A surveying and mapping result precision evaluation method based on a vehicle-mounted mobile measurement system

Abstract

This invention discloses a method for evaluating the accuracy of surveying results based on a vehicle-mounted mobile measurement system, comprising the following steps: receiving surveying result data, determining the data acquisition area and acquisition route, and acquiring raw 3D laser point cloud data and position data; processing the raw 3D laser point cloud data and position data to obtain point cloud data and position files in the target coordinate system; constructing trajectory reference line one based on the processed position files, forming trajectory reference line two based on time interval segmentation, and forming trajectory reference line three after trajectory quality screening; loading the vector data, point cloud data, and trajectory reference lines of the surveying result sample to be inspected, automatically extracting the relative accuracy detection side length and absolute accuracy detection points, calculating the offset value with the corresponding side length and corresponding point in the surveying result to be inspected, and statistically analyzing the mean square error to achieve the mathematical accuracy evaluation of the surveying result to be inspected. This invention is highly efficient; based on the integrated acquisition of a vehicle-mounted mobile measurement system, efficiency is improved; and accuracy is reliable.

Description

Technical Field

[0001] This invention belongs to the technical field of quality inspection of surveying and mapping geographic information results, and in particular relates to a method for evaluating the accuracy of surveying and mapping results based on a vehicle-mounted mobile measurement system. Background Technology

[0002] Currently, the main method for evaluating the mathematical accuracy of surveying and mapping results is to use surveying instruments such as total stations, rangefinders, and GNSS receivers to manually collect accuracy test data in the field. This data is then compared with corresponding data in the office using software or manually to evaluate the mathematical accuracy of the surveying and mapping results. While this method has advantages such as stable accuracy, it also has disadvantages such as long working hours in the field, high workload, high operating costs, and low efficiency.

[0003] In summary, existing methods for evaluating the mathematical accuracy of surveying and mapping results suffer from technical problems such as high workload and low efficiency. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention provides a method for evaluating the accuracy of surveying results based on a vehicle-mounted mobile measurement system, which features high mechanization, good traceability, and high evaluation efficiency.

[0005] The technical solution adopted by this invention to solve its technical problem is: a method for evaluating the accuracy of surveying results based on a vehicle-mounted mobile measurement system, comprising the following steps:

[0006] Receive surveying and mapping data, determine the data acquisition area and acquisition route, and collect raw 3D laser point cloud data and location data;

[0007] The original 3D laser point cloud data and position data obtained above are processed to obtain point cloud data and position files in the target coordinate system;

[0008] Based on the processed location file, trajectory reference line one is constructed; trajectory reference line two is formed based on time interval segmentation; and trajectory reference line three is formed after trajectory quality screening.

[0009] The system loads vector data, point cloud data, and trajectory reference lines of the mapping results sample to be inspected, automatically extracts the relative accuracy detection side length and absolute accuracy detection points, calculates the offset value with the corresponding side length and corresponding point in the mapping results to be inspected, and statistically evaluates the mathematical accuracy of the mapping results to be inspected.

[0010] Furthermore, the surveying and mapping results are vector data, including water system layers, traffic layers, vegetation layers, and residential map layers.

[0011] Furthermore, the collection area refers to the vehicular roads within the area covered by the sampled survey data; the collection route refers to the route taken by the vehicle-mounted mobile measurement system to collect data within the collection area, which includes at least three areas where satellite signals are not significantly obstructed.

[0012] Furthermore, the vehicle-mounted mobile measurement system includes at least a vehicle-mounted laser scanner, a Global Navigation Satellite System (GNSS) receiver, and an inertial measurement unit (IMU).

[0013] Furthermore, in the process of determining the data acquisition area and acquisition route, and acquiring the original 3D laser point cloud data and location data,

[0014] When traveling back and forth along the data collection route at a speed of 30km / h to 50km / h, the vehicle-mounted mobile measurement system scans the ground features within the data collection area to obtain target data for the data collection area.

[0015] Drive the vehicle-mounted mobile measurement system to an area where satellite signals are not significantly obstructed, stop and remain stationary for 3 to 5 minutes, then drive back and forth on the roads surrounding the area at a speed of 30 km / h to 50 km / h to collect surrounding ground features and obtain target data for the area. The ground features include at least buildings and road edges.

