A tunnel cross-section point cloud filtering method and an electronic device based on the minimum circle algorithm

Abstract

The present invention relates to the field of surveying and mapping, in particular to a tunnel cross-section point cloud filtering method and an electronic device based on the minimum circle algorithm. The present invention establishes a plane coordinate system in the target tunnel cross-section, constructs a minimum circle in this coordinate system, and then filters the point cloud through the filtering algorithm of the present invention. Since when there is no under-excavation phenomenon in the tunnel cross-section, the distance from the point cloud to the center of the minimum circle shows a regular increase from top to bottom, the filtering algorithm of the present invention can effectively detect the noise in the tunnel cross-section point cloud. Therefore, the filtering algorithm of the present invention can be applied to tunnel cross-section types such as circular, semi-circular, semi-elliptical, and arched. The algorithm has strong compatibility, does not require a large amount of manual participation, greatly reduces the difficulty of tunnel processing and analysis, further improves the filtering efficiency, and thus speeds up the tunnel analysis process.

Description

Technical Field

[0001] The present invention relates to the field of surveying and mapping, and particularly to a tunnel cross-section point cloud filtering method and an electronic device based on the minimum circle algorithm. Background Art

[0002] Since the 21st century, with the rapid development of China's transportation industry, the construction scale of tunnels has been increasing day by day, and the demand for tunnel scanning and detection has also been increasing. At present, three-dimensional laser scanning technology is commonly used for tunnel scanning, and dense laser point cloud is the main form of data obtained by three-dimensional laser scanning technology. However, due to the influence of scanning environmental factors, the tunnel laser point cloud obtained by using three-dimensional laser scanning technology at present contains not only tunnel wall point cloud, but also non-tunnel wall noise point clouds such as pedestrians and equipment, and cannot be directly used for tunnel over-excavation analysis, clearance analysis or cross-section acquisition in engineering measurement. For the non-tunnel wall noise points in the tunnel point cloud, point cloud filtering or manual removal is still required.

[0003] At present, most of the tunnel cross-section point cloud filtering methods are based on the overall or segmented fitting of circles or ellipses for point cloud data, calculate the geometric parameters of the tunnel center line and tunnel shape, calculate the distance from the scanning point to the tunnel center line in turn, and calculate the distance difference with the fitting radius as the subtractor, set the distance difference threshold, and regard the laser point cloud data within the threshold as noise points to be removed. Since there are various types of tunnel cross-section designs, for different types of tunnel design cross-sections, the corresponding cross-section fitting formula needs to be selected and a reasonable threshold needs to be set to achieve a better filtering effect. Such a filtering algorithm still has many manual analysis and processing steps, and it is necessary to ensure the accurate calculation of the shape parameters of the tunnel cross-section and the rationality of the threshold setting, otherwise it is easy to cause over-deletion of tunnel wall point cloud or under-deletion of noise points.

[0004] Therefore, there is a need for a point cloud filtering algorithm that can be compatible with various types of tunnel cross-sections at present to reduce the difficulty of tunnel processing and analysis and improve the efficiency of filtering analysis. Summary of the Invention

[0005] The purpose of the present invention is to overcome the problems of poor compatibility of the filtering algorithm in the prior art, the need for a large amount of manual participation and low efficiency, and to provide a tunnel cross-section point cloud filtering method and an electronic device based on the minimum circle algorithm.

[0006] In order to achieve the above-mentioned invention purpose, the present invention provides the following technical solutions:

[0007] A tunnel cross-section point cloud filtering method based on the minimum circle algorithm, comprising the following steps:

[0008] S1: Obtain the tunnel cross-section point cloud of the target mileage and construct an X-Z axis plane coordinate system; wherein, the X-axis represents the transverse coordinate of the tunnel cross-section at the target mileage, and the Z-axis represents the elevation coordinate of the tunnel cross-section at the target mileage;

[0009] S2: Divide the tunnel cross-section point cloud into left and right parts, and respectively sort the point cloud data of the left and right parts from large to small according to the Z-axis value to generate a left part point cloud list and a right part point cloud list;

[0010] S3: Determine the center coordinates and radius of the minimum circle of the tunnel, and construct the minimum circle of the tunnel;

[0011] S4: Filter the point cloud data of the left part point cloud list and the right part point cloud list respectively, and merge the filtered left part point cloud list and the right part point cloud list, and output it as the tunnel cross-section at the target mileage;

[0012] Among them, the filtering includes the following steps: taking the center coordinates as the base point, respectively calculate the distance between the point cloud data of the left part point cloud list and the right part point cloud list and the base point in sequence, and calculate the difference between adjacent point cloud data in sequence; when the difference between adjacent point cloud data does not meet the preset distance threshold interval, remove the latter point cloud data as a noise point.

