A simulation modeling method for roads and buildings along the road based on real road surface data

Abstract

The present invention relates to the field of simulation modeling technology, and discloses a method for simulating and modeling roads and buildings along the road based on real road surface data, comprising the following steps: S1, data acquisition: selecting an area to be measured and using a drone or other equipment to take panoramic photos to collect materials; S2, data processing and modeling: using the photos to generate a simulation model with road surface attributes and building attributes; S3, model integration: importing road surface and building data into three-dimensional software to produce a three-dimensional building simulation model; and S4, model correction. The present invention maps each base point where the drone is located into a node in sequence, making it convenient for staff to package and organize data according to each base point, determine the coordinate system position of each node, and establish a structure tree model along the road including multiple nodes. By establishing the structure tree model along the road, it is convenient for staff to organize data, and by establishing each node, the occurrence of model penetration can be reduced, and the work efficiency of staff can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of simulation modeling, and in particular to a method for simulation modeling of roads and buildings along the roads based on real road surface data. Background Art

[0002] Lane-level high-precision electronic maps are sub-meter precision maps primarily designed for autonomous vehicles, or robot drivers. Compared to traditional navigation maps, high-precision electronic maps offer greater real-time performance, more accurate representation of road features, and richer semantic information. After obtaining lane-level high-precision electronic map data, autonomous vehicles can use high-precision positioning to align themselves with the electronic map network in real time. By comparing data from the vehicle's high-precision perception system with the electronic map network data in real time, they can implement route planning and avoidance of other road users. This reduces the need for onboard high-precision perception systems and is an essential component of current autonomous vehicle technology.

[0003] However, existing lane-level high-precision electronic maps are limited by GPS accuracy, resulting in a lack of detailed road surface models and positioning accuracy, which cannot fully meet the needs of high-precision road simulation modeling. Therefore, those skilled in the art have provided a method for simulating and modeling roads and buildings along the road based on real road surface data to address the issues raised in the background art above. Summary of the Invention

[0004] The purpose of the present invention is to provide a road and roadside building simulation modeling method based on real road surface data to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for simulating and modeling roads and buildings along the roads based on real road surface data comprises the following steps:

[0007] S1. Data collection:

[0008] Select an area to be measured, select a coordinate starting point on the area to be measured, and collect the positions of different points on the edge of the area to be measured, so as to limit the range to be measured, set up a flight route, and establish multiple base points in sequence at equidistant positions along the flight route with the coordinate starting point. Use drones and other equipment to take panoramic photos of the road surface and the surrounding environment at the corresponding multiple base points to collect materials, obtain real road surface data through multiple aerial survey methods at multiple base point positions, map each base point where the drone is located into a node in sequence, and determine the coordinate system position of each node, establish a structure tree model along the line including multiple nodes, correspond each building on both sides of the road surface in the structure tree model, collect data on each part of each building, and establish logical associations based on the positional relationship between each building in each node.

[0009] S2. Data processing and modeling:

[0010] Based on the ground photos taken by the drone at each base point, lane-level road network data is established, and a logical relationship between each node is established on the lane-level road network data. The lane-level road network data is imported into the three-dimensional software to generate a simulation model with road surface attributes. The structure tree model along the line at each node is imported into the three-dimensional software in node order, and the logical relationship between each node is established to generate a simulation model with building attributes.

[0011] S3. Model integration:

[0012] The real road surface data obtained through various aerial survey methods are imported into the 3D software, so that the simulation model with road surface attributes can be improved into a 3D road network simulation model that conforms to the real road surface conditions. The data collected from each part of each building are imported into the 3D software, so that the simulation model with building attributes can be improved into a 3D building simulation model that conforms to the real building shape, and then the 3D models are integrated.

[0013] S4. Model modification:

[0014] The accuracy of the model is measured by comparing the model operation data with the actual road data. If there is a large error, the data is corrected through multiple data comparisons and actual measurements until there is no large error in multiple consecutive sets of data.

[0015] As a further solution of the present invention: in step S1, the area to be measured can be selected within the range of the drone, or selected in a circular range with the coordinate starting position. The coordinate starting position can be selected from the center position, the position with a landmark building, or the intersection of multiple routes.