[0016] Furthermore, the location data is processed to obtain a location file, which contains time information, quality information, and location information;

[0017] Set the information of the target data plane coordinate system based on the ellipsoid parameters and projection parameters of the plane coordinate system of the mapping result to be inspected;

[0018] The point cloud data, location files, and target data plane coordinate system information are processed to obtain the point cloud data and acquisition trajectory in the target coordinate system.

[0019] Furthermore, the trajectory reference line is generated by connecting and smoothing the coordinate information in the processed position file;

[0020] The second trajectory reference line is formed by drawing a perpendicular line to the first trajectory reference line every 8-12 seconds, starting from the starting point of the boundary of the first sample, based on the time information in the processed position file, and dividing the trajectory reference line according to the perpendicular point.

[0021] The trajectory reference line three is generated by connecting and smoothing positions that retain a quality information value of 1 and more than 5 consecutive positions based on the quality information in the processed position file.

[0022] Furthermore, the vector data of the sample to be tested is building data in the residential strata;

[0023] The point cloud data is divided into blocks based on trajectory reference line 2;

[0024] Using the house edge line in the vector data of the drawing result sample to be inspected as a reference, a buffer surface of 18-22 cm is made on both sides. The point cloud data falling in the buffer surface is used to obtain the relative accuracy check side length in the two-dimensional plane according to the straight line fitting algorithm. The difference is calculated with the same side length in the drawing result to be inspected. The same side length in the drawing result to be inspected refers to the vector side length at the same position as the detection side in the drawing result to be inspected.

[0025] Repeat this step to obtain 20 to 50 side length differences for each sample, and calculate the relative precision error for each sample;

[0026] The point cloud data is divided into blocks based on the trajectory reference line;

[0027] Using the house boundary line in the vector data of the sample to be inspected as a reference, a buffer surface of 18-22 cm is made on both sides. The point cloud data falling within the buffer surface is automatically fitted into a straight line in a two-dimensional plane using a straight line fitting algorithm. The intersection of each fitted line is used as the detection point with absolute accuracy. The offset value is calculated with the corresponding point in the sample to be inspected. The corresponding point in the sample to be inspected refers to the coordinates of the point with the same attribute as the detection point in the sample to be inspected. The coordinates are automatically obtained by the software.

[0028] Repeat this step to obtain offset values ​​of 20 to 50 corresponding points in the acquisition area, and calculate the absolute precision error;

[0029] The mathematical accuracy of the surveying results is evaluated by summarizing the relative and absolute accuracy errors of each sample.

[0030] Furthermore, the vector data output can be a large-scale topographic map, a geographic entity map, or a high-precision map.

[0031] Furthermore, the vehicle-mounted mobile measurement system performs initialization operations before receiving the surveying and mapping results data.

[0032] The beneficial effects of this invention are: 1) High efficiency: Compared with traditional mathematical accuracy evaluation methods such as total stations and GNSS receivers, traditional methods generally require technicians to use total stations to collect absolute accuracy test points and laser rangefinders to measure relative side lengths, typically requiring two people to complete about 10 frames in two days; this invention, based on an integrated acquisition system of a vehicle-mounted mobile measurement system, extracts test data semi-automatically, typically requiring two people to complete 20 frames in two days, significantly improving efficiency; 2) Reliable accuracy: Compared with traditional mathematical accuracy evaluation methods such as total stations and GNSS receivers, traditional methods generally require technicians to manually observe and obtain test data, where observation errors and random errors cannot be eliminated; this invention eliminates systematic errors in the vehicle-mounted mobile measurement system by using time slicing, improving the reliability of relative accuracy. Furthermore, the vehicle-mounted mobile measurement system requires no human intervention in data acquisition and processing; 3) Good traceability: Compared with traditional mathematical accuracy evaluation methods such as total stations and GNSS receivers, traditional methods cannot reconstruct the actual test location after a single acquisition; this invention semi-automatically acquires test data from point cloud data, allowing for multiple and repeated acquisitions, thus providing good traceability. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of data acquisition for the vehicle-mounted mobile measurement system of the present invention.

[0034] Figure 2 This is a schematic diagram of the location data calculation according to the present invention.

[0035] Figure 3 This is a schematic diagram showing the target plane coordinate system setup for this invention.

[0036] Figure 4 This is a schematic diagram of point cloud data for the present invention.

[0037] Figure 5 This is a schematic diagram illustrating the data loading process of the present invention.

