Vehicle positioning data-based BIM (Building Information Modeling) visual modeling method for municipal road engineering

Abstract

According to the BIM visual modeling method based on the vehicle positioning data for the municipal road engineering, positioning and point cloud data are acquired through driving of a vehicle carrying a positioning device and a laser radar system on a road with a preset length, so that real-time positioning three-dimensional coordinates of the vehicle are calculated and obtained in a background server; therefore, the efficient modeling purpose of modeling while driving is achieved, and meanwhile, an established simulation model is compared with an actual construction road to obtain a construction quality evaluation result.

Description

Technical Field

[0001] The present invention relates to a BIM visualization modeling method for municipal road engineering, and particularly to a BIM visualization modeling method for municipal road engineering based on vehicle positioning data, belonging to the field of geographical map drawing. Background Art

[0002] The images obtained by scanning through remote sensing satellites do not contain elevation data, so point cloud technology provides a remedy. However, the problem is that usually, the fusion technology of remote sensing images and point cloud images is required to complete a real three-dimensional map. Due to the blurred contours of the point cloud data, such a map cannot be realistically simulated. For the human visual sense, this type of fused map can essentially only provide coordinate data and cannot realistically present the three-dimensional shape of the road. Surveying by drones requires the additional purchase of expensive drones and requires flying at a fixed time and altitude, and the image processing speed is very slow, often requiring more advanced graphics cards. Therefore, it limits the development of visualization modeling, which is one of the problems.

[0003] On the other hand, existing technologies simply convert design drawings into visualization, for the sake of visualization itself, and do not specifically use visualization for other tasks, such as evaluating the quality of road engineering. Even if the evaluation is carried out, it is an ex-post evaluation after construction is completed. When the evaluation is unqualified, it is already irreparable, which greatly wastes social resources. Therefore, how to timely discover construction quality problems, and conduct real-time supervision to obtain data basis for evaluation, so as to guide the correction of construction, is another problem to be solved urgently. Summary of the Invention

[0004] In view of the above problems in the prior art, the present invention will be redesigned in the following aspects: First, adopt the positioning device carried by the vehicle to provide the map plane coordinates; second, map the coordinates located during the vehicle driving process to the corresponding point cloud map to obtain the corresponding elevation; third, use the positioning coordinates and the corresponding elevation to obtain the real-time three-dimensional positioning during the driving process, so as to form a three-dimensional trajectory of the road while driving. Thus, a realistic road model can be formed through texture rendering or other means.

[0005] Based on the above considerations, the present invention provides a BIM visualization modeling method for municipal road engineering based on vehicle positioning data. The method is completed by a modeling system. The modeling system includes at least one vehicle. Each of the at least one vehicle is equipped with a lidar for collecting point cloud data, a vehicle-mounted positioning system for collecting positioning coordinates, and a remote server installed with BIM software. The method specifically includes the following steps: S1 Build a road with a preset length, so that the vehicle drives along the route of the road with the preset length that has been built from the construction starting point. When driving the vehicle, send the positioning signals at regular intervals to the remote server, and send the lidar point cloud data to the remote server; S2 The server preprocesses the point cloud data, analyzes the corresponding coordinates according to the positioning signals at each moment, and in the point cloud data, finds multiple points near the corresponding coordinates, and calculates the point among these nearby points that is closest to the same horizontal plane projection of the corresponding coordinates on the horizontal plane projection as the calibration point at this moment; S3 Construct sectional models of roads with different widths through BIM software, and continuously generate a 3D road model as the driving progresses; S4 The remote server constructs a simulation three-dimensional rectangular coordinate system in the design drawing file, and uses the visualization script program in the BIM software to extract the coordinates and elevations of the corresponding calibration points of each simulation calibration point in the design drawing file. The simulation calibration point refers to continuing to simulate driving along the route of the subsequent road that has not been built after the road with the preset length in the manner of S2, and its coordinates and elevations are the coordinates and elevations of the corresponding calibration points (i.e., the corresponding simulation calibration points during the simulation driving process) in the corresponding drawing file, forming a simulation road point combination; Create simulation sectional models of roads with different widths through the BIM software, and continuously continue to generate a simulation road three-dimensional model after the 3D road model generated in S3 as the simulation driving progresses; S5 Continue to build a road with a preset length, and perform steps S1 - S4, compare the 3D road model generated through steps S1 - S3 and the simulation road three-dimensional model formed in the corresponding S4 step, so as to evaluate the actual construction quality of the road.

[0006] Optionally, the preset length is 100 - 500m, and the specified time is 1 - 5s.

