Error Self-Correction Control Method for 3D LiDAR Equipment for Transmission Line Foundation Pit Marking

Abstract

This invention discloses a self-correction control method for errors in a three-dimensional lidar device for foundation pit marking in power transmission lines, relating to the field of power transmission line construction technology. The invention is implemented through the following steps: First, based on a Cartesian coordinate system, the distance, horizontal angle, and vertical angle from the three-dimensional lidar device to the target point are defined to determine the initial coordinates of the target point. Second, reference control points are selected, noise and gross errors are removed, vertical offsets are corrected, and point cloud data is refined. Next, errors in the X, Y, and Z axes are defined, and an error correction model is established. Finally, self-correction of the target coordinates is performed based on the observed values ​​and the error correction model, and correction is achieved through an environmental feature-adaptive error weight allocation mechanism. This invention uses a multi-level data verification mechanism for closed-loop iterative optimization to ensure measurement accuracy, improve operational efficiency, enhance environmental adaptability, and is suitable for foundation pit marking operations in complex terrain.

Description

Technical Field

[0001] This invention relates to the field of power transmission line construction technology, specifically to a method for error self-correction control of three-dimensional lidar equipment for foundation pit marking in power transmission lines. Background Technology

[0002] In the field of modern power system construction, the accuracy and efficiency of foundation pit positioning for transmission lines are key factors in ensuring the smooth progress of the entire project. Traditional foundation pit positioning for transmission lines mainly relies on conventional equipment such as theodolites and total stations, which is inefficient, requires highly skilled surveyors, and is susceptible to human error, leading to deviations in control stakes, direction stakes, elevation / lowering leg positioning, and anchor bolt positioning. With the rapid development of large-scale new energy base projects and the construction of ultra-high-voltage transmission lines in my country, the areas where transmission lines are erected are gradually extending to mountainous and plateau regions with complex terrain, making the drawbacks of traditional pit positioning methods increasingly apparent.

[0003] 3D laser mapping radar based on Simultaneous Localization and Mapping (SLAM) technology can clearly display the 3D terrain information of power transmission line operation scenarios. Utilizing such equipment helps improve the automation level of power transmission line foundation excavation work and reduce the impact of human operation. However, in complex terrains with high vegetation coverage and fragmented topography, and in scenarios with low target reflectivity, measurement errors are caused by the performance limitations of the laser radar equipment itself. Appropriate measures must be taken to control these errors and ensure the accuracy of power transmission line foundation excavation.

[0004] In view of this, the present invention proposes an error self-correction control method for three-dimensional lidar equipment for power transmission line foundation pit division. This method changes the traditional method of using theodolites for pit division of power transmission line foundations. By utilizing three-dimensional lidar equipment and controlling measurement errors, the on-site operation of pit division for power transmission line tower foundations can be carried out efficiently and accurately. Summary of the Invention

[0005] The purpose of this invention is to solve the problems in the background art and to propose an error self-correction control method for three-dimensional lidar equipment for foundation pitting of transmission lines.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A method for error self-correction control of three-dimensional lidar equipment for foundation pit marking in transmission lines includes the following steps:

[0008] S1. Based on the Cartesian coordinate system, define the distance L, horizontal angle θ, and vertical angle ψ from the 3D lidar device to the target point A, identify the relative positional relationship between the 3D lidar device and the target point, determine the coordinates of the target point in the Cartesian coordinate system, and form the initial observation results;

[0009] S2. Select reference control point B, remove noise and coarse errors, correct vertical offset, and refine the 3D laser point cloud data;

[0010] S3. Define the errors in the X, Y, and Z axes of the Cartesian coordinate system as Δε. x-A , Δε y-A , Δε z-A Based on the equipment position information calibrated by the multi-mode positioning device bound to the 3D lidar, an error correction model for the 3D lidar point cloud basic pit-dividing equipment is established.

[0011] S4. Self-calibrate the target coordinates based on the observation values ​​and error correction model, and correct the error through an adaptive error weight allocation mechanism based on environmental characteristics.

