Road measurement method and system using airborne laser radar to replace vehicle-mounted laser radar

Abstract

The present invention discloses a road measurement method and system in which an airborne laser radar replaces a vehicle-mounted laser radar. The method includes obtaining laser point cloud data, satellite observation data, inertial navigation data and aerial images; tightly coupling the satellite observation data, inertial navigation data and aerial images to obtain high-precision trajectory data; fusing and solving the high-precision trajectory data with the laser point cloud data to obtain the original point cloud data; obtaining preset target point information, correcting the original point cloud data based on the target point information, and obtaining the corrected high-precision point cloud data; removing the interference point cloud in the high-precision point cloud data to obtain the ground point cloud data; processing the ground point cloud data to determine the road elevation. The solution provided by the present invention can achieve the accuracy of a vehicle-mounted laser radar and has the efficiency of an airborne laser radar; at the same time, it avoids the safety hazards of on-road measurement on closed highways.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic survey, and in particular to a road measurement method and system in which an airborne laser radar replaces a vehicle-mounted laser radar. Background Art

[0002] The reconstruction and expansion of expressways requires widening of existing bridges and culverts on both sides, and at the same time, it is necessary to ensure that the longitudinal and lateral indicators of the road surface meet the design requirements. Therefore, in the process of expressway reconstruction and expansion, it is necessary to accurately measure the old road surface. According to the requirements of the expressway reconstruction and expansion survey specification, the elevation survey accuracy of the old road surface must be within 2cm. However, the current status of expressway reconstruction and expansion survey has the following defects:

[0003] (1) The current conventional GNSS-RTK measurement method cannot meet the accuracy requirements. Only high-precision leveling control measurement can meet the accuracy index, but the leveling measurement efficiency is low. Each measurement point needs to transmit the elevation through the level rod. This measurement method is only suitable for control point measurement and is not suitable for surface engineering.

[0004] (2) Manual segmented leveling is used. To meet the design requirements, traffic needs to be closed. However, since highways are often arterial channels, closing traffic will cause a series of problems.

[0005] (3) With the rapid development of LiDAR technology in recent years, a few large-scale central enterprise survey and design institutes in China have adopted vehicle-mounted LiDAR to accurately measure the pavement of old roads. The elevation accuracy of the pavement can reach within 3 cm. The efficiency has been greatly improved compared with conventional measurements. However, it is still inevitable to perform target point leveling measurements on the pavement of old roads during traffic maintenance. At the same time, vehicle-mounted LiDAR can only scan part of the pavement, and the slopes of the highway and surrounding structures need to be measured separately.

[0006] To sum up the above, the existing measurement method for the reconstruction and expansion of old roads has technical problems such as low measurement accuracy and efficiency and affects the traffic on expressways. Summary of the invention

[0007] In order to solve the technical problems of low measurement accuracy, low measurement efficiency and impact on highway traffic in the existing measurement method for reconstruction and expansion of old roads, an embodiment of the present invention provides a road measurement method and system in which an airborne laser radar replaces a vehicle-mounted laser radar.

[0008] The technical solution of the embodiment of the present invention is achieved as follows:

[0009] An embodiment of the present invention provides a road measurement method in which an airborne laser radar replaces a vehicle-mounted laser radar, and the method includes: obtaining laser point cloud data, satellite observation data, inertial navigation data and aerial images collected by a drone when performing road measurement according to a preset route; tightly coupling the satellite observation data, the inertial navigation data and the aerial images to obtain high-precision trajectory data; fusing and solving the high-precision trajectory data with the laser point cloud data to obtain original point cloud data; obtaining preset target point information, and correcting the original point cloud data based on the target point information to obtain corrected high-precision point cloud data; performing point cloud classification on the high-precision point cloud data, removing interference point clouds in the high-precision point cloud data, and obtaining ground point cloud data; processing the ground point cloud data to determine the road surface elevation.

[0010] In one embodiment, the satellite observation data, the inertial navigation data and the aerial image are tightly coupled to obtain high-precision trajectory data, including: obtaining the satellite observation data synchronously received by a ground reference station and the installation position of the ground reference station; performing joint differential calculation on the satellite observation data synchronously received by the ground reference station, the satellite observation data collected by the UAV and the installation position of the ground reference station to obtain the flight trajectory coordinates of the UAV in a geodetic coordinate system; performing coordinate transformation on the flight trajectory coordinates to obtain trajectory coordinate data of the UAV in a project coordinate system; tightly coupling the trajectory coordinate data of the UAV in the project coordinate system with the inertial navigation data and the aerial image to obtain high-precision trajectory data.

[0011] In one embodiment, the flight trajectory coordinates are transformed, including: establishing a high-precision conversion relationship model between a geodetic coordinate system and a project coordinate system; using the conversion relationship model to transform the flight trajectory coordinates to obtain trajectory coordinate data of the UAV in the project coordinate system.

[0012] In one embodiment, a high-precision conversion relationship model between a geodetic coordinate system and a project coordinate system is established, including: setting the location where the ground reference station is set as a preset control point, obtaining first coordinates of multiple preset control points in the geodetic coordinate system and second coordinates in the project coordinate system; dividing the multiple preset control points into a first control point set and a second control point set; selecting a first preset number of preset control points from the first control point set; using the first coordinates and second coordinates of the first preset number of preset control points to obtain an initial conversion relationship model between the geodetic coordinate system and the project coordinate system; selecting a second preset number of preset control points from the second control point set; substituting the first coordinates of the second preset number of preset control points into the initial conversion relationship model to obtain conversion coordinates; calculating the error between the conversion coordinates and the second coordinates of the second preset number of preset control points; and judging whether the error is large or small. a preset error threshold; when the error is greater than the preset error threshold, reselect a first preset number of preset control points from the first control point set, use the first coordinates and the second coordinates of the first preset number of preset control points to obtain an initial transformation relationship model between the geodetic coordinate system and the project coordinate system, and again select a second preset number of preset control points from the second control point set; substitute the first coordinates of the second preset number of preset control points into the initial transformation relationship model to obtain transformation coordinates; calculate the error between the transformation coordinates and the second coordinates of the second preset number of preset control points, and determine whether the error is greater than the preset error threshold; repeat the above process until the error is less than or equal to the preset error threshold; determine the initial transformation relationship model between the geodetic coordinate system and the project coordinate system obtained at last as the final high-precision transformation relationship model between the geodetic coordinate system and the project coordinate system.