[0038] Figure 6 This is a schematic diagram of the relative accuracy side length acquisition method of the present invention.

[0039] Figure 7 This is a schematic diagram illustrating the accuracy statistics of the present invention. Detailed Implementation

[0040] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0041] A method for evaluating the accuracy of surveying results based on a vehicle-mounted mobile measurement system includes the following steps:

[0042] Step 1: Receive the surveying and mapping results to be tested, and determine the data collection area and collection route. The surveying and mapping results refer to vector data results such as large-scale topographic maps, geographic entities, and high-precision maps; the collection area refers to the vehicular roads within the area covered by the sampled surveying and mapping results; the collection route refers to the route taken by the vehicle-mounted mobile surveying system within the collection area, including at least three areas with no significant obstruction to satellite signals.

[0043] Step 2: Using a vehicle-mounted mobile measurement system, data is collected from the acquisition area along a specific acquisition route to obtain target data. The vehicle-mounted mobile measurement system includes at least: a vehicle-mounted laser scanner, a GNSS receiver, and an inertial measurement unit (IMU); the target data includes: raw 3D laser point cloud data and location data; mainly including:

[0044] Perform initialization operations on the vehicle-mounted mobile measurement system;

[0045] When the vehicle equipped with the vehicle-mounted mobile measurement system, after initialization, travels back and forth within the acquisition area at a speed of 30 km / h to 50 km / h along the acquisition route, the vehicle-mounted mobile measurement system scans the ground features within the acquisition area to obtain target data 1 for the acquisition area. Then, the measurement vehicle is driven to an area with no significant satellite signal obstruction. After the measurement vehicle has been stationary for 3 to 5 minutes, it travels back and forth on the roads surrounding the area with no significant satellite signal obstruction at a speed of 30 km / h to 50 km / h to collect surrounding ground features, obtaining target data 2 for the acquisition area. The ground features include at least: buildings and road edges.

[0046] Step 3: Process the target data to obtain point cloud data in the target coordinate system. The target coordinate system represents the coordinate system of the mapping result to be inspected. This mainly includes:

[0047] The location data is processed using a second data processing software to obtain a location file, wherein the second data processing software includes: Inertial Explorer 8.70 software;

[0048] The target data's plane coordinate system information is set using third data processing software based on the ellipsoidal parameters, projection parameters, and other information of the plane coordinate system of the drawing result to be inspected. The third data processing software includes LeicaInfinity 3.0.1.

[0049] The original point cloud data, the location file, and the target data coordinate system information are processed using a fourth data processing software to obtain point cloud data and acquisition trajectory in the target coordinate system. The fourth data processing software includes PEGASUS Manager v2018.2.2.

[0050] Step 4: Construct trajectory reference line one based on the processed location file; form trajectory reference line two based on time interval segmentation; and form trajectory reference line three after trajectory quality screening. This mainly includes:

[0051] The trajectory reference line is generated by connecting and smoothing the coordinate information in the processed location file.

[0052] The second trajectory reference line is formed by drawing a perpendicular line to the first trajectory reference line every 10 seconds, starting from the boundary starting point, based on the time information (GPSTIME) in the processed location file, and dividing the line according to the perpendicular points.

[0053] The trajectory reference line three is generated by connecting and smoothing positions that retain a quality information (QUALITY) value of 1 and have more than 5 consecutive positions in the processed position file.

[0054] Step 5: Load the vector data, point cloud data, trajectory reference lines, and other relevant data of the mapping result to be inspected into the first data processing software. Automatically extract the relative accuracy detection side lengths and absolute accuracy detection points, calculate the offset values ​​with the corresponding side lengths and points in the mapping result to be inspected, and calculate the mean square error to evaluate the mathematical accuracy of the mapping result to be inspected. The first data processing software is a vehicle-mounted point cloud geographic information result quality inspection software. It mainly includes:

[0055] The vector data, point cloud data, trajectory reference line, and other data of the survey results to be inspected are imported into the first data processing software. The vector data of the survey results to be inspected is generally building data in residential strata.