[0012] Furthermore, the specific process of S1 is as follows:

[0013] S1-101: Define the measurement parameters for the 3D LiDAR base pit-digging equipment:

[0014] Taking the location of the 3D LiDAR base excavation equipment as the origin O(0,0,0), define the distance from the 3D LiDAR base excavation equipment to any target point A(X) within the excavation work surface. A Y A Z A The straight-line distance is L. A ;

[0015] Define the horizontal angle θ as the distance from the true north of the 3D lidar base to the measurement target point A(X) on the horizontal plane. A Y A Z A The angle between directions;

[0016] Define the vertical angle ψ as the angle between the three-dimensional lidar base and the target point A(X) as the distance between the base and the target point A(X) as the base rotates horizontally upwards or downwards. A Y A Z A The angle between directions;

[0017] S1-102: Establishing the 3D LiDAR foundation pitting equipment and measurement target point A(X) A Y A Z A The relative positional relationship of )

[0018] Based on the parameters defined in step S1-101, establish the relative positional relationship between the 3D LiDAR base pit-dividing equipment and the measurement target point A, and convert it into coordinate information in the Cartesian coordinate system using the following formula:

[0019] X A =L A ×cos(ψ)×cos(θ), Y A =L A ×cos(ψ)×sin(θ), Z A =L A ×sin(ψ)

[0020] After the conversion is completed, the resulting coordinate information is the initial observation result formed based on the measurement data of the current 3D lidar foundation pit-digging equipment.

[0021] Furthermore, the specific operation steps of S2 are as follows:

[0022] S2-101: Based on the provided pit-digging operation design documents, the location of the center stake at the work site is determined using real-time dynamic positioning equipment as the reference control point B(X). B Y B Z B This provides a benchmark for subsequent data correction;

[0023] S2-102: Determine any target point A(X) within the excavation work surface in the Cartesian coordinate system. A Y A Z A ) and reference control point B(X) B Y B Z B Differences between values:

[0024] Δx A =|X A -X B |、Δy A =|Y A -Y B |、Δz A =|Z A -Z B |;

[0025] S2-103: When Δx A >2σ dyn or Δy A >2σ dyn or Δz A >2σ dyn Then it is assumed that the corresponding target point A(X) within the pit-digging operation surface is... A Y A Z A) represents coarse-grained points, which are then removed to refine the point cloud data acquired by the 3D LiDAR equipment; where σ dyn For dynamic thresholds;

[0026] S2-104: Relative position estimation is performed using the filtered 3D laser point cloud data. The following formula is used to determine any measurement target point A(X) within the pit-digging operation surface. A Y A Z A ) and reference control point B(X) B Y B Z B The relative position of X: rel-A =X A -X B Y rel-A =Y A -Y B Z rel-A =Z A -Z B .

[0027] Furthermore, the dynamic threshold σ dyn The determination process is as follows:

[0028] Integrate temperature and humidity sensors into the 3D LiDAR equipment to collect real-time temperature (T) and humidity (H) data of the working environment.

[0029] The influence of temperature and humidity on laser ranging error was experimentally calibrated, and a linear relationship model was established: ΔL=k T ×ΔT+k H ×ΔH, where ΔT and ΔH are the deviations of the current temperature and humidity from the calibration reference values, and k T k H This is a correction factor;

[0030] Dynamic threshold calculation Where L 标 For calibration distance, σ r It is a fixed threshold.

[0031] Furthermore, the specific operation steps of S3 are as follows:

[0032] S3-101: Define any target measurement point A(X) within the excavation operation. A Y A Z A The errors in the X, Y, and Z axes in the Cartesian coordinate system are Δε, ... x-A , Δε y-A , Δε z-A :

[0033] Where R is the reference control point B(X)B Y B Z B The Euler angle rotation matrix from the origin O(0, 0, 0) of the 3D LiDAR foundation pitting equipment location:

[0034]

[0035] The three angles, pitch angle θ, yaw angle ψ, and roll angle φ, are respectively the reference control point B(X). B Y B Z B For clockwise rotation of the X, Y, and Z axes of a Cartesian coordinate system, an additional roll angle φ is usually not required, while the pitch angle θ and yaw angle ψ can be calculated using the following formula:

[0036]

[0037] S3-102: Based on the steps in S3-101, measure any target point A(X) within the pit-dividing operation. A Y A Z A Perform coordinate correction to obtain the corrected coordinate information:

[0038]

[0039] Furthermore, the specific operation steps of S4 are as follows:

[0040] In the initial observation of step S1, the terrain slope of target point A is recorded simultaneously through the lidar point cloud computing and the reflectivity value is analyzed through the laser echo intensity.

