacing and the over- and under-excavation speed variation range of the point cloud slices corresponding to the initial arch frame mileage data. The density mean is calculated based on the mileage interval of the initial arch frame mileage data. If the density mean meets the preset conditions, it is determined that the point cloud slices corresponding to the initial arch frame mileage data contain arch frame point cloud data. Point cloud extraction is performed on the point cloud slices corresponding to the initial arch frame mileage data to obtain the arch frame point cloud data.

[0064] Specifically, based on the obtained arch mileage Extract the point cloud corresponding to the arch thickness at the mileage from the candidate point cloud of the arch, and sample the point cloud at intervals in the Y direction.

[0065] As can be seen from the above embodiments, by acquiring tunnel point cloud data and tunnel design data to calculate tunnel point cloud information, constructing a point cloud unfolding coordinate system, using normal vectors to remove tunnel contour points, determining initial arch frame mileage data, and extracting point cloud data from the initial arch frame mileage data, the accuracy of the extracted point cloud data is improved.

[0066] In one embodiment of this application, step S202 includes:

[0067] S2021: Compare the point cloud data of the tunnel with the tunnel data to calculate the point cloud mileage of each point cloud.

[0068] Specifically, the point cloud data of the tunnel is compared with the tunnel data, and the mileage of each point in the point cloud is obtained according to the coordinate mileage algorithm. The maximum and minimum values ​​of the point cloud mileage are recorded.

[0069] S2022: Calculate the rotation matrix between the tunnel coordinate system and the working face coordinate system based on the point cloud mileage of each point cloud, and transform each point cloud into the working face coordinate system.

[0070] Specifically, based on the angle between the tunnel horizontal curve and the tunnel vertical curve and the horizontal plane, the rotation matrix between the tunnel coordinate system and the working face coordinate system is calculated, and the center stake coordinates of the mileage point are used as the translation matrix to transform each point cloud into the working face coordinate system.

[0071] S2023: Calculate the over-excavation and under-excavation data and circumferential length data of each point cloud in the working face coordinate system, and record the point cloud mileage, over-excavation and under-excavation data and circumferential length data of each point cloud as point cloud information.

[0072] Specifically, the point cloud is compared with the designed tunnel outline, and the over-excavation and under-excavation data of each point and the circumferential length data of the corresponding outline of each point are calculated.

[0073] As can be seen from the above embodiments, by converting the tunnel point cloud data to the tunnel face coordinate system, and calculating the over-excavation and under-excavation data and circumferential length data of each point cloud in the tunnel face coordinate system, the diversity of point cloud information data is improved.

[0074] In one embodiment of this application, step S204 includes:

[0075] S2041: Calculate the normal vector of the tunnel point cloud information in the point cloud unfolded coordinate system using a tree search algorithm.

[0076] Specifically, a tree search algorithm is used to perform nearest neighbor search on the points in the point cloud unfolded coordinate system. For each point, a set of neighboring points within a certain range is found, and the normal vector of the point set is calculated through principal component analysis.

[0077] S2042: Eliminate tunnel contour points based on the normal vector to obtain candidate arch points.

[0078] Specifically, a threshold is set for the normal vector X direction, and points whose point cloud normal vectors are greater than the threshold for the normal vector X direction are selected as candidate points for the arch frame.

[0079] S2043: Analyze the over-excavation and under-excavation changes of candidate arch points to generate initial arch mileage data.

[0080] Specifically, the candidate points of the arch frame are sliced ​​according to the preset arch frame thickness and slice thickness to obtain multiple sets of point cloud slices. The over-excavation and under-excavation change rate of the tunnel point cloud along the longitudinal axis of the point cloud unfolding coordinate system is calculated based on the multiple sets of point cloud slices to obtain the change rate values ​​of the multiple sets of point cloud slices. The average value of the change rate values ​​of the multiple sets of point cloud slices is calculated to obtain the initial arch frame mileage data.

[0081] As can be seen from the above embodiments, by using the arch thickness value as the search radius and calculating the normal vector through the tree search algorithm, the accuracy of the tree search algorithm is improved. Based on the normal vector, the tunnel contour points are removed according to the threshold, which reduces the probability of mis-extraction of point cloud.

[0082] Figure 3 is a schematic flowchart of the point cloud data extraction method for the arch frame provided in this application. As shown in Figure 3, step S2043 includes:

[0083] S301: Slice the candidate points of the arch frame according to the preset arch frame thickness and slice thickness to obtain multiple sets of point cloud slices.

[0084] Specifically, the arch frame is designed with a thickness of 1.2, and the length is 1.2 × 1.2. The point cloud is sliced ​​to obtain multiple sets of point cloud slices.

[0085] S302: Calculate the over-excavation and under-excavation rate of the tunnel point cloud along the longitudinal axis of the point cloud unfolding coordinate system based on multiple sets of point cloud slices, and obtain the change rate values ​​of multiple sets of point cloud slices.