[0056] In the first data processing software, the point cloud data is divided into blocks according to the second trajectory reference line. The software then uses the house edge line in the vector data of the sample of the drawing result to be detected as a reference to make a 20 cm buffer on both sides. The point cloud data falling in the buffer plane is automatically used to obtain the relative accuracy check side length in the two-dimensional plane according to the straight line fitting algorithm. The difference is calculated with the same side length in the drawing result to be detected. The same side length in the drawing result to be detected refers to the vector side length at the same position as the detection side in the drawing result to be detected. The side length value is automatically obtained by the software. This step is repeated to obtain 20 to 50 differences of the same side length for each sample. The relative accuracy error is calculated for each sample.

[0057] In the first data processing software, the point cloud data is divided into blocks according to the trajectory reference line three. The software then uses the house edge vector data in the drawing result to be detected as a reference to make a 20 cm buffer on both sides. The point cloud data falling in the buffer plane is automatically fitted into a straight line in a two-dimensional plane according to the straight line fitting algorithm. The intersection of each fitted line is used as the detection point with absolute accuracy. The offset value is calculated with the corresponding point in the drawing result to be detected. The corresponding point in the drawing result to be detected refers to the coordinates of the point with the same attribute as the detection point in the drawing result to be detected. The coordinates are automatically obtained by the software. This step is repeated to obtain the offset values ​​of 20 to 50 corresponding points in the collection area and calculate the absolute accuracy error.

[0058] The mathematical accuracy of the surveying results is evaluated by summarizing the relative and absolute accuracy errors of each sample.

[0059] To make the objectives, technical methods, and advantages of this invention clearer, the following will take the evaluation of the mathematical accuracy of a 1:500 digital topographic map of a certain area in Zhejiang Province as an example to clearly and completely describe the technical solution of this invention.

[0060] Step 1:

[0061] First, we receive the 1:500 digital topographic map survey results submitted by the production unit.

[0062] Secondly, a certain number of samples are randomly selected according to the batch size and sampling plan of the surveying and mapping results to be inspected. The batch size refers to the total workload of the surveying and mapping results, and the sampling plan determines the sample size based on the batch size. For example, if the batch size of the 1:500 digital topographic maps to be inspected is 30.1 square kilometers, which is equivalent to 481.6 standard 1:500 map sheets (50cm×50cm), the random sample size according to the sampling plan is 40 sheets. The sampling plan is detailed in GB / T 24356-2009 "Quality Inspection and Acceptance of Surveying and Mapping Results".

[0063] Finally, based on the range of sample distribution and the geographical environment, a data collection route was planned, and five areas with no obvious obstruction to satellite signals were identified. The planning of the collection route mainly referred to Baidu Maps image maps and electronic map data.

[0064] Step 2:

[0065] First, fix the vehicle-mounted mobile measurement system to the measurement vehicle and check the tightness of the fixation. Connect the relevant lines, turn on the power, and start the vehicle-mounted mobile measurement system.

[0066] Secondly, the measurement vehicle is driven to an area where the satellite signal is not significantly obstructed. The measurement vehicle is then stopped and observed statically for 5 minutes. Then, the vehicle-mounted mobile measurement system is initialized by making figure-eight turns, left and right turns, acceleration and deceleration, etc., so that the error of the GNSS receiver and the inertial measurement unit (IMU) of the vehicle-mounted mobile measurement system converges to less than 1.0 and there is no large fluctuation.

[0067] Then, after the initialization operation is completed, the measurement vehicle is driven to the first sample area and driven at a speed of 30km / h to 50km / h along the collection route to collect data on buildings, roads and other features on both sides of the road. The collection of 20 sample target data is completed in sequence.

[0068] Finally, the measurement vehicle is driven to the first area with no significant obstruction to satellite signals. It is left stationary for 5 minutes to allow the error between the GNSS receiver and the IMU of the vehicle-mounted mobile measurement system to converge to less than 1.0. Then, it is driven at a speed of 30 km / h to 50 km / h to collect data on surrounding buildings, roads, and other targets. This process is repeated to sequentially collect data from five areas with no significant obstruction to satellite signals. A schematic diagram of the vehicle-mounted mobile measurement system's data acquisition is shown below. Figure 1 .

[0069] Step 3:

[0070] First, the location data was processed using Inertial Explorer 8.70 data processing software. This location data consisted of observations from the GNSS receiver of the vehicle-mounted mobile measurement system, observations from the IMU inertial navigation unit, and observations from 1Hz base stations within a 20km radius of the acquisition area, encompassing different constellations such as GPS, GLONASS, and BEIDOU. The processing yielded a location file containing location, time, and mass information. A schematic diagram of the location data processing is shown below. Figure 2 .