[0013] In one embodiment, the transformation relationship model is a 7-parameter model, wherein the 7 parameters include three translation parameters, three rotation parameters, and one scale parameter. The initial transformation relationship model between the geodetic coordinate system and the project coordinate system is obtained by using the first coordinates and the second coordinates of the first preset number of preset control points, including: using the first coordinates and the second coordinates of the first preset number of preset control points, the initial transformation relationship model between the geodetic coordinate system and the project coordinate system is obtained by the following calculation formula:

[0014] Calculation formula (1)

[0015] in, , , is the first coordinate, , , is the second coordinate, ΔX, ΔY, ΔZ are three translation parameters, , , are three rotation parameters and m is the scale parameter.

[0016] In one embodiment, calculating the error between the converted coordinates and the second coordinates of the second preset number of preset control points includes: calculating the error between the converted coordinates and the second coordinates of the second preset number of preset control points using the following calculation formula;

[0017] Calculation formula (2)

[0018] Where σ is the error, , is the difference between the transformed coordinates of the i-th preset control point and the second coordinate in the x-coordinate direction, the y-coordinate direction and the z-coordinate direction, and n' is the number of transformed coordinates.

[0019] In one embodiment, the original point cloud data is corrected based on the target point information, including: for each point cloud in the original point cloud data, determining a target point whose distance from the point cloud is within a preset range; assigning a corresponding weight to the target point according to the distance between the target point and the point cloud; and correcting the elevation of the point cloud based on the weight and elevation of the target point.

[0020] In one embodiment, assigning a corresponding weight to the target point according to the distance between the target point and the point cloud includes: assigning a corresponding weight to the target point according to the distance between the target point and the point cloud using the following calculation formula:

[0021] Calculation formula (3)

[0022] in, is the weight assigned to the elevation correction of each target point i, D i is the distance between the point cloud and the target point i, and n is the number of target points.

[0023] In one embodiment, the elevation of the point cloud is corrected based on the weight and elevation of the target point, including: correcting the elevation of the point cloud based on the weight and elevation of the target point using the following calculation formula:

[0024] Calculation formula (4)

[0025] in, is the corrected elevation of point cloud Vi, is the weight assigned to the elevation correction of each target point i, is the correction value for each target point i, is the difference between the elevation of target point i and the average elevation of the point cloud within the preset range of target point i, and n is the number of target points.

[0026] An embodiment of the present invention also provides a road measurement system in which an airborne laser radar replaces a vehicle-mounted laser radar, comprising: a target point, a drone and a processor; the target point is set at an intersection of a highway, an adjacent line or a hardened site; the target point is the connection point of two diagonally arranged squares; the squares are painted with predicted high-reflectivity paint; the drone is used to collect laser point cloud data, satellite observation data, inertial navigation data and aerial images when measuring roads according to a preset route; the processor is used to execute the steps of the above-mentioned method.

[0027] The road measurement method and system provided by the embodiment of the present invention uses an airborne laser radar to replace a vehicle-mounted laser radar, obtains laser point cloud data, satellite observation data, inertial navigation data and aerial images collected by a drone when measuring roads according to a preset route; tightly couples the satellite observation data, the inertial navigation data and the aerial images to obtain high-precision trajectory data; fuses and solves the high-precision trajectory data with the laser point cloud data to obtain original point cloud data; obtains preset target point information, corrects the original point cloud data based on the target point information, and obtains corrected high-precision point cloud data; classifies the high-precision point cloud data, removes interference point clouds in the high-precision point cloud data, and obtains ground point cloud data; processes the ground point cloud data to determine the road elevation. The solution provided by the present invention uses an airborne laser radar to replace a vehicle-mounted laser radar, which can achieve the accuracy of a vehicle-mounted radar and take into account the efficiency of an airborne radar, while also avoiding the safety hazards of closed highways for on-road measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A schematic flow chart of a road measurement method in which an airborne laser radar replaces a vehicle-mounted laser radar according to an embodiment of the present invention;

[0029] Figure 2 This is a schematic diagram of the operation process of an airborne laser radar according to an embodiment of the present invention;

[0030] Figure 3 This is a schematic diagram of route planning according to an embodiment of the present invention;

[0031] Figure 4 A schematic diagram of setting up a base station for synchronous observation according to an embodiment of the present invention;

[0032] Figure 5 A schematic diagram of a base station antenna height measurement according to an embodiment of the present invention;

[0033] Figure 6 This is a schematic diagram of airborne laser radar point cloud solution according to an embodiment of the present invention;

[0034] Figure 7 This is a schematic diagram of point cloud correction according to an embodiment of the present invention;

[0035] Figure 8 The following is a schematic diagram of target point layout and measurement according to an embodiment of the present invention; wherein: Figure 8 (a) is a schematic diagram of asphalt pavement layout. Figure 8 (b) is a schematic diagram of cement pavement layout;