[0016] As a further solution of the present invention: in step S1, the flight route can prioritize the main road and then the branch road, and can also fly in a circular manner with the coordinate starting point as the center. At the same time, the flight route is set at different heights, so that the building can be photographed and collected at different heights for use as multi-directional measurement data.

[0017] As a further solution of the present invention: in step S1, a variety of aerial survey methods including but not limited to vertical aerial photography, drone oblique photography, field lidar to generate point cloud data, field RTK marking, etc., are used to extract various road surface and building information.

[0018] As a further solution of the present invention: in step S1, the buildings include but are not limited to fixed objects such as houses, flower beds, and courtyard walls, and the data of each part include but are not limited to the locations of doors and windows, trees, flowers and plants, etc.

[0019] As a further solution of the present invention: in step S2, taking each base point as a unit, the ground photos taken at each base point are used as the base map to automatically identify and generate lane-level road network data through a computer algorithm, and a simulation model with road surface attributes is generated through the lane-level road network data, and each simulation model is connected to each other.

[0020] As a further solution of the present invention: in step S2, simulation models with building attributes generated from building photos taken at various base points are connected to various units respectively, and each simulation model with building attributes is matched with real data.

[0021] As a further solution of the present invention: in step S3, the real road surface data includes but is not limited to road lights, road surface undulations, traffic lights, etc.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] The present invention selects an area to be measured and a coordinate starting point on the area to be measured, thereby facilitating the staff to establish a suitable coordinate system, and at the same time collects the positions of different points at the edge of the area to be measured, thereby limiting the range to be measured and avoiding detecting unnecessary areas. By setting up a flight route, it is convenient for the staff to set the route of the drone in advance, or remotely control the drone according to the actual situation of the set flight route, and establishes multiple base points in sequence at equidistant positions along the flight route with the coordinate starting point, so that the staff can take fixed-point photos in the designated area and collect image data. The drone and other equipment can be used to take panoramic photos of the road surface and the surrounding environment of the road surface at the corresponding multiple base points to collect materials, and multi-directional photos are taken at multiple base points to facilitate the fixed-point data sorting, avoid repeated photos and improve work efficiency. At the same time, the panoramic photos of the road surface and the surrounding environment of the road surface can be convenient for the staff Establish a database of road surface photos and road surrounding environment photos, and obtain real road surface data at multiple base point locations through multiple aerial survey methods. Multiple aerial survey methods can maximize the measurement accuracy of real road surface data. Map each base point where the drone is located into a node in sequence, so that the staff can package and organize the data according to each base point, and determine the coordinate system position of each node. Establish a structure tree model along the line including multiple nodes. By establishing the structure tree model along the line, it is convenient for the staff to organize the data. Each building on both sides of the road is mapped to the structure tree model, and the data of each part of each building is collected to obtain more complete building data. A logical association is established based on the positional relationship between each building in each node, so that the staff can compare the simulation model based on the positional relationship between each building, thereby quickly testing the simulation model.

[0024] Based on the ground photos taken by the drone at each base point, lane-level road network data is generated, so as to facilitate data conversion of the ground photos, and the logical relationship between each node is established on the lane-level road network data, so as to integrate the lane-level road network data, and the lane-level road network data is imported into the three-dimensional software to generate a simulation model with road surface attributes, and the structure tree model along the line at each node is imported into the three-dimensional software in node order, and the logical relationship between each node is established to generate a simulation model with building attributes. By setting up each node, the situation of model penetration can be reduced, and the work efficiency of the staff can be improved. DETAILED DESCRIPTION

[0025] The following describes the implementation of the present invention using specific embodiments. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. Obviously, the embodiments described are only a portion of the present invention, not all of it. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0026] Preferably, a method for simulating and modeling roads and buildings along the roads based on real road surface data comprises the following steps:

[0027] S1. Data collection:

[0028] Select an area to be measured, select a coordinate starting point on the area to be measured, and collect the positions of different points on the edge of the area to be measured, so as to limit the range to be measured, set up a flight route, and establish multiple base points in sequence at equidistant positions along the flight route with the coordinate starting point. Use drones and other equipment to take panoramic photos of the road surface and the surrounding environment at the corresponding multiple base points to collect materials, obtain real road surface data through multiple aerial survey methods at multiple base point positions, map each base point where the drone is located into a node in sequence, and determine the coordinate system position of each node, establish a structure tree model along the line including multiple nodes, correspond each building on both sides of the road surface in the structure tree model, collect data on each part of each building, and establish logical associations based on the positional relationship between each building in each node.