[0013] As a preferred solution of the present invention, the acquisition of the tunnel cross-section point cloud of the target mileage in S1 includes the following steps:

[0014] S11: Obtain the tunnel point cloud model data, the tunnel center line vector data, and the mileage data of the target cross-section;

[0015] S12: Cut the tunnel point cloud model data at the target mileage with a preset thickness, and project the cut point cloud data on the tunnel cross-section at the target mileage to obtain the tunnel cross-section point cloud of the target mileage.

[0016] As a preferred solution of the present invention, S2 includes the following steps:

[0017] S21: Obtain the point cloud data with the largest Z-axis coordinate within the preset X-axis coordinate range, and use it as the topmost point of the tunnel;

[0018] S22: Taking the topmost point of the tunnel as the discrimination basis, divide the tunnel cross-section point cloud into left and right parts, and respectively sort the point cloud data of the left and right parts from large to small according to the Z-axis value to generate a left part point cloud list and a right part point cloud list.

[0019] As a preferred embodiment of the present invention, the X-axis coordinate of the center of the smallest circle of the tunnel in S3 is the X-axis coordinate of the topmost point of the tunnel, and the Z-axis coordinate is the projected coordinate of the tunnel centerline on the Z-axis plus a preset elevation threshold; the radius of the smallest circle of the tunnel is the distance from the center coordinate to the topmost point of the tunnel.

[0020] As a preferred embodiment of the present invention, the elevation threshold in S3 satisfies the interval [0, &), where & is the difference between the Z-axis coordinate of the topmost point of the tunnel and the projected coordinate of the tunnel centerline on the Z-axis.

[0021] As a preferred embodiment of the present invention, the filtering process of each point cloud list in S4 includes the following steps:

[0022] S41: Calculate the distance Di from the i-th point in the point cloud list to the base point; where the initial value of i is 1;

[0023] S42: Calculate the distance Di+1 from the (i + 1)-th point in the point cloud list to the base point;

[0024] S43: Calculate the distance difference Sub between the (i + 1)-th point and the i-th point in the point cloud list, Sub = Di+1 - Di;

[0025] S44: Judge the size relationship between Sub and the preset distance threshold interval ;

[0026] When Sub satisfies the distance threshold interval , regard this point as a real tunnel wall point, execute i = i + 1, and jump to S41 to continue the loop judgment,

[0027] When Sub does not satisfy the distance threshold interval , regard the (i + 1)-th point as a noise point, remove this point from the list, and jump to S42 to calculate the distance from the next point to the base point as Di+1, and loop to judge whether the (i + 1)-th point is a noise point;

[0028] Among them, when i + 1 is greater than the length of the point cloud list, the filtering process is completed and the loop stops.

[0029] As a preferred embodiment of the present invention, the distance threshold interval

[0030] An electronic device includes at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in any one of the above.

[0031] Compared with the prior art, the beneficial effects of the present invention:

[0032] 1. The present invention establishes a plane coordinate system in the target tunnel section, constructs the smallest circle in this coordinate system, and then filters the point cloud through the filtering algorithm of the present invention. Since when there is no under-excavation phenomenon in the tunnel section, the distance from the point cloud to the center of the smallest circle increases regularly from top to bottom, the filtering algorithm of the present invention can effectively detect the noise in the tunnel section point cloud. Therefore, the filtering algorithm of the present invention can be applied to tunnel section types such as semi-circular, semi-elliptical, and arched. The algorithm has strong compatibility, does not require a large amount of manual participation, greatly reduces the difficulty of tunnel processing and analysis, and further improves the filtering efficiency, thus accelerating the tunnel analysis process.