[0036] Fig. 9 Schematic diagram of target point recognition comparison in point cloud according to an embodiment of the present invention; wherein: Fig. 9 (a) is a schematic diagram of the target point effect before improvement. Fig. 9 (b) Schematic diagram of the improved target point effect;

[0037] Fig.10 1 is a diagram showing the internal structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

[0039] The embodiment of the present invention provides a road measurement method using an airborne laser radar instead of a vehicle-mounted laser radar, such as Figure 1 As shown, the method includes:

[0040] Step 101: Obtaining laser point cloud data, satellite observation data, inertial navigation data, and aerial images collected when the drone performs road measurement according to a preset route;

[0041] Step 102: Tightly couple the satellite observation data, the inertial navigation data and the aerial image to obtain high-precision trajectory data;

[0042] Step 103: fusing and solving the high-precision trajectory data with the laser point cloud data to obtain original point cloud data;

[0043] Step 104: obtaining preset target point information, and correcting the original point cloud data based on the target point information to obtain corrected high-precision point cloud data;

[0044] Step 105: performing point cloud classification on the high-precision point cloud data, removing interference point clouds in the high-precision point cloud data, and obtaining ground point cloud data;

[0045] Step 106: Process the ground point cloud data to determine the road surface elevation.

[0046] This embodiment uses drones equipped with laser radar, Beidou positioning, IMU inertial navigation, high-definition aerial cameras and other high-precision digital sensors to cooperate with each other to collect data, uses laser radar to scan the three-dimensional point cloud of the highway and the high-definition orthophoto for data fusion, and adopts a series of data processing methods, and finally achieves that the airborne laser radar can achieve the accuracy of the vehicle-mounted laser radar and take into account the efficiency of the airborne laser radar, while also avoiding the safety hazards of closed highways for on-road measurement. This embodiment has been successfully applied to the G4 Beijing-Hong Kong-Macao Expressway (Leiyi Section) expansion project and has passed the expert acceptance. Various accuracy indicators and social and economic benefits have reached the domestic advanced level.

[0047] Specifically, see Figure 2 ,The main workflow of airborne laser radar field data collection is as follows:

[0048] This embodiment uses the AA1300HL UAV laser radar system for data collection. The AA1300HL UAV laser radar system integrates various high-performance sensors, such as high-precision, long-range laser scanners, tactical-level inertial navigation systems, high-definition cameras, etc., and can collect a variety of data. The main technical parameters of the AA1300HL UAV laser radar system are shown in Table 1 below.

[0049] Table 1

[0050]

[0051] In this embodiment, the drone will perform road survey according to the preset route. Before the drone performs the flight operation, it is necessary to first perform survey area division, route planning, aerial photography factor calculation, etc. according to the preliminary field survey, preliminary design topographic map, low-resolution DEM of the survey area and related topographic and geomorphological data. The survey area route parameter design is shown in Table 2, the single route aerial photography factor calculation design is shown in Table 3, and the route design is shown in Table 4. Figure 3 .

[0052] Table 2

[0053]

[0054] Table 3

[0055]

[0056] Please note the following when flying:

[0057] (1) The flight altitude within the same route should not exceed 5%~10% of the relative altitude, and the actual altitude change should not exceed 5%~10% of the designed altitude;

[0058] (2) The aircraft flight speed should be kept consistent throughout the entire measurement area, and the flight speed change within the same route should not exceed 10m / s;

[0059] (3) The pitch angle and roll angle of the route are generally less than 2° and the maximum cannot be greater than 4°; the route curvature cannot exceed 3%; the bank angle of the aircraft when turning cannot exceed 15°;

[0060] (4) If there are many water bodies and ponds in the survey area, appropriate photographic conditions should be selected to avoid overexposure of the digital image;

[0061] (5) Before takeoff and after landing, the system should be observed statically for at least 10 minutes. This project conducted 30 minutes of static observation. For INS that needs to be activated, it is necessary to fly an inverted "8" route 5 to 10 minutes before entering the survey area to complete the north search. However, for INS systems that can automatically find the north, they can directly enter the survey area after static observation. After data collection is completed, another inverted "8" route should be flown within 5 to 10 minutes.

[0062] During the flight operation, in order to ensure the accuracy of the results, an "8" flight should be performed before entering the operation route to activate the radar IMU device, and coordinated turns should be used at the bends to ensure the accuracy of POS positioning and attitude determination, and to ensure the accuracy of the aerial photography external orientation element solution. The speed is maintained between 5m / s-7m / s, and the lateral overlap rate of the laser point cloud is not less than 60%, ensuring that the collected laser point cloud has a high heading and lateral point spacing, and has sufficient density to facilitate the use of checkpoints for plane and elevation accuracy verification and point cloud correction in the later stage.

[0063] After the drone completes the flight mission, it will collect laser point cloud data, satellite observation data, inertial navigation data and aerial images. In this embodiment, the satellite observation data, inertial navigation data and aerial images need to be tightly coupled to obtain high-precision trajectory data; the high-precision trajectory data is fused and solved with the laser point cloud data to obtain the original point cloud data.

[0064] Here, the satellite observation data, the inertial navigation data and the aerial image are tightly coupled to obtain high-precision trajectory data, including: obtaining the satellite observation data synchronously received by the ground reference station and the installation position of the ground reference station; performing joint differential calculation on the satellite observation data synchronously received by the ground reference station, the satellite observation data collected by the UAV and the installation position of the ground reference station to obtain the flight trajectory coordinates of the UAV in the geodetic coordinate system; performing coordinate transformation on the flight trajectory coordinates to obtain the trajectory coordinate data of the UAV in the project coordinate system; tightly coupling the trajectory coordinate data of the UAV in the project coordinate system with the inertial navigation data and the aerial image to obtain high-precision trajectory data.