[0029] S2. Data processing and modeling:

[0030] Based on the ground photos taken by the drone at each base point, lane-level road network data is established, and a logical relationship between each node is established on the lane-level road network data. The lane-level road network data is imported into the three-dimensional software to generate a simulation model with road surface attributes. The structure tree model along the line at each node is imported into the three-dimensional software in node order, and the logical relationship between each node is established to generate a simulation model with building attributes.

[0031] S3. Model integration:

[0032] The real road surface data obtained through various aerial survey methods are imported into the 3D software, so that the simulation model with road surface attributes can be improved into a 3D road network simulation model that conforms to the real road surface conditions. The data collected from each part of each building are imported into the 3D software, so that the simulation model with building attributes can be improved into a 3D building simulation model that conforms to the real building shape, and then the 3D models are integrated.

[0033] S4. Model modification:

[0034] The accuracy of the model is measured by comparing the model operation data with the actual road data. If there is a large error, the data is corrected through multiple data comparisons and actual measurements until there is no large error in multiple consecutive sets of data.

[0035] Preferably, the area to be measured can be selected within the range of the drone, or selected in a circular range with the coordinate starting position. The coordinate starting position can be selected as a central position, a position with a landmark building, or an intersection of multiple routes.

[0036] Preferably, the flight route can prioritize the main roads and then the branch roads. It can also fly in a circular manner with the coordinate starting point as the center. At the same time, the flight route is set at different heights, so that the building can be photographed and collected at different heights for use as multi-directional measurement data.

[0037] Preferably, a variety of aerial survey methods include but are not limited to vertical aerial photography, drone oblique photography, field lidar to generate point cloud data, field RTK marking, etc., to extract various road surface and building information.

[0038] Preferably, the buildings include but are not limited to fixed objects such as houses, flower beds, and courtyard walls, and the data of each part include but are not limited to the locations of doors and windows, trees, flowers, and plants.

[0039] Preferably, taking each base point as a unit, the ground photos taken at each base point are used as the base map to automatically identify and generate lane-level road network data through a computer algorithm, and a simulation model with road surface attributes is generated through the lane-level road network data, and each simulation model is connected to each other.

[0040] Preferably, simulation models with building attributes generated from building photos taken at various base points are connected to various units respectively, and each simulation model with building attributes is matched with real data.

[0041] Preferably, the real road surface data includes but is not limited to road lights, road undulations, traffic lights, etc.

[0042] In summary, the present invention: by selecting an area to be measured and selecting a coordinate starting point position on the area to be measured, it is convenient for staff to establish a suitable coordinate system, and at the same time collect the positions of different points at the edge of the area to be measured, thereby limiting the range to be measured and avoiding detection of unnecessary areas. By setting up a flight route, it is convenient for staff to set the route of the drone in advance, or remotely control the drone according to the actual situation of the set flight route, and establish multiple base points in sequence at equidistant positions along the flight route with the coordinate starting point position, so that staff can take fixed-point photos in the designated area and collect image data. UAVs and other equipment are used to take panoramic photos of the road surface and the surrounding environment of the road surface at the corresponding multiple base points to collect materials, and multi-directional photos are taken at multiple base points to facilitate fixed-point data sorting, avoid repeated photos and improve work efficiency. At the same time, panoramic photos of the road surface and the surrounding environment of the road surface can facilitate workers. The staff established a database of road surface photos and a database of road surface surrounding environment photos, and obtained real road surface data at multiple base point locations through multiple aerial survey methods. Multiple aerial survey methods can maximize the measurement accuracy of real road surface data. Each base point where the drone is located is mapped into a node in sequence, which is convenient for the staff to package and organize data according to each base point, and determine the coordinate system position of each node. A structure tree model along the line is established, which is convenient for the staff to organize data. Each building on both sides of the road is mapped to the structure tree model, and data of each part of each building is collected to obtain more complete building data. Logical associations are established based on the positional relationship between each building in each node, so that the staff can compare simulation models based on the positional relationship between each building, thereby quickly testing the simulation model.