[0033] 2. The filtering algorithm of the present invention directly uses the original point cloud data, directly extracts the point cloud of the target tunnel section from it, does not require fitting the point cloud data, and then filters through the smallest circle algorithm, effectively improving the filtering efficiency.

[0034] 3. The filtering algorithm of the present invention determines whether the point cloud is a noise point by setting a certain distance threshold interval based on the difference in the distances from the sorted front and rear point clouds to the center of the smallest circle. And the distance threshold interval can be dynamically adjusted according to the tunnel situation, which can effectively improve the judgment accuracy of noise points. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic flow chart of a method for filtering tunnel section point cloud based on the smallest circle algorithm described in Embodiment 1 of the present invention;

[0036] Figure 2 It is a schematic diagram of constructing the smallest circle of the tunnel section in a method for filtering tunnel section point cloud based on the smallest circle algorithm described in Embodiment 3 of the present invention;

[0037] Figure 3 It is a schematic diagram A of the processing effect of the tunnel section in a method for filtering tunnel section point cloud based on the smallest circle algorithm described in Embodiment 3 of the present invention;

[0038] Figure 4 It is a schematic diagram B of the processing effect of the tunnel section in a method for filtering tunnel section point cloud based on the smallest circle algorithm described in Embodiment 3 of the present invention;

[0039] Figure 5 It is a schematic structural diagram of an electronic device using the method for filtering tunnel section point cloud based on the smallest circle algorithm described in Embodiment 1 of the present invention in Embodiment 4 of the present invention.

[0040] Markings in the figure: 1 - elliptical cylinder tunnel, 2 - tunnel center line, 3 - tunnel cross-section, 4 - X-axis, 5 - Z-axis, 6 - cross-section point cloud, 7 - projected coordinates of the tunnel center line, 8 - modified projected coordinates of the tunnel center line, 9 - minimum circle radius, 10 - minimum circle, 11 - noise point cloud. Detailed implementation mode

[0041] The present invention will be further described in detail below in conjunction with test examples and specific implementation modes. However, it should not be understood that the scope of the above-mentioned subject matter of the present invention is limited to the following embodiments. All technologies implemented based on the content of the present invention belong to the scope of the present invention.

[0042] Embodiment 1

[0043] As Figure 1 shown, a tunnel cross-section point cloud filtering method based on the minimum circle algorithm includes the following steps:

[0044] S1: Obtain the tunnel cross-section point cloud of the target mileage and construct an X-Z axis plane coordinate system; wherein, the X-axis represents the transverse coordinate of the tunnel cross-section of the target mileage, and the Z-axis represents the elevation coordinate of the tunnel cross-section of the target mileage.

[0045] S2: Divide the tunnel cross-section point cloud into left and right parts, and respectively sort the point cloud data of the left and right parts from large to small according to the Z-axis value to generate a left part point cloud list and a right part point cloud list.

[0046] S3: Determine the center coordinates and radius of the minimum circle of the tunnel and construct the minimum circle of the tunnel.

[0047] S4: Filter the point cloud data of the left part point cloud list and the right part point cloud list respectively, and merge the filtered left part point cloud list and right part point cloud list, and output it as the tunnel cross-section of the target mileage.

[0048] Among them, the filtering includes the following steps: taking the center coordinates as the base point, calculating the distances between the point cloud data of the left part point cloud list and the right part point cloud list and the base point respectively in sequence, and calculating the differences between adjacent point cloud data in sequence; when the differences between adjacent point cloud data do not meet the preset distance threshold interval, remove the latter point cloud data as noise points.

[0049] Embodiment 2

[0050] The difference between this embodiment and Embodiment 1 is that the acquisition of the tunnel cross-section point cloud of the target mileage in S1 includes the following steps:

[0051] S11: Obtain the tunnel point cloud model data, the tunnel center line vector data, and the mileage data of the target cross-section. The tunnel point cloud model data can be in the las format.

[0052] S12: Cut the tunnel point cloud model data at the target mileage with a preset thickness, project the cut point cloud data onto the cross-section of the tunnel at the target mileage, and obtain the tunnel cross-section point cloud at the target mileage.