[0007] Optionally, the route of the road includes the vehicle lane route according to the form of the road, including the center lines of single-lane, multi-lane in the same direction, two-way double-lane, and multi-lane in the opposite direction.

[0008] Optionally, the preprocessing includes noise point processing, and the BIM software includes Revit.

[0009] Optionally, in the point cloud data, finding multiple points near the corresponding coordinates and calculating the point among these nearby points that is closest to the same horizontal plane projection of the corresponding coordinates on the horizontal plane projection as the calibration point at this moment includes: S2-1 Construct a geographical rectangular coordinate system, preset a preset radius of 0.2 - 0.5m, and each of the corresponding coordinates , Is the sequential number of the calibration points, projected in the XY plane of the geodetic rectangular coordinate system , S2-2 projects the point cloud data within the circular area with a preset radius centered on onto the XY plane , is the number of the point cloud within the circular area, then the points in the corresponding point cloud data are the points near multiple corresponding coordinates; S2-3 then calibrates the points , is the time.

[0010] Optionally, the segmented model and the simulation segmented model are models between two adjacent calibration points.

[0011] It can be understood that by performing simulation modeling in the geodetic coordinate system of the point cloud data after real modeling, construction problems such as road deviation, road size, and road elevation can be promptly discovered in comparison with subsequent real construction, thereby avoiding waste of resources in a timely manner.

[0012] Therefore, optionally, evaluating the actual construction quality of the road includes road deviation, road size, and road elevation.

[0013] Optionally, steps S1-S3 are adopted on any already constructed road to generate a 3D model of the road.

[0014] A vehicle for implementing a BIM visualization modeling method for municipal road engineering based on vehicle positioning data includes an intelligent vehicle machine, a lidar for visualization modeling provided on the windshield, and an on-vehicle positioning system on the roof, and the intelligent vehicle machine communicates with the remote server.

[0015] Preferably, the on-vehicle positioning system can be controlled by the intelligent vehicle machine to be turned off. The user can choose to turn on the on-vehicle positioning system for paid participation in modeling, and whether or not to participate, the user can choose whether the positioning signal after turning on is officially confidential or willing to be made public.

[0016] Preferably, the user can specify the geographical range to be made public or specify other users or non-users to whom the disclosure is made.

[0017] Optionally, the user can obtain a notice of paid participation in modeling through the intelligent vehicle machine and apply online to participate.

[0018] Optionally, the method of specifying other users or non-users to whom the disclosure is made is for the handheld intelligent mobile devices of the other users or non-users.

[0019] Preferably, the handheld intelligent mobile device is a smartphone.

[0020] It should be understood that the lidar of the present invention is not used for assisted driving, but for visual modeling. The vehicle with the above structure enables the cars driving on the road to collect data and perform modeling quickly and efficiently.

[0021] Beneficial effects: By comparing the real construction with the simulation results in the form of building while simulating and modeling, the construction quality is evaluated, and engineering problems can be discovered as early as possible. Through the real-time data collection of road vehicles and low-cost and fast background processing, the simulation results and the evaluation results of the construction quality are given. Description of the Drawings

[0022] Figure 1 Schematic diagram of the vehicle collecting positioning information and point cloud data during the driving process in the reverse two-way road with a preset length of 100m in Embodiment 1 of the present invention Figure 2 For Figure 1 The midpoint D of the point cloud data at the end of the road in, the selected nearby points after magnifying the nearby circular area, and the schematic diagram of the selected calibration point method Figure 3 Schematic diagram of the connection between the road three-dimensional model and the simulated road three-dimensional model, in which the comparison diagram between the actually constructed road and the simulated road three-dimensional model according to the drawing file is given Figure 4 Flow chart of step S4 Detailed Description of the Invention

[0023] Embodiment 1 As Figure 1 shown, a BIM visual modeling method for a municipal road project based on vehicle positioning data is completed by a modeling system, which includes a vehicle, a lidar installed on the vehicle, a positioning device on the roof, and a remote server communicating with the vehicle.

[0024] The specific method includes the following steps: S1 Build a reverse two-way road with a preset length of 100m, so that the vehicle starts from the construction starting point (such as Figure 1 ) and drives along the route of the 100m reverse two-way road that has been built. When the vehicle is driving, the positioning signal at every specified 2s is sent to the remote server, and the lidar point cloud data is sent to the remote server; Figure 1 In, a local section of the road with points A, B, and C at three adjacent 2s intervals during the driving process is given, as well as the point D at the end of 100m. The vehicle triggers the sending of the positioning signal and the lidar point cloud data at these points.

