Automated Road Vector Map Generation Method and System Based on Laser Point Cloud Data

Abstract

The present invention discloses an automated road vector map generation method and system based on laser point cloud data. The method includes: obtaining all points in the laser point cloud data, setting a separation model for ground points and non-ground points, calculating the separation value of each point in the laser point cloud data, so as to separate the ground points and non-ground points, and retaining the ground points; extracting the feature vectors of all points, setting a road marking extraction model, calculating the clustering set of road markings, and identifying the road markings in the laser point cloud data; setting a vectorization model for road markings, vectorizing the identified road markings to generate the vector value of each road marking; obtaining real road network data, setting a road network fusion and optimization model, and optimizing the vector value of the road markings to make the vector value of the road markings closest to the real road network data, thereby generating a road vector map.

Description

Technical Field

[0001] The present invention belongs to the technical field of map generation, and more specifically, relates to a method and system for automatically generating a road vector map based on lidar point cloud data. Background Art

[0002] Vector map generation includes data acquisition, processing, storage, and visualization, etc. The following are some of the main technical progress and applications:

[0003] Data Acquisition:

[0004] Remote sensing technology: Using remote sensing devices such as satellites and drones to obtain geographical information, combined with technologies such as spectroscopy and lidar (LiDAR), improves the accuracy and resolution of data.

[0005] Data Processing:

[0006] Automated extraction: Using image processing and machine learning algorithms to automatically extract ground object information from images and convert raster data into vector format.

[0007] Big data technology: With the help of big data processing frameworks such as Hadoop and Spark, it accelerates the processing and analysis of large-scale geographical data.

[0008] Storage and Management:

[0009] Cloud computing: Cloud platforms (such as Google Cloud, AWS, etc.) provide efficient storage solutions, supporting large-scale vector data storage and sharing.

[0010] Database: Geographic Information System (GIS) databases (such as PostGIS) allow the storage and management of complex spatial data, supporting efficient querying and analysis.

[0011] Visualization:

[0012] Web map services: Open source libraries such as Mapbox and Leaflet enable users to easily display and interact with vector maps on web pages.

[0013] Three-dimensional visualization: Combining technologies such as WebGL, it realizes the three-dimensional visualization of vector maps, improving the user experience.

[0014] Application Areas:

[0015] Navigation and positioning: Used in applications for real-time navigation and traffic management, such as Google Maps and Amap.

[0016] Urban planning: Assisting urban planning and management, for environmental monitoring and disaster management.

[0017] Geographical analysis: used for geographical data analysis, spatial decision-making support, etc., and widely applied in fields such as ecology and resource management.

[0018] However, the vector maps generated by the technical solutions in the existing technologies currently have insufficient accuracy. Therefore, a high-precision vector map generation solution is urgently needed. Summary of the Invention

[0019] To solve the above technical problems, the present invention proposes an automated road vector map generation method based on laser point cloud data, including:

[0020] Obtain all the points in the laser point cloud data, set a separation model for ground points and non-ground points, calculate the separation value of each point in the laser point cloud data, so as to separate the ground points and non-ground points, and retain the ground points;

[0021] Extract the feature vectors of all the points, set a road marking extraction model, calculate the clustering set of road markings, and identify the road markings in the laser point cloud data;

[0022] Set a vectorization model for road markings, vectorize the identified road markings, and generate the vector values of each road marking;

[0023] Obtain real road network data, set a road network fusion and optimization model, optimize the vector values of road markings, make the vector values of road markings closest to the real road network data, and thus generate a road vector map.

[0024] Further, the separation model for ground points and non-ground points includes:

[0025]

[0026] Among them, D(p) is the separation value of point p in the laser point cloud data. When the separation value D of point p in the laser point cloud data exceeds the preset separation threshold, then point p in the laser point cloud data is a ground point, N is the number of points in the laser point cloud data, a1 is the first adjustment factor of the separation model, b1 is the second adjustment factor of the separation model, z i is the height feature vector of the i-th point p i in the laser point cloud data, c′ is the third adjustment factor of the separation model, d1 is the fourth adjustment factor of the separation model, N neighbors (p i ) is the number of neighborhood points of the i-th point p i in the laser point cloud data, α is the fifth adjustment factor of the separation model, p i is the i-th point in the laser point cloud data, p j is the j-th point in the neighborhood point set, N′(i) is the i-th point p iThe set of neighborhood points, where σ is an adjustment factor used to control the influence range of the neighborhood.