[0065] In this embodiment, a reference station is set up on the ground. A receiver is installed on the reference station to receive satellite signals synchronously with the drone's onboard receiver. During post-solution, the observation data of the reference station and the onboard mobile station are jointly differentially solved to obtain the three-dimensional spatial position of the route. Since the coordinates of the ground reference station are known, the distance correction number from the reference station to the satellite can be calculated using the coordinates of the reference station and the coordinates obtained by receiving satellite signals, thereby improving the positioning accuracy of the onboard receiver. In this embodiment, the location of the reference station is defined as the control point. See Figure 4 , Figure 4 A schematic diagram of setting up a base station for synchronous observation according to an embodiment of the present invention.

[0066] In this embodiment, the base station will use the China Measurement X12 dual-frequency receiver.

[0067] The ground base stations will be deployed as follows:

[0068] (1) The distance between the base station and the take-off point is controlled within 5 km. The base stations of this project are all set up on existing GPS points. The existing control points in the survey area fully meet the requirements for the base station setting of this project.

[0069] (2) The field of view around the point is wide, there are no obstacles above the height cutoff angle of 15°, and the sampling frequency is set to 1 Hz;

[0070] (3) The distance between the measuring station and high-power radio radiation sources (such as TV stations, microwave stations, etc.) should be no less than 500m;

[0071] (4) The distance from high-voltage transmission lines and substations shall not be less than 200m.

[0072] In addition, the ground reference station must meet the following requirements during observation:

[0073] (1) Start collecting data 15 minutes before the LIDAR system is started and stop collecting data 15 minutes after the system is shut down. Accurately record the startup time and shutdown time.

[0074] (2) Observers shall strictly follow the operating instructions in the GNSS receiver operating manual when performing observation operations.

[0075] (3) When the antenna is placed on the tripod and directly centered and leveled, the centering error is less than 1 mm.

[0076] (4) During observation, no people or other objects should be allowed to move around the antenna to avoid hitting the antenna or blocking the signal.

[0077] (5) The antenna height should be measured before and after each observation period. The difference between the two antenna heights should not exceed 2 mm, and the average value should be taken as the final antenna height.

[0078] (6) The observation record form must be filled in strictly in accordance with the specifications on site.

[0079] (7) After the daily observation, the field data files shall be promptly transferred to the storage medium, the integrity and soundness of the data shall be checked, and double backup of the data shall be performed.

[0080] See also Figure 5 , which is a schematic diagram of the base station antenna height measurement in an embodiment of the present invention. In addition, the ground base station observation data is shown in Table 4 below.

[0081] Table 4

[0082]

[0083] During field measurements, the mobile station collects satellite observation data and receives data from the base station through a data link, composes differential observation values ​​within the system for real-time processing, and converts the observed geodetic coordinates into plane coordinates in the project coordinate system through coordinate transformation methods.

[0084] When the mobile station collects satellite observation data, the following operating points should be noted:

[0085] (1) Before each observation, the instrument should be initialized and a fixed solution should be obtained. If a fixed solution cannot be obtained for a long time, the communication link should be disconnected and the initialization operation should be performed again.

[0086] (2) The mobile station was restarted and reinitialized between each survey to ensure independent observation;

[0087] (3) The single observation plane convergence accuracy of the control point is no more than ±2cm;

[0088] (4) During the measurement, the mobile station was centered and leveled using a tripod, and 30 epochs were observed each time with an interval of 2 seconds. The error of the plane coordinates of each measurement was less than 4 cm;

[0089] (5) Take the median of the plane coordinates of each valid measurement as the final result.

[0090] Since the point cloud is processed in the form of longitude and latitude geodetic coordinates (B, L, H), it is necessary to first convert the point cloud into plane coordinates (X, Y, h) before applying it to engineering projects. To convert between the two coordinate systems, it is necessary to first solve the 7 unknown parameters in the coordinate system conversion model. The 7 parameters include: three translation parameters (ΔX, ΔY, ΔZ), three rotation parameters (εx, εy, εz) and a scale parameter (m). The 7 parameters can be used to complete the coordinate conversion.

[0091] This embodiment is to find out the coordinates of more than three control points (i.e. the locations where ground reference stations are set up) in two sets of coordinate systems, and then use the "Bursa model" to obtain 7 parameters and evaluate the coordinate system conversion error.

[0092] Specifically, in one embodiment, the flight trajectory coordinates are transformed, including: establishing a high-precision transformation relationship model between the geodetic coordinate system and the project coordinate system; using the transformation relationship model to transform the flight trajectory coordinates to obtain the trajectory coordinate data of the UAV in the project coordinate system.

[0093] Among them, establishing a high-precision conversion relationship model between the geodetic coordinate system and the project coordinate system includes: obtaining the first coordinates of multiple preset control points in the geodetic coordinate system and the second coordinates in the project coordinate system; dividing the multiple preset control points into a first control point set and a second control point set; selecting a first preset number of preset control points from the first control point set; using the first coordinates and the second coordinates of the first preset number of preset control points to obtain an initial conversion relationship model between the geodetic coordinate system and the project coordinate system; selecting a second preset number of preset control points from the second control point set; substituting the first coordinates of the second preset number of preset control points into the initial conversion relationship model to obtain conversion coordinates; calculating the error between the conversion coordinates and the second coordinates of the second preset number of preset control points; judging whether the error is greater than a preset error threshold; and determining whether the error is greater than a preset error threshold. When the error is greater than a preset error threshold, a first preset number of preset control points are reselected from the first control point set, and the initial transformation relationship model between the geodetic coordinate system and the project coordinate system is obtained by using the first coordinates and the second coordinates of the first preset number of preset control points, and a second preset number of preset control points are again selected from the second control point set; the first coordinates of the second preset number of preset control points are substituted into the initial transformation relationship model to obtain the transformation coordinates; the error between the transformation coordinates and the second coordinates of the second preset number of preset control points is calculated to determine whether the error is greater than a preset error threshold; the above process is repeated until the error is less than or equal to the preset error threshold; the initial transformation relationship model between the geodetic coordinate system and the project coordinate system obtained at last is determined as the final high-precision transformation relationship model between the geodetic coordinate system and the project coordinate system.