[0071] Secondly, based on the mapping coordinate system of the 1:500 topographic map, namely the ellipsoid parameters (major axis, minor axis, flattening, etc.) and projection parameters (Gauss-Kruger projection, 3-degree zoning, central meridian, etc.) of the 2000 National Geodetic Coordinate System, the plane coordinate system information of the target data is set in the LeicaInfinity 3.0.1 data processing software; a schematic diagram of the target plane coordinate system setting is shown below. Figure 3 .

[0072] Then, based on the original point cloud data, location files, and target data coordinate system information, data processing was performed using PEGASUS Manager v2018.2.2 data processing software to obtain point cloud data and acquisition trajectories in the 2000 National Geodetic Coordinate System (central meridian 120°). The point cloud data is as follows: Figure 4 .

[0073] Step 4:

[0074] First, based on the planar coordinate information in the processed location file, a trajectory reference line is generated by connecting and smoothing.

[0075] Secondly, based on the time information (GPSTIME) in the processed location file, perpendicular lines are drawn every 10 seconds from the boundary starting point to form trajectory reference line two by dividing the points according to the perpendicular points.

[0076] Finally, based on the quality information in the processed location file, positions with a quality information value of 1 and more than 5 consecutive positions are retained, and then connected and smoothed to generate trajectory reference line three.

[0077] Step 5:

[0078] First, the vehicle-mounted point cloud geographic information results quality inspection software and data processing software load 40 sample vector data sheets of 1:500 topographic maps, point cloud data, trajectory reference lines, and other related data; the data loading diagram is shown below. Figure 5 .

[0079] Secondly, in the vehicle-mounted point cloud geographic information results quality inspection software, the point cloud data is divided into blocks according to the trajectory reference line two; then, using the house boundary lines in the 1:500 topographic map sample sheet as a reference, a 20 cm buffer is made on both sides. The point cloud data falling within the buffer surface is automatically used to obtain the relative accuracy check side length in a two-dimensional plane according to the straight line fitting algorithm, and the difference is calculated with the original side length in the 1:500 topographic map sample sheet; this step is repeated, and for each sample, the software automatically obtains 20 to 50 side length differences of the same name, and calculates the relative accuracy error of each sample; the relative accuracy side length acquisition diagram is shown below. Figure 6 .

[0080] Then, in the vehicle-mounted point cloud geographic information results quality inspection software, the point cloud data is divided into blocks according to the aforementioned trajectory reference line three. A 20cm buffer is then created on both sides of the house edges in the 1:500 topographic map sample sheet as a reference. The point cloud data falling within the buffer surface is automatically fitted into straight lines in a two-dimensional plane using a straight line fitting algorithm. The intersection points of each fitted line are used as absolute accuracy detection points, and their displacement values ​​are calculated with the original house corner points in the 1:500 topographic map sample sheet. This step is repeated, and the software automatically acquires 20 to 50 displacement values ​​of corresponding points in the acquisition area, calculating the absolute accuracy error. A schematic diagram of the absolute accuracy detection point acquisition is shown below. Figure 7 .

[0081] Finally, the mathematical accuracy of the surveying results is evaluated by summarizing the relative accuracy of each sample and the mean error of the absolute accuracy of the collected area.

[0082] The above specific embodiments are used to explain and illustrate the present invention, but not to limit the present invention. Any modifications and changes made to the present invention within the spirit and scope of the claims shall fall within the protection scope of the present invention.