[0086] Specifically, the point cloud is sorted according to the Y direction, and the velocity change value of over- and under-dig along the Y direction for each adjacent point cloud slice is calculated.

[0087] In this embodiment, the formula for calculating the velocity variation value of over-excavation and under-excavation along the Y direction based on adjacent point cloud slices a and b is as follows:

[0088]

[0089] In the formula, This represents the velocity variation along the Y direction between adjacent point cloud slices a and b, indicating over- and under-excavation. This represents the over-mining and under-mining data of point cloud slice b; This represents the over-mining and under-mining data of point cloud slice a; This represents the circumferential length data of the contour corresponding to point cloud slice b; This represents the circumferential length data of the contour corresponding to point cloud slice a.

[0090] S303: Calculate the average value of the change rate values ​​of multiple point cloud slices to obtain the initial arch mileage data.

[0091] Specifically, the average value of the rate of change of multiple point cloud slices is calculated, and the minimum average value among all the average values ​​is taken as the initial arch mileage data.

[0092] As can be seen from the above embodiments, by slicing the candidate points of the arch frame with preset arch frame thickness and slice thickness, calculating the over- and under-excavation change rate of the point cloud along the vertical axis of the point cloud unfolding coordinate system, and obtaining the initial arch frame mileage data based on the average value of the change rate values ​​of multiple sets of point cloud slices, the integrity of the arch frame data is improved.

[0093] In one embodiment of this application, step S205 includes:

[0094] S2051: Obtain the over- and under-excavation speed variation range of the point cloud slice corresponding to the initial arch mileage data.

[0095] In this embodiment, the over- and under-excavation speed variation range of the point cloud slice corresponding to the initial arch frame mileage data is recorded as follows: .

[0096] Specifically, after obtaining the over- and under-excavation speed variation range of the point cloud slices corresponding to the initial arch frame mileage data, a tree search algorithm is used to obtain the density values ​​within the radius of the arch frame thickness value in the slice point cloud, and the average value is taken as... This serves as a reference feature for other arch point clouds to be extracted.

[0097] S2052: Determine the mileage range of the initial arch frame mileage data based on the over- and under-excavation speed variation range of the point cloud slices corresponding to the arch frame installation spacing and the initial arch frame mileage data.

[0098] Specifically, based on the arch frame installation spacing interval, the point cloud of mileage interval a and mileage interval b is sliced ​​and calculated. The minimum value of the average over-excavation and under-excavation change rate of each slice of the point cloud in mileage interval 1 and mileage interval 2 is obtained and recorded in mileage mile1 and mile2 respectively.

[0099] In this embodiment, the formula for calculating mileage interval 1 is:

[0100]

[0101] In the formula, This indicates the range of over- and under-excavation speed variations for the point cloud slice corresponding to the initial arch frame mileage data; interval indicates the arch frame installation spacing. This indicates the design thickness of the arch frame.

[0102] In this embodiment, the formula for calculating mileage interval 2 is:

[0103]

[0104] In the formula, This indicates the range of over- and under-excavation speed variations for the point cloud slice corresponding to the initial arch frame mileage data; interval indicates the arch frame installation spacing. This indicates the design thickness of the arch frame.

[0105] S2053: Calculate the average density based on the mileage interval of the initial arch mileage data.

[0106] Specifically, the mean density of point cloud slices at mile1 and mile2 is calculated. .

[0107] S2054: If the density mean meets the preset conditions, then the point cloud slice corresponding to the initial arch mileage data is determined to contain the arch point cloud data, and the point cloud is extracted from the point cloud slice corresponding to the initial arch mileage data to obtain the arch point cloud data.

[0108] Specifically, if If so, the slice point cloud of that mileage is identified as containing the arch point cloud. And this mileage is used as... Search for other arch mileages within an arch spacing interval of the mileage interval until the search interval yields the maximum and minimum mileage interval of the point cloud.

[0109] As can be seen from the above embodiments, by statistically analyzing the over- and under-excavation speed variation range of point cloud slices, the mileage interval is determined based on the arch frame installation spacing and the over- and under-excavation speed variation range. The average density within the mileage interval is calculated and compared with the preset average density. If it meets the preset average density, point cloud extraction is performed to obtain the arch frame point cloud data, thereby improving the accuracy of point cloud data extraction.

[0110] In one embodiment of this application, after step S205, the method further includes:

[0111] S206: If there are missing data in the point cloud data to be sampled, record the location information of the missing point cloud data.

[0112] In this embodiment, during the point cloud extraction process, the arch frame point cloud data is checked for holes or sparse regions. If data loss is found (e.g., due to scan occlusion or noise), the mileage range and circumferential position of the missing region are recorded to form a missing location information table.