[0053] Embodiment 3

[0054] This embodiment is a specific implementation manner of the tunnel cross-section point cloud filtering method based on the minimum circle algorithm described in Embodiment 1 or Embodiment 2, and includes the following steps:

[0055] S1: Obtain the tunnel cross-section point cloud at the target mileage and construct an X-Z axis plane coordinate system; wherein, the X-axis represents the transverse coordinate of the tunnel cross-section at the target mileage, and the Z-axis represents the elevation coordinate of the tunnel cross-section at the target mileage.

[0056] S2: Divide the tunnel cross-section point cloud into left and right parts, and respectively sort the point cloud data of the left and right parts from large to small according to the Z-axis value to generate a left part point cloud list and a right part point cloud list.

[0057] S21: Obtain the point cloud data with the largest Z-axis coordinate within the preset X-axis coordinate range, and use it as the top vertex of the tunnel.

[0058] S22: Take the top vertex of the tunnel, that is, the X-axis coordinate value of the point with the largest Z-axis coordinate as the discrimination basis, divide the tunnel cross-section point cloud into left and right parts, and respectively sort the point cloud data of the left and right parts from large to small according to the Z-axis value to generate a left part point cloud list and a right part point cloud list.

[0059] S3: As Figure 2 shown, determine the center coordinates and radius of the minimum circle of the tunnel, and construct the minimum circle of the tunnel.

[0060] The X-axis coordinate of the center coordinates of the minimum circle of the tunnel is the X-axis coordinate of the top vertex of the tunnel, and the Z-axis coordinate is the projected coordinate of the tunnel center line on the Z-axis plus a preset lift threshold; the radius of the minimum circle of the tunnel is the distance from the center coordinates to the top vertex of the tunnel. The lift threshold satisfies the interval [0, &), and & is the difference between the Z-axis coordinate of the top vertex of the tunnel and the projected coordinate of the tunnel center line on the Z-axis.

[0061] S4: Respectively filter the point cloud data in the left part point cloud list and the right part point cloud list, and merge the filtered left part point cloud list and the right part point cloud list, and output it as the tunnel cross-section at the target mileage.

[0062] Among them, the filtering process of each point cloud list includes the following steps:

[0063] S41: Calculate the distance D from the i-th point in the point cloud list to the base point i ; wherein, the initial value of i is 1;

[0064] S42: Calculate the distance D from the (i + 1)-th point in the point cloud list to the base point i+1 ;

[0065] S43: Calculate the distance difference Sub between the (i + 1)-th point and the i-th point in the point cloud list, Sub = D i+1 - D i ; S44: Judge the size relationship between Sub and the preset distance threshold interval ;

[0066] When Sub satisfies the distance threshold interval , regard this point as a real tunnel wall point, execute i = i + 1, and jump to S41 to continue the loop judgment. Preferably,

[0067] When Sub does not satisfy the distance threshold interval , regard the (i + 1)-th point as a noise point, remove this point from the list, and jump to S42 to calculate the distance D from the next point to the base point i+1 , and loop to judge whether the (i + 1)-th point is a noise point;

[0068] Among them, when (i + 1) is greater than the length of the point cloud list, the filtering process is completed and the loop stops.

[0069] As Figure 3 and Figure 4 shown, input the point clouds of two tunnel cross-sections, and according to the above steps, output the point clouds of the tunnel cross-sections corresponding to the target mileage.

[0070] Embodiment 4

[0071] As Figure 5 shown, an electronic device includes at least one processor, a memory communicatively connected to the at least one processor, and at least one input / output interface communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute a method for filtering tunnel cross-section point clouds based on the minimum circle algorithm described in the foregoing embodiments. The input / output interface may include a display, a keyboard, a mouse, and a USB interface for inputting and outputting data.

[0072] Those skilled in the art can understand that all or part of the steps for implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments; and the foregoing storage medium includes: various media such as removable storage devices, read-only memory (ROM), magnetic disks, or optical discs that can store program codes.

[0073] When the above-mentioned integrated unit of the present invention is implemented in the form of a software functional unit and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention essentially or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes such as removable storage devices, ROMs, magnetic disks, or optical discs.