[0043] Based on the ground photos taken by the drone at each base point, lane-level road network data is generated, so as to facilitate data conversion of the ground photos, and the logical relationship between each node is established on the lane-level road network data, so as to integrate the lane-level road network data, and the lane-level road network data is imported into the three-dimensional software to generate a simulation model with road surface attributes, and the structure tree model along the line at each node is imported into the three-dimensional software in node order, and the logical relationship between each node is established to generate a simulation model with building attributes. By setting up each node, the situation of model penetration can be reduced, and the work efficiency of the staff can be improved.

[0044] The above is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solutions and inventive concepts of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A road and building simulation modeling method based on real road surface data, characterized in that: The steps include: S1. Data collection: Select an area to be measured, select a coordinate starting point position on the area to be measured, and collect the positions of different points on the edge of the area to be measured, so as to limit the range to be measured, set up a flight route, and sequentially establish multiple base points at equidistant positions along the flight route with the coordinate starting point position, use drone equipment to take panoramic photos of the road surface and the surrounding environment at the corresponding multiple base points to collect materials, obtain real road surface data at the multiple base point positions through multiple aerial survey methods, map each base point where the drone is located into a node in sequence, and determine the coordinate system position of each node, establish a structure tree model along the line including multiple nodes, correspond each building on both sides of the road surface in the structure tree model, collect data of each part of each building, and establish logical associations based on the positional relationship between each building in each node; S2. Data processing and modeling: Generate lane-level road network data based on ground photos taken by the drone at each base point, establish logical relationships between nodes on the lane-level road network data, import the lane-level road network data into three-dimensional software, thereby generating a simulation model with road surface attributes, import the structure tree model along each node into the three-dimensional software in node order, and establish logical relationships between each node, thereby generating a simulation model with building attributes; S3. Model integration: Importing real road surface data obtained through various aerial survey methods into 3D software, the simulation model with road surface attributes is refined into a 3D road network simulation model that conforms to the actual road surface conditions. Importing data collected from various parts of each building into the 3D software, the simulation model with building attributes is refined into a 3D building simulation model that conforms to the actual building shape. The 3D models are then integrated. S4. Model modification: The accuracy of the model is measured by comparing the model operation data with the actual road data. If there is an error, the data is corrected through multiple data comparisons and actual measurements until there is no error in multiple consecutive sets of data.

2. A road and building simulation modeling method based on real road surface data according to claim 1, characterized in that: In step S1, the area to be measured is selected within the range of the drone, or is selected in a circular range with the coordinate starting point position. The coordinate starting point position selects the center position, the position with a landmark building, or the intersection of multiple routes.

3. The method for simulating and modeling roads and buildings along the roads based on real road surface data according to claim 1, wherein: In step S1, the flight route prioritizes the main roads and then the branch roads. It also flies in a circular manner with the coordinate starting point as the center. At the same time, the flight route is set at different heights, so that the building can be photographed and collected at different heights for use as multi-directional measurement data.

4. The method for simulating and modeling roads and buildings along the roads based on real road surface data according to claim 1, wherein: In step S1, various aerial survey methods including but not limited to vertical aerial photography, drone oblique photography, field lidar to generate point cloud data, field RTK marking, extract various road surface and building information.

5. The method for simulating and modeling roads and buildings along the roads based on real road surface data according to claim 1, wherein: In step S1, the buildings include but are not limited to houses, flower beds, and courtyard walls, and the data of each part include but are not limited to the locations of doors, windows, trees, flowers and plants.

6. The method for simulating and modeling roads and buildings along the roads based on real road surface data according to claim 1, characterized in that: In step S2, taking each base point as a unit, the ground photos taken at each base point are used as the base map to automatically identify and generate lane-level road network data through a computer algorithm, and a simulation model with road surface attributes is generated through the lane-level road network data, and each simulation model is connected to each other.

7. The method for simulating and modeling roads and buildings along the roads based on real road surface data according to claim 6, characterized in that: In step S2, simulation models with building attributes generated from building photos taken at each base point are connected to each unit respectively, and each simulation model with building attributes is matched with real data.

8. The method for simulating and modeling roads and buildings along the roads based on real road surface data according to claim 1, characterized in that: In step S3, the real road surface data includes but is not limited to road lights, road undulations, and traffic lights.