[0113] S207: Obtain the point cloud data of adjacent arches based on the location information of the missing point cloud data.

[0114] Specifically, based on the missing location information, the point cloud data of the corresponding circumferential location is extracted from the point cloud data of adjacent arch installation locations (such as the previous and next arches) as reference data. The point cloud data of adjacent arches is obtained from the processed point cloud information.

[0115] S208: Repair missing point cloud data based on point cloud data of adjacent arches.

[0116] Specifically, interpolation or fitting methods are used to reconstruct the point cloud of the missing area based on the point cloud data of adjacent arches.

[0117] As can be seen from the above embodiments, when the point cloud data to be sampled is missing, the point cloud data of the adjacent arch frame is obtained by recording the location information of the missing point cloud data, and the missing point cloud data is repaired based on the point cloud data of the adjacent arch frame to ensure the integrity of the point cloud data.

[0118] Figure 4 is a schematic diagram of the point cloud data extraction device for the arch frame provided in this application. As shown in Figure 4, the point cloud data extraction device 40 for the arch frame provided in this embodiment includes: a first acquisition module 401, a calculation module 402, a construction module 403, a removal module 404, and an extraction module 405.

[0119] The first acquisition module 401 is used to acquire point cloud data and tunnel design data of the tunnel.

[0120] The calculation module 402 is used to calculate and generate tunnel point cloud information based on the tunnel point cloud data and tunnel design data.

[0121] Module 403 is used to construct a point cloud unfolding coordinate system based on tunnel point cloud information.

[0122] The elimination module 404 is used to eliminate tunnel contour points based on tunnel point cloud information and point cloud unfolded coordinate system using normal vectors, so as to determine the initial arch frame mileage data.

[0123] Extraction module 405 is used to extract point cloud data from the initial arch frame mileage data to obtain arch frame point cloud data.

[0124] In one embodiment of this application, the computing module 402 includes:

[0125] The comparison unit is used to compare the point cloud data of the tunnel with the tunnel data and calculate the point cloud mileage of each point cloud.

[0126] The first calculation unit is used to calculate the rotation matrix between the tunnel coordinate system and the working face coordinate system based on the point cloud mileage of each point cloud, and to transform each point cloud into the working face coordinate system.

[0127] The second calculation unit is used to calculate the over-excavation and under-excavation data and circumferential length data of each point cloud in the working face coordinate system, and record the point cloud mileage, over-excavation and under-excavation data and circumferential length data of each point cloud as point cloud information.

[0128] In one embodiment of this application, the rejection module 404 includes:

[0129] The third calculation unit is used to calculate the normal vector of the tunnel point cloud information in the point cloud unfolded coordinate system using a tree search algorithm.

[0130] The elimination unit is used to eliminate tunnel contour points based on the normal vector to obtain candidate points for the arch frame.

[0131] The analysis unit is used to analyze the over-excavation and under-excavation changes of candidate arch points and generate initial arch mileage data.

[0132] In one embodiment of this application, the parsing unit includes:

[0133] The slicing subunit is used to slice candidate points of the arch frame according to the preset arch frame thickness and slice thickness, so as to obtain multiple sets of point cloud slices.

[0134] The first calculation subunit is used to calculate the over-excavation and under-excavation rate of the tunnel point cloud along the vertical axis of the point cloud unfolding coordinate system based on multiple sets of point cloud slices, and obtain the change rate values ​​of multiple sets of point cloud slices.

[0135] The second calculation subunit is used to calculate the average value of the change rate values ​​of multiple sets of point cloud slices to obtain the initial arch mileage data.

[0136] In one embodiment of this application, the extraction module 405 includes:

[0137] The acquisition unit is used to acquire the over- and under-excavation speed variation range of the point cloud slice corresponding to the initial arch frame mileage data.

[0138] The first determining unit is used to determine the mileage interval of the initial arch frame mileage data based on the over- and under-excavation speed variation range of the point cloud slices corresponding to the arch frame installation spacing and the initial arch frame mileage data.

[0139] The fourth calculation unit is used to calculate the density mean based on the mileage interval of the initial arch frame mileage data.

[0140] The second determining unit is used to determine that the point cloud slice corresponding to the initial arch mileage data contains arch point cloud data if the density mean satisfies the preset conditions, and to extract the point cloud from the point cloud slice corresponding to the initial arch mileage data to obtain the arch point cloud data.

[0141] In one embodiment of this application, the point cloud data extraction device 40 for the arch frame further includes:

[0142] The recording module is used to record the location information of missing point cloud data if there are missing data in the point cloud data to be sampled.

[0143] The second acquisition module is used to acquire point cloud data of adjacent arches based on the location information of the missing point cloud data.

[0144] The data repair module is used to repair missing point cloud data based on the point cloud data of adjacent arches.