[0025] The S2 server processes the noise in the point cloud data, analyzes the corresponding coordinates based on the positioning signals at each moment, and in the point cloud data, finds multiple points near the corresponding coordinates, and calculates the point among these nearby points that is closest to the same horizontal plane projection of the corresponding coordinate on the horizontal plane projection as the calibration point at this moment; Specifically, as Figure 2 shown, taking the end in Figure 1 as an example, there are four point cloud data points a, b, c, and d outside the resolution limit within a circle with site D as the center and a radius of 50 cm.

[0026] As Figure 3 shown, continue to build a simulation road model after the current preset length of 100 m reverse two-way road, specifically including S2-1 to construct a geographical rectangular coordinate system XYZ-O, with a preset radius of 0.5 m, and project each of the corresponding coordinates , as the sequential number of the calibration point, and project it on the XY plane of the geographical rectangular coordinate system . The circled part in the figure is the local range of the 100 m road section in Figure 1 which has three sites A, B, and C.

[0027] S2-2 projects the point cloud data within the circular domain with a preset radius centered on onto the XY plane , is the number of the point cloud within the circular domain, then the points in the corresponding point cloud data are the points near the multiple corresponding coordinates; S2-3 then calibrates the point , is the moment, specifically taking the moment at site B as an example.

[0028] S3 constructs a segmented model of roads with different widths through Revit, and continuously generates a 3D road model as the vehicle travels; S4, as Figure 4 shown, the remote server constructs a simulation three-dimensional rectangular coordinate system 0-X'Y'Z' in the design drawing file, and uses the visualization script program in Revit to extract the coordinates and elevations of the corresponding calibration points of each simulation calibration point in the design drawing file. Figure 4 gives examples of four simulation calibration points.

[0029] The simulation calibration point refers to following the method of S2. After the current preset length of 100 m reverse two-way road under construction, that is, after the end, continue to simulate driving on the route of the subsequent road that has not been built. Its coordinates and elevations are in the corresponding drawing file ( Figure 1 shown), and then continue to simulate driving on the route of the subsequent road that has not been built. Its coordinates and elevations are in the corresponding drawing file ( Figure 4The coordinates and elevations of the corresponding calibration points (i.e., the corresponding simulation calibration points during the simulated driving process) in the figure (as shown) are used to form a combination of simulated road points. By using the Revit, a simulated segmented model of roads with different widths (caused by the change in the number of lanes) is created, and after the continuously generated 3D road model in S3 during the simulated driving, a simulated 3D road model is continued (see Figure 4 ); S5 continues to construct a road with a preset length, and performs steps S1 - S4, comparing the 3D road model generated through steps S1 - S3 and the simulated 3D road model formed in the corresponding S4 step, so as to evaluate the actual construction quality of the road.

[0030] Specifically, to evaluate the actual construction quality of the road as Figure 3 shown, an arbitrary calibration point is selected at the end of the actual construction, for example, point D. A tangent 1 to the driving route is made through D. Similarly, in the simulated 3D road model, a tangent 2 is made through the corresponding point D ( Figure 3 not shown in the figure). In the geographical rectangular coordinate system XYZ - O, the orientations of the two are compared. If it is within the preset angle range (for example, 0.01 - 0.08°), it is qualified; otherwise, it is unqualified. In Figure 3 the figure, for the sake of clarity, the actually constructed road is offset from its actual position so that the tangents 1 and 2 can be more intuitively distinguished.

[0031] Embodiment 2 This embodiment provides a vehicle for implementing the BIM visual modeling method for municipal road engineering based on vehicle positioning data as in Embodiment 1. The vehicle includes an intelligent car machine, and also includes a lidar for visual modeling provided on the windshield as well as a vehicle-mounted positioning system on the roof as Figure 1 shown. The intelligent car machine communicates with the remote server.

[0032] Among them, the vehicle-mounted positioning system can be controlled by the intelligent car machine to be turned off. The user can choose to turn on the vehicle-mounted positioning system for paid participation in modeling, and whether participating or not, the user can choose whether the positioning signal after turning on is officially kept confidential or willing to be made public.

[0033] The user can specify the smartphones of non-users, such as family members or friends, to whom the information is to be made public. The user can also obtain a notice for paid participation in modeling through the intelligent car machine and apply to participate online. Thus, the position where the user's vehicle travels can be known, eliminating the worry about safety during the journey.