[0027] Furthermore, the road marking extraction model includes:

[0028]

[0029] Among them, C is the clustering set of road markings, representing the set of center points of the extracted road markings. c j is the center point of the j-th road marking, and F(p i ) is the feature vector of the i-th point p i in the lidar point cloud data. λ is the first adjustment factor of the road marking extraction model, M is the number of center points, p′ is the second adjustment factor of the road marking extraction model, μ is the third adjustment factor of the road marking extraction model, and d′(c j , c k ) is the distance between the center point c j of the j-th road marking and the center point c k of the k-th road marking. η is an adjustment factor used to control the distance influence.

[0030] Furthermore, the vectorization model of the road marking includes:

[0031]

[0032] Among them, V(C) is the vector value of each road marking in the clustering set C of road markings. x i′ is the abscissa of the i'-th node on the road marking, y i′ is the ordinate of the i'-th node on the road marking. β is the first adjustment factor of the vectorization model of the road marking. L i′+1 is the (i'+1)-th node on the road marking, L i′ is the i'-th node on the road marking. p″ is the second adjustment factor of the vectorization model of the road marking. θ is the direction angle of the road marking, and K is the number of nodes on the road marking.

[0033] Furthermore, the road network fusion and optimization model includes:

[0034]

[0035] Among them, M is the number of road markings in the real road network data. R j′ is the vector value of the j'-th road marking in the real road network data. Length(V j′ (C)) is the length of the road marking corresponding to the vector value of the j'-th road marking in the clustering set C of road markings. Curvature(V j′(C) is the curvature of the road marking corresponding to the vector value of the j'-th road marking in the clustering set C of road markings, δ is the adjustment factor of the road network fusion optimization model, V j′ (C) is the vector value of the j'-th road marking in the clustering set C of road markings, is the mean value of the vector values of all road markings in the clustering set C of road markings.

[0036] The present invention also provides an automated road vector map generation system based on laser point cloud data, including:

[0037] A separation module, configured to obtain all points in the laser point cloud data, set a separation model for ground points and non-ground points, calculate the separation value of each point in the laser point cloud data, so as to separate the ground points and non-ground points and retain the ground points;

[0038] A marking recognition module, configured to extract the feature vectors of all points, set a road marking extraction model, calculate the clustering set of road markings, and identify the road markings in the laser point cloud data;

[0039] A vectorization module, configured to set a vectorization model for road markings, vectorize the identified road markings, and generate the vector value of each road marking;

[0040] An optimization module, configured to obtain real road network data, set a road network fusion optimization model, optimize the vector values of road markings, make the vector values of road markings closest to the real road network data, and thus generate a road vector map.

[0041] Further, the separation model for ground points and non-ground points includes:

[0042]

[0043] Wherein, D(p) is the separation value of point p in the laser point cloud data. When the separation value D of point p in the laser point cloud data exceeds the preset separation threshold, then point p in the laser point cloud data is a ground point, N is the number of points in the laser point cloud data, a1 is the first adjustment factor of the separation model, b1 is the second adjustment factor of the separation model, z i is the height feature vector of the i-th point p i in the laser point cloud data, c' is the third adjustment factor of the separation model, d1 is the fourth adjustment factor of the separation model, N neighbors (p i ) is the number of neighboring points of the i-th point p i in the laser point cloud data, α is the fifth adjustment factor of the separation model, p i is the i-th point in the laser point cloud data, p jThe j-th point in the neighborhood point set, and N′(i) is the neighborhood point set of the i-th point p in the lidar point cloud data, where σ is an adjustment factor used to control the influence range of the neighborhood. i

[0044] Furthermore, the road marking extraction model includes:

[0045]

[0046] Among them, C is the clustering set of road markings, representing the set of center points of the extracted road markings. c j is the center point of the j-th road marking. F(p i ) is the feature vector of the i-th point p in the lidar point cloud data. i λ is the first adjustment factor of the road marking extraction model, M is the number of center points, p′ is the second adjustment factor of the road marking extraction model, μ is the third adjustment factor of the road marking extraction model, and d′(c j , c k ) is the distance between the center point c j of the j-th road marking and the center point c k of the k-th road marking, where η is an adjustment factor used to control the influence of the distance.