[0094] In addition, in one embodiment, the conversion relationship model is a 7-parameter model, wherein the 7 parameters include three translation parameters, three rotation parameters and one scale parameter, and the initial conversion relationship model between the geodetic coordinate system and the project coordinate system is obtained by using the first coordinates and the second coordinates of the first preset number of preset control points, including:

[0095] Using the first coordinates and the second coordinates of the first preset number of preset control points, an initial conversion relationship model between the earth coordinate system and the project coordinate system is obtained by the following calculation formula:

[0096] Calculation formula (1)

[0097] in, , , is the first coordinate, , , is the second coordinate, ΔX, ΔY, ΔZ are three translation parameters, , , are three rotation parameters and m is the scale parameter.

[0098] Here, the error between the converted coordinates and the second coordinates of the second preset number of preset control points is calculated using the following calculation formula;

[0099] Calculation formula (2)

[0100] Where σ is the error, , is the difference between the transformed coordinates of the i-th preset control point and the second coordinate in the x-coordinate direction, the y-coordinate direction and the z-coordinate direction, and n' is the number of transformed coordinates.

[0101] In actual application, the x-coordinate direction, y-coordinate direction, and z-coordinate direction in the project coordinate system generally refer to the north direction N, the east direction E, and the elevation direction H.

[0102] Since the conversion relationship model obtained in this embodiment is when the error is less than or equal to the preset error threshold, the conversion relationship model in this embodiment is a high-precision conversion relationship model. The coordinates converted using the high-precision conversion relationship model are also high-precision coordinates, and the high-precision coordinates are used to process road elevation, thereby improving the accuracy of the point cloud processing process. That is, using a high-precision conversion relationship model (with an accuracy better than 2 cm) to replace the low-precision RTK measurement coordinates (with an accuracy of 5 cm) for 7-parameter conversion can improve the point cloud accuracy by more than 3 cm.

[0103] In this embodiment, the airborne laser radar point cloud data is mainly solved by Copre2.0 point cloud solution software. The ground base station of the airborne laser scanning uses the ground static base station mode to collect ephemeris data, and uses the GNSS first-level control points arranged in the survey area as the ground base station installation points. The base station data can all be solved by GNSS, and the solution state fixation rate within the route range is high, and there is no loss of lock. The three-dimensional coordinate plane of the laser point cloud uses the control point plane results, and the elevation uses the leveling results.

[0104] The main processing flow of airborne laser radar point cloud solution is as follows: Figure 6 shown.

[0105] After data collection is completed, the point cloud data must be pre-processed and the point cloud accuracy must be sampled and verified. Only when the data is qualified can you leave the site. If the point cloud data in some areas is unqualified, corrective or re-measurement measures should be taken.

[0106] Under normal circumstances, the laser radar system can obtain the following data in a single acquisition:

[0107] 1) GNSS base station data, which is the GNSS data observed by the base station;

[0108] 2) POS collects data, mainly including mobile station GNSS data and inertial navigation data;

[0109] 3) Laser data, which is the data scanned by a laser scanner;

[0110] 4) Image data, i.e. photos taken by the camera.

[0111] In this embodiment, the point cloud solution is mainly carried out using CoPre2.0, a mobile measurement data preprocessing software, which can realize the synchronous display of high-precision POS tracks and online or offline maps, and can download, load and browse maps in real time. The solution area can be selected by line selection and frame selection, and the POS track can be automatically filtered by the GNSS signal value to quickly realize the solution of the original laser data, display the solution progress in real time and automatically output the LAS format point cloud in segments. Static data can be automatically filtered during the solution process, and the data can be classified for multiple echoes, supporting distance filtering, gray value filtering and jump noise filtering. The software also includes functions such as photo organization and point cloud coloring, which basically realizes one-click automatic processing. At the same time, the software also supports multiple coordinate system conversions to meet the production needs of different engineering projects.

[0112] After obtaining the original point cloud data through fusion solution, the original point cloud data can be corrected.

[0113] In this embodiment, the target points are accurately measured and then the point cloud is corrected through the target points. However, the number of point clouds is massive, generally billions of point cloud data, and our target control points are limited, generally with a spacing of about 200 meters and evenly distributed along both sides of the route. How to use limited target points to improve the accuracy of billions of point clouds? If only a simple fixed difference correction is relied upon, the point cloud accuracy cannot be effectively improved, and even gross errors or mistakes may be introduced.

[0114] Therefore, this embodiment proposes an inverse distance weight method for elevation correction fitting. That is, the closer the point in the point cloud is to the target point, the more it matches the target point, and the greater the weight of its correction number, otherwise it will reduce the match with the target point. Through this algorithm, the perfect match between the point cloud and the target point can be achieved, and finally the correction of the target point to the point cloud can be achieved. The specific algorithm is implemented as follows:

[0115] In one embodiment, correcting the original point cloud data based on the target point information includes:

[0116] For each point cloud in the original point cloud data, determining a target point whose distance from the point cloud is within a preset range;

[0117] According to the distance between the target point and the point cloud, a corresponding weight is assigned to the target point;

[0118] The elevation of the point cloud is corrected based on the weight and elevation of the target point.