[0041] Error correction weights are defined based on slope α and reflectivity β:

[0042] W x =k1×e -α +k1×β,W y W z Similarly, corrections are made, where k1 and k2 are experimental calibration coefficients, and the obtained W... x W y W z During the coordinate correction process, the corresponding error Δε is respectively... x-A , Δε y-A , Δε z-A Multiplication enables dynamic error compensation.

[0043] Furthermore, S4 also includes

[0044] The closed-loop iterative optimization process utilizes a multi-level data verification mechanism, as follows:

[0045] After completing the gross error removal in S2, the spatial consistency of the point cloud data is verified by using the relative position of the reference control point B and the target point A; if the point cloud density in a certain area is lower than the threshold, a local rescan is triggered.

[0046] After S4 self-calibration, the corrected coordinates are back-calculated to the original measurement parameters (L, θ, ψ). The theoretical coordinates are regenerated through steps S1-102 and compared with the actual corrected coordinates. If the deviation exceeds the threshold, the S2-S4 process is automatically iterated until convergence.

[0047] The number of iterations and the final deviation value are recorded as the pit quality assessment indicators for post-construction acceptance.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] (1) This invention significantly improves the accuracy and reliability of foundation pit measurement for transmission lines by introducing a three-dimensional lidar equipment error self-correction control method. Traditional methods are easily affected by factors such as human operation and equipment performance limitations under complex terrain and environmental conditions, resulting in large measurement errors. This invention can effectively reduce measurement errors by defining measurement parameters, establishing an error correction model and an adaptive error weight allocation mechanism, ensuring measurement accuracy in complex environments, greatly improving the accuracy and reliability of construction, and providing a solid technical guarantee for the construction of transmission lines.

[0050] (2) This invention changes the traditional mode of using theodolites and other equipment for foundation pit division of transmission lines. By utilizing three-dimensional lidar equipment and using an error self-correction control method, it realizes the automated operation of foundation pit division of transmission line towers. This method can quickly process three-dimensional lidar point cloud data, remove noise and coarse errors, and perform closed-loop iterative optimization through a multi-level data verification mechanism, reducing manual intervention and improving operation efficiency. At the same time, by using real-time dynamic positioning equipment to determine the position of the center pile at the work site as a reference control point, it provides a benchmark for subsequent data correction, further improving the automation level of the operation, shortening the construction cycle, reducing labor intensity, and improving construction efficiency.

[0051] (3) In this invention, environmental feature analysis is introduced during the measurement process. By collecting temperature and humidity data of the working environment in real time and combining the linear relationship model calibrated by the experiment, the error threshold is dynamically adjusted, thereby enhancing the adaptability of the measurement system to complex environments. In addition, by refining the three-dimensional laser point cloud data, gross errors are eliminated and vertical offsets are corrected, further improving the data quality. By using a multi-level data verification mechanism for closed-loop iterative optimization, abnormal points in the data can be automatically detected and corrected, ensuring the consistency and accuracy of the data. This not only improves the reliability of the measurement results, but also provides high-quality data support for subsequent construction and acceptance, which helps to improve the quality and safety of the entire power transmission line construction. Attached Figure Description

[0052] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0053] Figure 1 This is a flowchart of the method of the present invention;

[0054] Figure 2 This is a diagram showing the relative positions in the Cartesian coordinate system used in this invention. Detailed Implementation

[0055] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] It should be understood that the terms “comprising” and “including” used in this disclosure and claims indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0057] It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure. As used in this disclosure and claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this disclosure and claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations.