[0145] The point cloud data extraction device for the arch frame provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0146] Figure 5 is a schematic diagram of the structure of the electronic device provided in this application. As shown in Figure 5, the electronic device 50 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the electronic device 50 further includes a communication component 503. The processor 501, the memory 502, and the communication component 503 are connected via a bus.

[0147] In the specific implementation process, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to execute the above-described method for extracting point cloud data of the arch frame.

[0148] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0149] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0150] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0151] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0152] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for extracting point cloud data of an arch.

[0153] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method for extracting point cloud data of an arch.

[0154] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0155] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0156] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0157] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0158] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0159] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0160] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0161] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for extracting point cloud data from an arch frame, characterized in that, include: Acquire point cloud data and tunnel design data for the tunnel; Tunnel point cloud information is generated based on the tunnel point cloud data and the tunnel design data; Construct a point cloud unfolding coordinate system based on the tunnel point cloud information; The tunnel contour points are removed by using the normal vector based on the tunnel point cloud information and the coordinate system of the expanded point cloud to determine the initial arch frame mileage data; the initial arch frame mileage data is then used to extract the point cloud data to obtain the arch frame point cloud data.

2. The method according to claim 1, characterized in that, The step of generating tunnel point cloud information based on the tunnel point cloud data and the tunnel design data includes: comparing the tunnel point cloud data with the tunnel data, and calculating the point cloud mileage of each point cloud; calculating the rotation matrix between the tunnel coordinate system and the working face coordinate system based on the point cloud mileage of each point cloud, and transforming each point cloud into the working face coordinate system; calculating the over-excavation and under-excavation data and the circumferential length data of each point cloud in the working face coordinate system, and recording the point cloud mileage, the over-excavation and under-excavation data, and the circumferential length data of each point cloud as point cloud information.

3. The method according to claim 1, characterized in that, The step of eliminating tunnel contour points based on the tunnel point cloud information and the expanded coordinate system of the point cloud using normal vectors to determine the initial arch mileage data includes: calculating the normal vector of the tunnel point cloud information in the expanded coordinate system of the point cloud using a tree search algorithm; eliminating tunnel contour points based on the normal vectors to obtain candidate arch points; and performing over-excavation and under-excavation change analysis on the candidate arch points to generate the initial arch mileage data.

4. The method according to claim 3, characterized in that, The step of analyzing the over-excavation and under-excavation changes of the candidate arch points to generate initial arch mileage data includes: slicing the candidate arch points according to preset arch thickness and slice thickness to obtain multiple sets of point cloud slices; calculating the over-excavation and under-excavation change rate of the tunnel point cloud along the longitudinal direction of the point cloud unfolding coordinate system based on the multiple sets of point cloud slices to obtain the change rate value of the multiple sets of point cloud slices; and calculating the average value of the change rate values ​​of the multiple sets of point cloud slices to obtain the initial arch mileage data.

5. The method according to claim 1, characterized in that, The step of extracting point cloud data from the initial arch frame mileage data to obtain arch frame point cloud data includes: obtaining the over-excavation and under-excavation speed variation range of the point cloud slice corresponding to the initial arch frame mileage data; determining the mileage interval of the initial arch frame mileage data based on the arch frame installation spacing and the over-excavation and under-excavation speed variation range of the point cloud slice corresponding to the initial arch frame mileage data; calculating the density mean based on the mileage interval of the initial arch frame mileage data; if the density mean meets a preset condition, determining that the point cloud slice corresponding to the initial arch frame mileage data contains arch frame point cloud data, and extracting point cloud data from the point cloud slice corresponding to the initial arch frame mileage data to obtain arch frame point cloud data.

6. The method according to claim 1, characterized in that, After extracting point cloud data from the arch frame mileage data, the method further includes: if there are missing data in the point cloud data to be sampled, recording the location information of the missing point cloud data; obtaining the point cloud data of adjacent arch frames based on the location information of the missing point cloud data; and repairing the missing point cloud data based on the point cloud data of the adjacent arch frames.

7. A point cloud data extraction device for an arch frame, characterized in that, include: The first acquisition module is used to acquire point cloud data and tunnel design data of the tunnel; The calculation module is used to calculate and generate tunnel point cloud information based on the tunnel point cloud data and the tunnel design data; The construction module is used to construct a point cloud unfolding coordinate system based on the tunnel point cloud information; The elimination module is used to eliminate tunnel contour points based on the tunnel point cloud information and the expanded coordinate system of the point cloud using normal vectors, so as to determine the initial arch mileage data; The extraction module is used to extract point cloud data from the initial arch mileage data to obtain arch point cloud data.

8. An electronic device, characterized in that, include: Memory, processor; The memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory, causing the processor to perform the point cloud data extraction method for the arch as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the point cloud data extraction method for the arch frame as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the point cloud data extraction method for the arch as described in any one of claims 1 to 6.

Images