[0119] Among them, the corresponding weight is assigned to the target point according to the distance between the target point and the point cloud using the following calculation formula:

[0120] Calculation formula (3)

[0121] in, is the weight assigned to the elevation correction of each target point i, D i is the distance between the point cloud and the target point i, and n is the number of target points.

[0122] Based on the weight and elevation of the target point, the elevation of the point cloud is corrected using the following calculation formula:

[0123] Calculation formula (4)

[0124] in, is the corrected elevation of point cloud Vi, is the weight assigned to the elevation correction of each target point i, is the correction value for each target point i, is the difference between the elevation of target point i and the average elevation of the point cloud within the preset range of target point i, and n is the number of target points.

[0125] The point cloud correction process described above will be described in detail below using a practical application example.

[0126] See also Figure 7 In this embodiment, target points are arranged alternately along the old road at intervals of about 200m. In this embodiment, all target points (such as Figure 7), taking the plane coordinates (Xi, Yi) of the target point as the center, read the elevations of all point clouds Vi within a circle with a radius of 60 cm, and then calculate the average elevation Hi of all point clouds Vi; then calculate the difference between the target point elevation HB and the point cloud average elevation Hi, that is, .

[0127] Since the target point spacing is 200m, in this embodiment, each target point is taken as the center and the distance Di from all point clouds to the target point within a range of 300m is read;

[0128] That is, the elevation correction value of each point Vi in the point cloud is affected by target points 1, 2, and 3 within the target point spacing of 300m. However, due to the different distances between Vi and the three target points, different weights need to be applied to the three target points.

[0129] This embodiment uses the inverse distance weight method to perform elevation correction on each point cloud Vi, and its mathematical formula is:

[0130] ;

[0131] in, is the corrected elevation of point cloud Vi, is the weight assigned to the elevation correction of each target point i, is the correction value for each target point i, is the difference between the elevation of target point i and the average elevation of the point cloud within the preset range of target point i, and n is the number of target points; D i is the distance between the point cloud and the target point i, and n is the number of target points.

[0132] Within the range of 300m as the center of the circle, the above steps are repeated and iterated to correct the elevation of each point cloud; until the high-precision correction of the inverse distance weight of the point cloud along the entire line is achieved.

[0133] After the point cloud is corrected, it can be further classified. The corrected high-precision point cloud data includes all ground objects, including the ground, tree tops, tree trunks, roofs, etc. However, in actual engineering applications, we need to remove non-ground points such as houses and vegetation and only retain the ground point cloud data. This process is point cloud classification. In actual engineering applications, TerraSolid software is used to classify high-precision point cloud data, and ultimately only retains ground point cloud data.

[0134] After point cloud correction is completed, the ground point cloud data can be processed to determine the road surface elevation.

[0135] The embodiment method has the following innovative features:

[0136] (1) Vehicle-mounted laser radar measurement can only scan highway road surface data, and data outside the road surface must be measured by airborne laser, which is inefficient; secondly, the point cloud correction target points must be placed on the old highway road to be scanned, and the highway must be closed for measurement, which affects traffic and poses safety risks. However, this application adopts an airborne method, so the point cloud correction target points can be set outside the highway and scanned, which can correct the point cloud and improve accuracy. At the same time, it also avoids the safety risks of closed traffic affecting traffic and on-road measurement, making it possible for the airborne laser radar solution to replace the vehicle-mounted laser radar solution, which not only meets the accuracy requirements, but also improves efficiency and reduces costs.

[0137] (2) Use high-precision known control points to set up a GNSS base station, and conduct synchronous observations with the GNSS mobile station on the UAV. The base station and the mobile station can jointly perform post-differential processing, so as to correct the errors of the positioning results of the UAV mobile station through the high-precision known points of the base station. This can accurately obtain the real-time high-precision positioning of the UAV, and provide the basic conditions for the UAV laser radar to obtain high-precision point clouds.

[0138] (3) The original coordinate system of the airborne laser radar is not an engineering coordinate system. It is necessary to solve the conversion relationship between the two coordinate systems. An inaccurate conversion relationship model will lead to reduced point cloud accuracy. This application uses the high-precision static measurement control point results of the highway project to replace the low-precision real-time measurement RTK results to solve the coordinate system conversion model, thereby improving the coordinate conversion accuracy.

[0139] (4) By accurately measuring the target points, the point cloud is corrected through the target points. However, the number of point clouds is huge, generally billions of point cloud data, and our target control points are limited, generally with a spacing of about 200 meters and evenly distributed along both sides of the route. How to use limited target points to improve the accuracy of billions of point clouds cannot be achieved by simply correcting the fixed difference. The point cloud accuracy cannot be effectively improved, and even gross errors or errors may be introduced. This application uses an inverse distance weighted algorithm for elevation fitting and correction, that is, different correction weights are calculated based on the distance between the point cloud and the target point, thereby reducing the workload of setting up target points while improving the accuracy of point cloud correction.

[0140] (5) Through repeated experiments, high-reflection materials (pearl powder, glass beads, etc.) were added to the target point coating to achieve high reflectivity of the laser radar. The target points can be identified more accurately in the point cloud results, effectively improving the recognition accuracy and resolution of the target points. The point cloud plane and elevation are corrected through higher-resolution target points, which improves the point cloud accuracy and ultimately realizes the solution of airborne laser radar replacing vehicle-mounted radar.