[0058] like Figures 1-2 As shown, the error self-correction control method for three-dimensional lidar equipment for foundation pit marking of transmission lines includes the following steps:

[0059] S1. Based on the Cartesian coordinate system, define the distance L, horizontal angle θ, and vertical angle ψ from the 3D lidar device to the target point A, identify the relative positional relationship between the 3D lidar device and the target point, determine the coordinates of the target point in the Cartesian coordinate system, and form the initial observation results;

[0060] S1-101: Define the measurement parameters for the 3D LiDAR base pit-digging equipment:

[0061] Taking the location of the 3D LiDAR base excavation equipment as the origin O(0,0,0), define the distance from the 3D LiDAR base excavation equipment to any target point A(X) within the excavation work surface. A Y A Z A The straight-line distance is L. A ;

[0062] Define the horizontal angle θ as the distance from the true north (or other reference direction) of the three-dimensional lidar base pit-dividing equipment on the horizontal plane to the measurement target point A(X). A Y A Z A The angle between directions;

[0063] Define the vertical angle ψ as the angle between the base of the 3D lidar and the target point A(X) as the base rotates upwards (or downwards) along the horizontal plane. A Y A Z A The angle between directions.

[0064] S1-102: Establishing the 3D LiDAR foundation pitting equipment and measurement target point A(X) A Y A Z A The relative positional relationship of )

[0065] Based on the parameters defined in step S1-101, establish the relative positional relationship between the 3D LiDAR base pit-dividing equipment and the measurement target point A, and convert it into coordinate information in the Cartesian coordinate system using the following formula:

[0066] X A =L A ×cos(ψ)×cos(θ), Y A =L A ×cos(ψ)×sin(θ), Z A =L A ×sin(ψ)

[0067] After the above transformation is completed, the resulting coordinate information is the initial observation result formed based on the measurement data of the current three-dimensional lidar foundation pit-digging equipment.

[0068] S2. Select reference control point B, remove noise and coarse errors, correct vertical offset, and refine the 3D laser point cloud data;

[0069] S2-101: Based on the provided pit-digging operation design documents, use real-time kinematic (RTK) equipment to determine the position of the center stake at the work site as the reference control point B(X). B Y B Z B This provides a benchmark for subsequent data correction;

[0070] S2-102: Determine any target point A(X) within the excavation work surface in the Cartesian coordinate system. A Y A Z A ) and reference control point B(X) B Y B Z B Differences between values:

[0071] Δx A =|X A -X B |、Δy A =|Y A -Y B |、Δz A =|Z A -Z B |;

[0072] S2-103: When Δx A >2σ dyn or Δy A >2σ dyn or Δz A >2σ dyn Then it is assumed that the corresponding target point A(X) within the pit-digging operation surface is... A Y A Z A ) represents coarse-grained points, which are then removed to refine the point cloud data acquired by the 3D LiDAR equipment; where σ dyn The determination process is as follows:

[0073] Temperature and humidity sensors are integrated into a 3D lidar device to collect real-time temperature (T) and humidity (H) data of the operating environment. The influence of temperature and humidity on laser ranging error is experimentally calibrated, and a linear relationship model is established: ΔL = k T ×ΔT+k H ×ΔH, where ΔT and ΔH are the deviations of the current temperature and humidity from the calibration reference values, and k T k H This is a correction factor;

[0074] Dynamic threshold calculation Where L 标 For the calibration distance (e.g., 10m), σ r The fixed threshold is set to 0.02m.

[0075] S2-104: Relative position estimation is performed using the filtered 3D laser point cloud data. The following formula is used to determine any measurement target point A(X) within the pit-digging operation surface. A Y A Z A ) and reference control point B(X) B Y B Z B The relative position of )

[0076] X rel-A =X A -X B Y rel-A =Y A -Y B Z rel-A =Z A -Z B ;

[0077] S3. Define the errors in the X, Y, and Z axes of the Cartesian coordinate system as Δε. x-A , Δε y-A , Δε z-A Based on the equipment position information calibrated by the multi-mode positioning device bound to the 3D LiDAR, an error correction model for the 3D LiDAR point cloud-based pit-division equipment is established; the process is as follows:

[0078] S3-101: Define any target measurement point A(X) within the excavation operation. A Y A Z A The errors in the X, Y, and Z axes in the Cartesian coordinate system are Δε, ... x-A , Δε y-A , Δε z-A ,