[0074] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A tunnel cross-section point cloud filtering method based on the minimum circle algorithm, characterized in that, it includes the following steps: S1: Obtain the tunnel cross-section point cloud of the target mileage and construct an X-Z axis plane coordinate system; wherein, the X axis represents the transverse coordinate of the tunnel cross-section of the target mileage, and the Z axis represents the elevation coordinate of the tunnel cross-section of the target mileage; S2: Divide the tunnel cross-section point cloud into left and right parts, and respectively sort the point cloud data of the left and right parts from large to small according to the Z-axis value to generate a left part point cloud list and a right part point cloud list; S3: Determine the center coordinates and radius of the minimum circle of the tunnel, and construct the minimum circle of the tunnel; S4: Filter the point cloud data of the left part point cloud list and the right part point cloud list respectively, and merge the filtered left part point cloud list and the right part point cloud list, and output it as the tunnel cross-section of the target mileage; wherein, the filtering includes the following steps: taking the center coordinates as the base point, calculate the distance between the point cloud data of the left part point cloud list and the right part point cloud list and the base point in sequence, and calculate the difference between adjacent point cloud data in sequence; when the difference between adjacent point cloud data does not satisfy the preset distance threshold interval, remove the latter point cloud data as a noise point; The S2 includes the following steps: S21: Obtain the point cloud data with the largest Z-axis coordinate within the preset X-axis coordinate range, and use it as the top vertex of the tunnel; S22: Taking the top vertex of the tunnel as the discrimination basis, divide the tunnel cross-section point cloud into left and right parts, and respectively sort the point cloud data of the left and right parts from large to small according to the Z-axis value to generate a left part point cloud list and a right part point cloud list; In S3, the X-axis coordinate of the center coordinates of the minimum circle of the tunnel is the X-axis coordinate of the top vertex of the tunnel, and the Z-axis coordinate is the projection coordinate of the tunnel center line on the Z axis plus a preset lifting threshold; the radius of the minimum circle of the tunnel is the distance from the center coordinates to the top vertex of the tunnel; The filtering process of each point cloud list in S4 includes the following steps: S41: Calculate the distance D from the i-th point in the point cloud list to the base point i ; where the initial value of i is 1; S42: Calculate the distance D from the (i + 1)-th point in the point cloud list to the base point i+1 ; S43: Calculate the distance difference Sub between the (i + 1)-th point and the i-th point in the point cloud list, Sub = D i+1 -D i ;; S44: Judge the size relationship between Sub and the preset distance threshold interval ∂; When Sub satisfies the distance threshold interval ∂, regard this point as a real tunnel wall point, execute i = i + 1, and jump to S41 to continue the loop judgment, ∂ <= 0; When Sub does not satisfy the distance threshold interval ∂, the (i + 1)-th point is regarded as a noise point, and this point is removed from the list, then jump to S42 to calculate the distance D from the next point to the base point i+1 , and loop to judge whether the (i + 1)-th point is a noise point; wherein, when i + 1 is greater than the length of the point cloud list, the filtering process is completed and the loop stops.

2. A tunnel cross-section point cloud filtering method based on the minimum circle algorithm according to claim 1, characterized in that, the acquisition of the tunnel cross-section point cloud of the target mileage in S1 includes the following steps: S11: Obtain the tunnel point cloud model data, the tunnel center line vector data, and the mileage data of the target cross-section; S12: Cut the tunnel point cloud model data at the target mileage with a preset thickness, and project the cut point cloud data on the tunnel cross-section of the target mileage to obtain the tunnel cross-section point cloud of the target mileage.

3. A tunnel cross-section point cloud filtering method based on the minimum circle algorithm according to claim 1, characterized in that, the lifting threshold in S3 satisfies the interval [0, &), and & is the difference between the Z-axis coordinate of the top vertex of the tunnel and the projection coordinate of the tunnel center line on the Z axis.

4. A tunnel cross-section point cloud filtering method based on the minimum circle algorithm according to claim 1, characterized in that, the distance threshold interval ∂ <= 0.

5. An electronic device, characterized in that, comprising at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to any one of claims 1 to 4.

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