[0141] The road measurement method and system provided by the embodiment of the present invention uses an airborne laser radar to replace a vehicle-mounted laser radar, obtains laser point cloud data, satellite observation data, inertial navigation data and aerial images collected by a drone when measuring roads according to a preset route; tightly couples the satellite observation data, the inertial navigation data and the aerial images to obtain high-precision trajectory data; fuses and solves the high-precision trajectory data with the laser point cloud data to obtain original point cloud data; obtains preset target point information, corrects the original point cloud data based on the target point information, and obtains corrected high-precision point cloud data; classifies the high-precision point cloud data, removes interference point clouds in the high-precision point cloud data, and obtains ground point cloud data; processes the ground point cloud data to determine the road elevation. The solution provided by the present invention uses an airborne laser radar to replace a vehicle-mounted laser radar, which can achieve the accuracy of a vehicle-mounted radar and take into account the efficiency of an airborne radar, while also avoiding the safety hazards of closed highways for on-road measurement.

[0142] In order to implement the method of the embodiment of the present invention, the embodiment of the present invention also provides a road measurement system in which an airborne laser radar replaces a vehicle-mounted laser radar, including: a target point, a drone and a processor; the target point is set at an intersection of a highway, an adjacent line or a hardened site; the target point is the connection point of two diagonally arranged squares; the square is painted with a predicted high-reflectivity paint; the drone is used to collect laser point cloud data, satellite observation data, inertial navigation data and aerial images when measuring roads according to a preset route; the processor is used to execute the steps of the above-mentioned method.

[0143] In this embodiment, target points are arranged alternately along the two sides of the old road at a distance of about 200m, using two squares with a side length of 60cm arranged diagonally, and a mold is used to paint the diagonal grid with white or black paint according to the road surface material. Figure 8 ,like Figure 8 As shown in (a), the asphalt road surface is painted white. Figure 8 As shown in (b), the cement road surface is painted black, and a self-modulated high-reflectivity special coating is added to the paint (specifically pearl powder and glass beads. Pearl powder and glass beads are added to the paint, which has strong reflection to the lidar and can significantly improve the scanning resolution of the target point cloud, thereby improving the accuracy of the point cloud plane elevation). The reflection intensity of the target point is increased to facilitate post-processing and identification. The target point is used to calibrate the point cloud data, correct the POST data, and effectively improve the overall accuracy of the point cloud. A measurement mark is nailed at the center to facilitate the measurement and identification of the checkpoint.

[0144] See also Fig. 9 , Fig. 9 (a) Schematic diagram of the target point effect before improvement. Fig. 9(a) The target point has more noise in the point cloud and poor recognition. Fig. 9 (b) Schematic diagram of the improved target point effect. Fig. 9 (b) The target point has clear corners in the point cloud and is highly recognizable.

[0145] In this embodiment, the target point elevation is measured by fourth-order leveling, and the plane measurement is measured by network RTK mode according to the requirements of the first-level GNSS control point. The operation is carried out strictly in accordance with the first-level GNSS control point accuracy in the specification "Global Positioning System Real-time Dynamic Measurement (RTK) Technical Specification" (CH / T2009-2010), the RTK satellite window is good, the number of satellites within the cut-off elevation angle of 15° is ≥6, and the PDOP value is less than 4.

[0146] This embodiment has the following advantages:

[0147] (1) High precision: Through a series of technical processing, the elevation accuracy of the old road surface can reach 2cm, meeting the requirements of the specifications.

[0148] (2) High efficiency: The drone equipped with radar can scan the road surface and off-road ancillary facilities (slopes, retaining walls, etc.) at a high viewing angle and over a wide range. Compared with the vehicle-mounted scanning solution that can only scan the road surface, this embodiment does not require manual supplementary measurement for the off-road parts, which can improve measurement efficiency.

[0149] (3) High safety: Due to the use of airborne laser radar scanning, the target points can be placed outside the old road, avoiding the problem of closing the highway to measure the target points in the vehicle-mounted method, thereby reducing safety risks.

[0150] (4) No impact on high-speed traffic: Since there is no need to close traffic, there is no impact on high-speed traffic.

[0151] The above-mentioned system provided in this embodiment belongs to the same concept as the above-mentioned method embodiment. The specific implementation process thereof is detailed in the method embodiment and will not be repeated here.

[0152] In order to implement the method of the embodiment of the present invention, the embodiment of the present invention also provides a computer program product, which includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps of the above method.

[0153] Based on the hardware implementation of the above program modules, and in order to implement the method of the embodiment of the present invention, the embodiment of the present invention further provides an electronic device (computer device). Specifically, in one embodiment, the computer device may be a terminal, and its internal structure diagram may be as follows: Fig.10As shown. The computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05 and a memory (not shown in the figure) connected through a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A06. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor A01, the method of any one of the above embodiments is implemented. The display screen A04 of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device A05 of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0154] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0155] The device provided by the embodiment of the present invention includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the method of any one of the above embodiments is implemented.

[0156] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0157] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0158] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0159] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0160] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0161] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0162] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0163] It can be understood that the memory of the embodiment of the present invention can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAMbus random access memory (DRRAM, Direct Rambus Random Access Memory).The memories described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memories.