[0047] Furthermore, the vectorization model of the road markings includes:

[0048]

[0049] Among them, V(C) is the vector value of each road marking in the clustering set C of road markings. x i′ is the abscissa of the i′-th node on the road marking, y i′ is the ordinate of the i′-th node on the road marking. β is the first adjustment factor of the vectorization model of the road markings. L i′+1 is the (i′ + 1)-th node on the road marking, and L i′ is the i′-th node on the road marking. p″ is the second adjustment factor of the vectorization model of the road markings, θ is the direction angle of the road marking, and K is the number of nodes on the road marking.

[0050] Furthermore, the road network fusion and optimization model includes:

[0051]

[0052] Among them, M is the number of road markings in the real road network data. R j′ is the vector value of the j′-th road marking in the real road network data. Length(V j′ ​(C)) is the length of the road marking corresponding to the vector value of the j'-th road marking in the clustering set C of road markings, Curvature(V j′ (C)) is the curvature of the road marking corresponding to the vector value of the j'-th road marking in the clustering set C of road markings, δ is the adjustment factor of the road network fusion optimization model, V j′ (C) is the vector value of the j'-th road marking in the clustering set C of road markings, is the mean value of the vector values of all road markings in the clustering set C of road markings.

[0053] Generally speaking, compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:

[0054] Through the technical solution of the present invention, the accuracy of traffic road network passage in the project can be greatly improved, making the generated vector map closer to the real road. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is the flowchart of the method of Embodiment 1 of the present invention;

[0056] Figure 2 is the system structure diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0057] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0058] The method provided by the present invention can be implemented in the following terminal environment. The terminal may include one or more of the following components: a processor, a storage medium, and a display screen. Among them, at least one instruction is stored in the storage medium, and the instruction is loaded and executed by the processor to implement the method described in the following embodiments.

[0059] The processor may include one or more processing cores. The processor connects various parts inside the terminal through various interfaces and lines, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the storage medium, and by calling data stored in the storage medium.

[0060] The storage medium may include a random access memory (RAM), and may also include a read-only memory (ROM). The storage medium can be used to store instructions, programs, codes, code sets, or instructions.

[0061] The display screen is used to display the user interfaces of various application programs.

[0062] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal. The terminal may include more or fewer components, or combine certain components, or have different component arrangements. For example, the terminal may also include components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be elaborated here.

[0063] Embodiment 1

[0064] As Figure 1 shown, an automated road vector map generation method based on laser point cloud data proposed in an embodiment of the present invention includes:

[0065] Step 101: Obtain all the points in the laser point cloud data, set a separation model for ground points and non-ground points, calculate the separation value of each point in the laser point cloud data, so as to separate the ground points and non-ground points, and retain the ground points;

[0066] Specifically, the separation model for ground points and non-ground points includes:

[0067]

[0068] Among them, D(p) is the separation value of point p in the laser point cloud data. When the separation value D of point p in the laser point cloud data exceeds a preset separation threshold, then point p in the laser point cloud data is a ground point, N is the number of points in the laser point cloud data, a1 is the first adjustment factor of the separation model, b1 is the second adjustment factor of the separation model, z i is the height feature vector of the i-th point p i in the laser point cloud data, c′ is the third adjustment factor of the separation model, d1 is the fourth adjustment factor of the separation model, N neighbors (p i ) is the number of neighborhood points of the i-th point p i in the laser point cloud data, α is the fifth adjustment factor of the separation model, p i is the i-th point in the laser point cloud data, p j is the j-th point in the neighborhood point set, N′(i) is the neighborhood point set of the i-th point p i in the laser point cloud data, and σ is an adjustment factor for controlling the neighborhood influence range.

[0069] Step 102: Extract the feature vectors of all the points, set a road marking extraction model, calculate the clustering set of road markings, and identify the road markings in the laser point cloud data;

[0070] Specifically, the road marking extraction model includes:

[0071]

[0072] Among them, C is the clustering set of road markings, representing the set of the center points of the extracted road markings, and c j is the center point of the j-th road marking, and F(p i ) is the feature vector of the i-th point p i in the lidar point cloud data, λ is the first adjustment factor of the road marking extraction model, M is the number of center points, p' is the second adjustment factor of the road marking extraction model, μ is the third adjustment factor of the road marking extraction model, and d'(c j , c k ) is the distance between the center point c j of the j-th road marking and the center point c k of the k-th road marking, and η is the adjustment factor for controlling the influence of the distance.