Claims

1. A method for evaluating the accuracy of surveying results based on a vehicle-mounted mobile measurement system, characterized in that, Includes the following steps: Receive surveying and mapping data, determine the data acquisition area and acquisition route, and collect raw 3D laser point cloud data and location data; The original 3D laser point cloud data and position data obtained above are processed to obtain point cloud data and position files in the target coordinate system; The location data is processed to obtain a location file, which contains time information, quality information, and location information; Set the information of the target data plane coordinate system based on the ellipsoid parameters and projection parameters of the plane coordinate system of the mapping result to be inspected; The point cloud data, location file, and target data plane coordinate system information are processed to obtain the point cloud data and acquisition trajectory in the target coordinate system; Based on the processed location file, trajectory reference line one is constructed; trajectory reference line two is formed based on time interval segmentation; and trajectory reference line three is formed after trajectory quality screening. The trajectory reference line is generated by connecting and smoothing the coordinate information in the processed position file; The second trajectory reference line is formed by drawing a perpendicular line to the first trajectory reference line every 8-12 seconds, starting from the starting point of the boundary of the first sample, based on the time information in the processed position file, and dividing the trajectory reference line according to the perpendicular point. The trajectory reference line three is generated by connecting and smoothing positions that retain a quality information value of 1 and more than 5 consecutive positions based on the quality information in the processed position file. Load the vector data, point cloud data, and trajectory reference line of the drawing result sample to be inspected, automatically extract the relative accuracy detection side length and absolute accuracy detection point, calculate the offset value with the same side length and same point in the drawing result to be inspected, and statistically evaluate the mathematical accuracy of the drawing result to be inspected. The vector data of the mapping results sample to be tested is the building data in the residential strata; The point cloud data is divided into blocks based on trajectory reference line 2; Using the house edge line in the vector data of the drawing result sample to be inspected as a reference, a buffer surface of 18-22 cm is made on both sides. The point cloud data falling in the buffer surface is used to obtain the relative accuracy check side length in the two-dimensional plane according to the straight line fitting algorithm. The difference is calculated with the same side length in the drawing result to be inspected. The same side length in the drawing result to be inspected refers to the vector side length at the same position as the detection side in the drawing result to be inspected. Repeat this step to obtain 20 to 50 side length differences for each sample, and calculate the relative precision error for each sample; The point cloud data is divided into blocks based on the trajectory reference line; Using the house boundary line in the vector data of the sample to be inspected as a reference, a buffer surface of 18-22 cm is made on both sides. The point cloud data falling within the buffer surface is automatically fitted into a straight line in a two-dimensional plane using a straight line fitting algorithm. The intersection of each fitted line is used as the detection point with absolute accuracy. The offset value is calculated with the corresponding point in the sample to be inspected. The corresponding point in the sample to be inspected refers to the coordinates of the point with the same attribute as the detection point in the sample to be inspected. The coordinates are automatically obtained by the software. Repeat this step to obtain offset values ​​of 20 to 50 corresponding points in the acquisition area, and calculate the absolute precision error; The mathematical accuracy of the surveying results is evaluated by summarizing the relative and absolute accuracy errors of each sample.

2. The method for evaluating the accuracy of surveying results based on a vehicle-mounted mobile measurement system according to claim 1, characterized in that: The surveying and mapping results are vector data, including water system layers, traffic layers, vegetation layers, and residential map layers.

3. The method for evaluating the accuracy of surveying results based on a vehicle-mounted mobile measurement system according to claim 1, characterized in that: The collection area refers to the vehicular roads within the area covered by the extracted survey data; the collection route refers to the route taken by the vehicle-mounted mobile measurement system to collect data within the collection area, which includes at least three areas where satellite signals are not significantly obstructed.

4. The method for evaluating the accuracy of surveying results based on a vehicle-mounted mobile measurement system according to claim 1, characterized in that: The vehicle-mounted mobile measurement system includes at least a vehicle-mounted laser scanner, a Global Navigation Satellite System (GNSS) receiver, and an inertial measurement unit (IMU).

5. The method for evaluating the accuracy of surveying results based on a vehicle-mounted mobile measurement system according to claim 1, characterized in that: The process of determining the data acquisition area and acquisition route, and acquiring raw 3D laser point cloud data and location data, When traveling back and forth along the data collection route at a speed of 30km / h to 50km / h, the vehicle-mounted mobile measurement system scans the ground features within the data collection area to obtain target data for the data collection area. Drive the vehicle-mounted mobile measurement system to an area where satellite signals are not significantly obstructed, stop and remain stationary for 3 to 5 minutes, then drive back and forth on the roads surrounding the area at a speed of 30 km / h to 50 km / h to collect surrounding ground features and obtain target data for the area. The ground features include at least buildings and road edges.

6. The method for evaluating the accuracy of surveying results based on a vehicle-mounted mobile measurement system according to claim 2, characterized in that: The vector data output is a large-scale topographic map, geographic entity map, or high-precision map.

7. The method for evaluating the accuracy of surveying results based on a vehicle-mounted mobile measurement system according to claim 1, characterized in that: Initialization operations are performed before acquiring raw 3D laser point cloud data and location data.

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