[0164] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0165] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A road measurement method using an airborne laser radar instead of a vehicle-mounted laser radar, characterized in that: The method comprises: Obtain laser point cloud data, satellite observation data, inertial navigation data and aerial images collected by the drone when measuring roads according to the preset route; Tightly coupling the satellite observation data, the inertial navigation data and the aerial image to obtain high-precision trajectory data; The high-precision trajectory data is fused and solved with the laser point cloud data to obtain original point cloud data; Acquiring preset target point information, and correcting the original point cloud data based on the target point information to obtain corrected high-precision point cloud data; Performing point cloud classification on the high-precision point cloud data, removing interference point clouds in the high-precision point cloud data, and obtaining ground point cloud data; Processing the ground point cloud data to determine the road elevation; Wherein, the satellite observation data, the inertial navigation data and the aerial image are tightly coupled to obtain high-precision trajectory data, including: obtaining the satellite observation data synchronously received by the ground reference station and the installation position of the ground reference station; performing joint differential calculation on the satellite observation data synchronously received by the ground reference station, the satellite observation data collected by the UAV and the installation position of the ground reference station to obtain the flight trajectory coordinates of the UAV in the geodetic coordinate system; performing coordinate conversion on the flight trajectory coordinates to obtain the trajectory coordinate data of the UAV in the project coordinate system; tightly coupling the trajectory coordinate data of the UAV in the project coordinate system with the inertial navigation data and the aerial image to obtain high-precision trajectory data; The coordinate conversion of the flight trajectory coordinates includes: establishing a high-precision conversion relationship model between the earth coordinate system and the project coordinate system; using the conversion relationship model to convert the flight trajectory coordinates to obtain the trajectory coordinate data of the UAV in the project coordinate system; Among them, establishing a high-precision conversion relationship model between the geodetic coordinate system and the project coordinate system includes: setting the location where the ground reference station is set as a preset control point, obtaining the first coordinates of multiple preset control points in the geodetic coordinate system and the second coordinates in the project coordinate system; dividing the multiple preset control points into a first control point set and a second control point set; selecting a first preset number of preset control points from the first control point set; using the first coordinates and second coordinates of the first preset number of preset control points to obtain an initial conversion relationship model between the geodetic coordinate system and the project coordinate system; selecting a second preset number of preset control points from the second control point set; substituting the first coordinates of the second preset number of preset control points into the initial conversion relationship model to obtain conversion coordinates; calculating the error between the conversion coordinates and the second coordinates of the second preset number of preset control points; and judging whether the error is greater than the preset value. Set an error threshold; when the error is greater than the preset error threshold, reselect a first preset number of preset control points from the first control point set, use the first coordinates and second coordinates of the first preset number of preset control points to obtain an initial conversion relationship model between the geodetic coordinate system and the project coordinate system, and again select a second preset number of preset control points from the second control point set; substitute the first coordinates of the second preset number of preset control points into the initial conversion relationship model to obtain conversion coordinates; calculate the error between the conversion coordinates and the second coordinates of the second preset number of preset control points, and determine whether the error is greater than the preset error threshold; repeat the above process until the error is less than or equal to the preset error threshold; determine the final initial conversion relationship model between the geodetic coordinate system and the project coordinate system as the final high-precision conversion relationship model between the geodetic coordinate system and the project coordinate system; Among them, correcting the original point cloud data based on the target point information includes: for each point cloud in the original point cloud data, determining a target point whose distance from the point cloud is within a preset range; assigning a corresponding weight to the target point according to the distance between the target point and the point cloud; and correcting the elevation of the point cloud based on the weight and elevation of the target point.

2. The road measurement method using airborne laser radar instead of vehicle-mounted laser radar according to claim 1 is characterized in that: The conversion relationship model is a 7-parameter model, which includes three translation parameters, three rotation parameters and one scale parameter. The initial conversion relationship model between the geodetic coordinate system and the project coordinate system is obtained by using the first coordinates and the second coordinates of the first preset number of preset control points, including: Using the first coordinates and the second coordinates of the first preset number of preset control points, an initial conversion relationship model between the earth coordinate system and the project coordinate system is obtained by the following calculation formula: Calculation formula (1) Among them, △ x ,△ y ,△ z is the first coordinate, x '、 y '、 z ' is the second coordinate, ΔX, ΔY, ΔZ are three translation parameters, ε x , ε y , ε z are three rotation parameters and m is the scale parameter.

3. The road measurement method using airborne laser radar instead of vehicle-mounted laser radar according to claim 2 is characterized in that: Calculating the error between the transformed coordinates and the second coordinates of the second preset number of preset control points includes: Calculating the error between the transformed coordinates and the second coordinates of the second preset number of preset control points using the following calculation formula; Calculation formula (2) Where σ is the error, , is the difference between the transformed coordinates of the i-th preset control point and the second coordinate in the x-coordinate direction, the y-coordinate direction and the z-coordinate direction, and n' is the number of transformed coordinates.

4. The road measurement method using airborne laser radar instead of vehicle-mounted laser radar according to claim 1 is characterized in that: According to the distance between the target point and the point cloud, a corresponding weight is assigned to the target point, including: According to the distance between the target point and the point cloud, the corresponding weight is assigned to the target point using the following calculation formula: Calculation formula (3) Among them, Wi is the weight assigned to the elevation correction of each target point i, D i is the distance between the point cloud and the target point i, and n is the number of target points.

5. The road measurement method using airborne laser radar instead of vehicle-mounted laser radar according to claim 1 is characterized in that: Correcting the elevation of the point cloud based on the weight and elevation of the target point includes: Based on the weight and elevation of the target point, the elevation of the point cloud is corrected using the following calculation formula: Calculation formula (4) in, is the corrected elevation of point cloud Vi, Wi is the weight assigned to the elevation correction of each target point i, is the correction value for each target point i, is the difference between the elevation of target point i and the average elevation of the point cloud within the preset range of target point i, and n is the number of target points.

6. A road measurement system using an airborne laser radar instead of a vehicle-mounted laser radar, characterized in that: include: Target points, drones, and processors; The target point is set at the intersection of the expressway, adjacent line or hardened site; The target point is the connection point of two diagonally arranged squares; the squares are painted with the predicted high reflectivity paint; The drone is used to collect laser point cloud data, satellite observation data, inertial navigation data and aerial images when measuring roads according to a preset route; The processor is used to execute the steps of the method according to any one of claims 1 to 5.

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