[0073] Step 103: Set up a vectorization model for road markings, vectorize the recognized road markings, and generate the vector value of each road marking;

[0074] Specifically, the vectorization model of the road markings includes:

[0075]

[0076] Among them, V(C) is the vector value of each road marking in the clustering set C of road markings, x i′ is the abscissa of the i'-th node on the road marking, y i′ is the ordinate of the i'-th node on the road marking, β is the first adjustment factor of the vectorization model of the road markings, L i′+1 is the (i'+1)-th node on the road marking, L i′ is the i'-th node on the road marking, p'' is the second adjustment factor of the vectorization model of the road markings, θ is the direction angle of the road marking, and K is the number of nodes on the road marking.

[0077] Step 104: Obtain the real road network data, set up a road network fusion and optimization model, optimize the vector value of the road markings, and make the vector value of the road markings closest to the real road network data, so as to generate a road vector map.

[0078] Specifically, the road network fusion and optimization model includes:

[0079]

[0080] Among them, M is the number of road markings in the real road network data, R j′ is the vector value of the j'-th road marking in the real road network data, and Length(V j′(C)) is the length of the road marking corresponding to the vector value of the j'-th road marking in the clustering set C of road markings, Curvature(V j′ (C)) is the curvature of the road marking corresponding to the vector value of the j'-th road marking in the clustering set C of road markings, δ is the adjustment factor of the road network fusion optimization model, V j′ (C) is the vector value of the j'-th road marking in the clustering set C of road markings, is the mean value of the vector values of all road markings in the clustering set C of road markings.

[0081] Specifically, this embodiment further includes calculating the longitudinal section slope and the cross-section slope of the road within a set range. Specifically, first, the ground points are obtained according to the separation model of ground points and non-ground points, and then the lidar point cloud data is sliced to obtain the point set of the ground points in each sliced interval, and variables are set:

[0082] p i =(x i , y i , z i ): the i-th point p i in the lidar point cloud data, including spatial coordinates and elevation information, where z i is the elevation of the i-th point p i ;

[0083] H long (x): the longitudinal section slope function, representing the slope along the x-direction (usually the driving direction of the road);

[0084] H cross (y): the cross-section slope function, representing the slope along the y-direction (usually the lateral width direction of the road);

[0085] Δz i，i+1 ..: the height difference between two points p i and p i+1 , Δz i，i+1 =z i+1 -z i .

[0086] Calculation of the longitudinal section slope

[0087] The longitudinal section slope is usually calculated along the driving direction of the road (usually the x-axis direction), and the slope of the longitudinal section can be calculated by the following formula:

[0088]

[0089] where, ||x i+1 -x i ||2 represents the distance between point p i+1 and point pi The distance in the x - direction (Euclidean distance) is used to normalize the height difference. α″′ is a weight coefficient representing the non - linear sensitivity to slope changes, and N is the number of longitudinal points selected at a given interval d.

[0090] Calculation of the cross - section slope

[0091] The cross - section slope is usually calculated along the transverse width direction of the road (usually the y - axis direction). The slope of the cross - section can be calculated by the following formula:

[0092]

[0093] where, ‖y i+1 - y i ‖2 represents the distance in the y - direction (Euclidean distance) between point p i+1 and point p i for normalizing the height difference. β″′ is a weight coefficient representing the non - linear sensitivity to cross - section slope changes, and M is the number of transverse points selected at a given interval d.

[0094] Embodiment 2

[0095] As Figure 2 shown, the embodiment of the present invention also provides an automated road vector map generation system based on laser point cloud data, including:

[0096] A separation module for obtaining all the points in the laser point cloud data, setting a separation model for ground points and non - ground points, calculating the separation value of each point in the laser point cloud data, thereby separating the ground points and non - ground points, and retaining the ground points;

[0097] Specifically, the separation model for ground points and non - ground points includes:

[0098]

[0099] where D(p) is the separation value of point p in the laser point cloud data. When the separation value D of point p in the laser point cloud data exceeds a preset separation threshold, then point p in the laser point cloud data is a ground point. N is the number of points in the laser point cloud data, a1 is the first adjustment factor of the separation model, b1 is the second adjustment factor of the separation model, z i is the height feature vector of the i - th point p i in the laser point cloud data, c′ is the third adjustment factor of the separation model, d1 is the fourth adjustment factor of the separation model, N neighbors (p i ) is the number of neighborhood points of the i - th point p i in the laser point cloud data, α is the fifth adjustment factor of the separation model, p iis the i-th point in the laser point cloud data, p j is the j-th point in the neighborhood point set, N′(i) is the neighborhood point set of the i-th point p i in the laser point cloud data, and σ is an adjustment factor for controlling the neighborhood influence range.