Claims

1. A BIM visualization modeling method for municipal road engineering based on vehicle positioning data, characterized in that: The method is completed by using a modeling system, which includes at least one vehicle, each of which is equipped with a laser radar for collecting point cloud data, a vehicle-mounted positioning system for collecting positioning coordinates, and a remote server installed with BIM software, and specifically includes the following steps: S1 constructs a road of preset length so that the vehicle starts from the construction starting point and travels along the route of the road of preset length that has been built. When the vehicle is traveling, a positioning signal is sent to a remote server at a prescribed time interval, and the laser radar point cloud data is sent to the remote server; The S2 server pre-processes the point cloud data, analyzes the corresponding coordinates according to the positioning signal at each moment, and finds multiple points near the corresponding coordinates in the point cloud data, and calculates the point closest to the same horizontal plane projection of the corresponding coordinates in the horizontal plane projection among these nearby points as the calibration point at that moment; S3 uses BIM software to construct segmented models of roads of different widths, and continuously generates three-dimensional models of the roads as driving progresses; S4 remote server constructs a simulated three-dimensional rectangular coordinate system in the design drawing file, and uses the visual script program in the BIM software to extract the coordinates and elevations of the corresponding calibration points of each simulated calibration point in the design drawing file, wherein the simulated calibration point refers to the route of the subsequent road that has not yet been built after the preset length of the road in accordance with S2, and its coordinates and elevations are the coordinates and elevations of the corresponding calibration points in the corresponding drawing file, forming a simulated road point combination; the BIM software is used to create simulated segmented models of roads of different widths, and the simulated road three-dimensional model is continuously generated after the road three-dimensional model generated in S3 as the simulated driving progresses; S5 continues to build a road of a preset length and performs steps S1-S4, comparing the three-dimensional road model generated by steps S1-S3 with the corresponding three-dimensional road model formed by step S4, so as to evaluate the actual construction quality of the road.

2. The method according to claim 1, characterized in that The preset length is 100-500m, and the specified time is 1-5s; The route of a road includes lane routes according to the vehicle form of the road, including single-lane lanes, multiple lanes in the same direction, two lanes in the opposite direction, and the center line of multiple lanes in the opposite direction.

3. The method according to claim 1, characterized in that: In the point cloud data, find multiple points near corresponding coordinates, calculate the point among these nearby points that is closest to the same horizontal plane projection of the corresponding coordinates on the horizontal plane projection, and use it as the calibration point at this moment, including: S2-1 constructs a geographic rectangular coordinate system, presets a preset radius of 0.2-0.5m, and sets each corresponding coordinate , It is the sequential number of the calibration points, projected in the XY plane of the geographic rectangular coordinate system ; S2-2 will Project the point cloud data in a circle with a preset radius centered on the circle onto the XY plane , is the number of the point cloud in the circular domain, then The points in the corresponding point cloud data are points near multiple corresponding coordinates; S2-3 is the calibration point , For the moment.

4. The method according to claim 3, characterized in that The segmented model and the simulated segmented model are models between two adjacent calibration points; The assessment of the actual construction quality of roads includes road deviation, road size, and road elevation.

5. The method according to claim 3, characterized in that: Road deviation includes selecting any calibration point at the end of the actual construction, making a tangent to the driving route through the calibration point, and similarly making another tangent through the corresponding calibration point in the simulated road three-dimensional model, and comparing the orientations of the two in the geographic rectangular coordinate system XYZ-O.

6. The method according to any one of claims 1 to 5, characterized in that Steps S1-S3 are used on any constructed road to generate a three-dimensional road model for the constructed road.

7. A vehicle for implementing the BIM visualization modeling method for a municipal road project based on vehicle positioning data as described in any one of claims 1 to 6, characterized in that: It includes an intelligent vehicle machine, a laser radar arranged on the windshield for visual modeling, and a vehicle-mounted positioning system on the roof. The intelligent vehicle machine communicates with the remote server.

8. The vehicle according to claim 7, characterized in that The vehicle positioning system can be controlled and turned off by the intelligent vehicle computer. Users can choose to turn on the vehicle positioning system for paid participation in modeling. Regardless of whether they participate or not, users can choose to keep the positioning signal after turning it on as official confidential or willing to make it public.

9. The vehicle according to claim 7, characterized in that The user may specify the geographical scope of the disclosure or specify other users or non-users to whom the disclosure is directed; Users can obtain notices of paid modeling participation through the smart car machine and apply to participate online.

10. The vehicle according to claim 9, characterized in that The designated disclosure method is directed to other users or non-users, and is directed to handheld intelligent mobile devices of the other users or non-users.

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