[0079] Where R is the reference control point B(X) B Y B Z B The Euler angle rotation matrix from the origin O(0, 0, 0) of the 3D LiDAR foundation pitting equipment location:

[0080]

[0081] The three angles, pitch angle θ, yaw angle ψ, and roll angle φ, are respectively the reference control point B(X). BY B Z B For clockwise rotation of the X, Y, and Z axes of a Cartesian coordinate system, an additional roll angle φ is usually not required, while the pitch angle θ and yaw angle ψ can be calculated using the following formula:

[0082]

[0083] S3-102: Based on the steps in S3-101, measure any target point A(X) within the pit-dividing operation. A Y A Z A Perform coordinate correction to obtain the corrected coordinate information:

[0084]

[0085] After the above correction steps, the measurement results of 10 point clouds (P1 to P10) within the working area were analyzed, and the deviation results are shown in the table below:

[0086]

[0087] As can be seen, after multiple measurements, the average deviations of the measurement results in the X, Y, and Z axes were 0.0176m, 0.0177m, and 0.0181m, respectively. On the foundation pit excavation work surface of a transmission line with a distance of 10 meters, the pit excavation measurement accuracy can be controlled to be no higher than 2‰.

[0088] S4. Self-calibration of target coordinates is performed based on observations and error correction models, and error correction is performed through an adaptive error weight allocation mechanism based on environmental characteristics. The process is as follows:

[0089] In the initial observation of step S1, the terrain slope (through lidar point cloud computing) and reflectivity value (through laser echo intensity analysis) of target point A are recorded simultaneously.

[0090] Error correction weights are defined based on slope (α) and reflectivity (β):

[0091] W x =k1×e -α +k1×β,W y W z Similarly, corrections are made, where k1 and k2 are experimental calibration coefficients, and the obtained W... x W y W z During the coordinate correction process, the corresponding error Δε is respectively... x-A , Δε y-A , Δε z-A Multiplication enables dynamic error compensation;

[0092] The closed-loop iterative optimization process utilizes a multi-level data verification mechanism, as follows:

[0093] After removing coarse errors in S2, the spatial consistency of the point cloud data is verified using the relative positions of reference control point B and target point A. If the point cloud density in a certain area is lower than a threshold (e.g., <10 points / m²), the spatial consistency is checked. 2 This triggers a local rescan. After self-calibration in S4, the corrected coordinates are back-calculated to the original measurement parameters (L, θ, ψ). The theoretical coordinates are regenerated through steps S1-102 and compared with the actual corrected coordinates. If the deviation exceeds a threshold (e.g., 0.02m), the S2-S4 process is automatically iterated until convergence. The number of iterations and the final deviation value are recorded as pit-division quality assessment indicators for post-construction acceptance.