[0100] The road marking recognition module is used to extract the feature vectors of all points, set up a road marking extraction model, calculate the clustering set of road markings, and recognize the road markings in the laser point cloud data;

[0101] Specifically, the road marking extraction model includes:

[0102]

[0103] Among them, C is the clustering set of road markings, representing the set of the center points of the extracted road markings, c j is the center point of the j-th road marking, F(p i ) is the feature vector of the i-th point p i in the laser point cloud data, λ is the first adjustment factor of the road marking extraction model, M is the number of center points, p′ is the second adjustment factor of the road marking extraction model, μ is the third adjustment factor of the road marking extraction model, d′(c j , c k ) is the distance between the center point c j of the j-th road marking and the center point c k of the k-th road marking, and η is an adjustment factor for controlling the distance influence.

[0104] The vectorization module is used to set up a vectorization model for road markings, vectorize the recognized road markings, and generate the vector values of each road marking;

[0105] Specifically, the vectorization model of the road marking includes:

[0106]

[0107] Among them, V(C) is the vector value of each road marking in the clustering set C of road markings, x i′ is the abscissa of the i′-th node on the road marking, y i′ is the ordinate of the i′-th node on the road marking, β is the first adjustment factor of the vectorization model of the road marking, L i′+1 is the i′ + 1-th node on the road marking, L i′ is the i′-th node on the road marking, p″ is the second adjustment factor of the vectorization model of the road marking, θ is the direction angle of the road marking, and K is the number of nodes on the road marking.

[0108] An optimization module, configured to obtain real road network data, set a road network fusion optimization model, optimize the vector values of road markings, and make the vector values of road markings closest to the real road network data, so as to generate a road vector map.

[0109] Specifically, the road network fusion optimization model includes:

[0110]

[0111] Where M is the number of road markings in the real road network data, R j′ is the vector value of the j'-th road marking in the real road network data, Length(V j′ (C)) is the length of the road marking corresponding to the vector value of the j'-th road marking in the clustering set C of road markings, Curvature(V j′ (C)) is the curvature of the road marking corresponding to the vector value of the j'-th road marking in the clustering set C of road markings, δ is the adjustment factor of the road network fusion optimization model, V j′ (C) is the vector value of the j'-th road marking in the clustering set C of road markings, is the mean value of the vector values of all road markings in the clustering set C of road markings.

[0112] Embodiment 3

[0113] The embodiment of the present invention also proposes a storage medium storing multiple instructions, and the instructions are used to implement the automated road vector map generation method based on laser point cloud data.

[0114] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0115] Optionally, in this embodiment, the storage medium is set to store program code for performing the following steps: Step 101, obtain all points in the laser point cloud data, set a separation model for ground points and non-ground points, calculate the separation value of each point in the laser point cloud data, so as to separate ground points and non-ground points, and retain ground points;

[0116] Specifically, the separation model for ground points and non-ground points includes:

[0117]

[0118] Among them, D(p) is the separation value of point p in the laser point cloud data. When the separation value D of point p in the laser point cloud data exceeds the preset separation threshold, then point p in the laser point cloud data is a ground point. N is the number of points in the laser point cloud data, a1 is the first adjustment factor of the separation model, b1 is the second adjustment factor of the separation model, z i is the height feature vector of the i-th point p i in the laser point cloud data, c′ is the third adjustment factor of the separation model, d1 is the fourth adjustment factor of the separation model, N neighbors (p i ) is the number of neighboring points of the i-th point p i in the laser point cloud data, α is the fifth adjustment factor of the separation model, p i is the i-th point in the laser point cloud data, p j is the j-th point in the neighboring point set, N′(i) is the neighboring point set of the i-th point p i in the laser point cloud data, and σ is the adjustment factor used to control the neighboring influence range.

[0119] Step 102: Extract the feature vectors of all points, set the road marking extraction model, calculate the clustering set of road markings, and identify the road markings in the laser point cloud data;

[0120] Specifically, the road marking extraction model includes:

[0121]

[0122] Among them, C is the clustering set of road markings, representing the set of the center points of the extracted road markings, c j is the center point of the j-th road marking, F(p i ) is the feature vector of the i-th point p i in the laser point cloud data, λ is the first adjustment factor of the road marking extraction model, M is the number of center points, p′ is the second adjustment factor of the road marking extraction model, μ is the third adjustment factor of the road marking extraction model, d′(c j , c k ) is the distance between the center point c j of the j-th road marking and the center point c k of the k-th road marking, and η is the adjustment factor used to control the distance influence.