[0094] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for error self-correction control of a power transmission line foundation trench three-dimensional laser radar device, characterized in that, Comprise the following steps: S1, based on Cartesian coordinate system, define the distance L of three-dimensional laser radar equipment to target point A, horizontal angle θ, vertical angle ψ, identify the relative position relationship of three-dimensional laser radar equipment to target point, determine the coordinates of target point in Cartesian coordinate system, form the initial observation result; S2, select reference control point B, remove noise and gross error points, correct vertical direction offset, and finely process three-dimensional laser point cloud data; S3, define the error of X, Y, Z three-axis direction in Cartesian coordinate system respectively Based on the device position information calibrated by the multi-mode positioning device bound by the three-dimensional laser radar, an error correction model of the three-dimensional laser point cloud based on the device is established. S4, based on the observation value and error correction model, the self-correction of the target coordinate is carried out, and the error is corrected through the adaptive error weight distribution mechanism based on the environment characteristics; The specific process of S1 is as follows: S1-101: define the measurement parameters of three-dimensional laser radar basic trench equipment: With the position of the three-dimensional laser radar-based trenching equipment as the origin O (0, 0, 0), the straight-line distance from the three-dimensional laser radar-based trenching equipment to any target point in the trenching operation surface is defined as . ​ The horizontal included angle θ is defined as the clockwise rotation of the north direction along the three-dimensional laser radar base trench equipment on the horizontal plane to the measured target point angle between directions; The vertical included angle ψ is defined as the three-dimensional laser radar base trench equipment rotates upward or downward along the horizontal plane to measure the target point The angle between the directions; S1-102: Establishing relative position relationship between three-dimensional laser radar basic trenching equipment and measurement target point of the measurement target point According to the parameters defined in step S1-101, the relative position relationship between three-dimensional laser radar basic trench equipment and measurement target point A is established, and the coordinate information in Cartesian coordinate system is converted through the following formula: 、 、 After the conversion is completed, the obtained coordinate information is the initial observation result formed based on the current three-dimensional laser radar basic trench equipment measurement data; The specific operation steps of S2 are as follows: S2-101: According to the provided pit excavation design file, the position of the center stake of the work site is determined as a reference control point using a real-time dynamic positioning device to provide a reference for subsequent data correction; S2-102: Determine any target point in the sub-pit working face under the Cartesian coordinate system with the reference control point Difference between the values: 、 、 ； S2-103: when or or the corresponding target point in the sub-pit operation surface is considered to be a gross error point, and is deleted to refine the point cloud data obtained by the three-dimensional laser radar device; wherein is a dynamic threshold value. is a dynamic threshold value. S2-104: Using the screened three-dimensional laser point cloud data to estimate the relative position, and using the following formula to determine the relative position of any measurement target point in the pit operation surface to the reference control point , , , .

2. The method according to claim 1, wherein The dynamic threshold The determination process is as follows: Integrate temperature and humidity sensor in three-dimensional laser radar equipment, real-time collect temperature T and humidity H data of working environment; The influence of temperature and humidity on laser ranging error is calibrated by experiment, and a linear relationship model is established: wherein is the deviation of current temperature and humidity from the calibration reference value, is the correction coefficient; Dynamic threshold calculation wherein is a calibrated distance, is a fixed threshold.

3. The method of claim 1, wherein the method further comprises: The specific operation steps of S3 are as follows: S3-101: Define any target measurement point within the pit operation The errors in the X, Y, Z three-axis directions in the Cartesian coordinate system are respectively : ; where R is a reference control point Euler angle rotation matrix to the origin O (0, 0, 0) of the location of the three-dimensional laser radar base pit equipment: ； Three angles, pitch angle θ, yaw angle ψ, and roll angle φ, are respectively reference control points For the clockwise rotation angle of the Cartesian coordinate system X, Y, Z axes, usually no additional roll angle φ is needed, and the pitch angle θ and the yaw angle ψ can be calculated by the following formula: θ = arctan(Y B , X A ), ψ = arctan(Y B , X Comprise the following steps: ) ; S3-102: Based on the step S3-101, any target measurement point in the pit operation The coordinate is corrected to obtain the corrected coordinate information: 。 4. The method of claim 1, wherein, The specific operation steps of S4 are as follows: In the initial observation of step S1, the terrain slope of target point A is calculated through laser radar point cloud and the reflectivity value is analyzed through laser echo intensity; According to the slope α and reflectivity β, the error correction weight is defined: , Similarly, the correction is made, wherein is the experimental calibration coefficient, and the resulting In the coordinate correction process, respectively, with the corresponding error multiplied, the dynamic error compensation is realized.

5. The method of error self-correction control of the power transmission line foundation trench three-dimensional laser radar equipment according to claim 1, characterized in that, S4 also includes: Use multi-level data checking mechanism for closed loop iteration optimization, the process is as follows: After completing the gross error point elimination of S2, the relative position of reference control point B and target point A is used to check the spatial consistency of point cloud data; if the point cloud density of a certain area is lower than the threshold value, trigger local rescan; After S4 self-correction, the corrected coordinates are back-propagated to the original measurement parameters, including L, θ, ψ, and the theoretical coordinates are regenerated through step S1-102, compared with the actual corrected coordinates; if the deviation exceeds the threshold value, automatically iterate S2-S4 process until convergence; Record the iteration number and final deviation value as trench quality evaluation index, used for post-construction acceptance.

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