[0123] Step 103: Set the vectorization model of the road markings, vectorize the identified road markings, and generate the vector values of each road marking;

[0124] Specifically, the vectorization model of the road markings includes:

[0125]

[0126] Among them, V(C) is the vector value of each road marking in the clustering set C of road markings, and x i′ is the abscissa of the i'-th node on the road marking, and y i′ is the ordinate of the i'-th node on the road marking, β is the first adjustment factor of the vectorization model of the road marking, and L i′+1 is the (i'+1)-th node on the road marking, and L i′ is the i'-th node on the road marking, p″ is the second adjustment factor of the vectorization model of the road marking, θ is the direction angle of the road marking, and K is the number of nodes on the road marking.

[0127] Step 104: Obtain real road network data, and set up a road network fusion optimization model to optimize the vector value of the road marking so that the vector value of the road marking is closest to the real road network data, thereby generating a road vector map.

[0128] Specifically, the road network fusion optimization model includes:

[0129]

[0130] Among them, M is the number of road markings in the real road network data, and R j′ is the vector value of the j'-th road marking in the real road network data, Length(V j′ (C)) is the length of the road marking corresponding to the vector value of the j'-th road marking in the clustering set C of road markings, Curvature(V j′ (C)) is the curvature of the road marking corresponding to the vector value of the j'-th road marking in the clustering set C of road markings, δ is the adjustment factor of the road network fusion optimization model, and V j′ (C) is the vector value of the j'-th road marking in the clustering set C of road markings, is the mean value of the vector values of all road markings in the clustering set C of road markings.

[0131] Embodiment 4

[0132] The embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor. The storage medium stores multiple instructions, and the instructions can be loaded and executed by the processor so that the processor can execute the described method for automatically generating a road vector map based on laser point cloud data.

[0133] Specifically, the electronic device in this embodiment can be a computer terminal, and the computer terminal can include: one or more processors and a storage medium.

[0134] Among them, the storage medium can be used to store software programs and modules, such as a method for automatically generating a road vector map based on laser point cloud data in the embodiments of the present invention, and the corresponding program instructions / modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, implements the above-mentioned method for automatically generating a road vector map based on laser point cloud data. The storage medium can include a high-speed random storage medium, and can also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium can further include a storage medium remotely set relative to the processor, and these remote storage media can be connected to the terminal through a network. Examples of the above network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and combinations thereof.

[0135] The processor can call the information and application programs stored in the storage medium through the transmission system to execute the following steps: Step 101, obtain all the points in the laser point cloud data, set the separation model of ground points and non-ground points, calculate the separation value of each point in the laser point cloud data, so as to separate the ground points and non-ground points, and retain the ground points;

[0136] Specifically, the separation model of ground points and non-ground points includes:

[0137]

[0138] Among them, D(p) is the separation value of point p in the laser point cloud data. When the separation value D of point p in the laser point cloud data exceeds the preset separation threshold, then point p in the laser point cloud data is a ground point, N is the number of points in the laser point cloud data, a1 is the first adjustment factor of the separation model, b1 is the second adjustment factor of the separation model, z i is the height feature vector of the i-th point p i in the laser point cloud data, c′ is the third adjustment factor of the separation model, d1 is the fourth adjustment factor of the separation model, N neighbors (p i ) is the number of neighborhood points of the i-th point p i in the laser point cloud data, α is the fifth adjustment factor of the separation model, p i is the i-th point in the laser point cloud data, p j is the j-th point in the neighborhood point set, N′(i) is the neighborhood point set of the i-th point p i in the laser point cloud data, and σ is the adjustment factor used to control the neighborhood influence range.

[0139] Step 102, extract the feature vectors of all the points, set the road marking extraction model, calculate the clustering set of road markings, and identify the road markings in the laser point cloud data;

[0140] Specifically, the road marking extraction model includes:

[0141]

[0142] Among them, C is the clustering set of road markings, representing the set of center points of the extracted road markings, and c j is the center point of the j-th road marking, and F(p i ) is the feature vector of the i-th point p i in the lidar point cloud data, λ is the first adjustment factor of the road marking extraction model, M is the number of center points, p' is the second adjustment factor of the road marking extraction model, μ is the third adjustment factor of the road marking extraction model, and d'(c j , c k ) is the distance between the center point c j of the j-th road marking and the center point c k of the k-th road marking, and η is the adjustment factor for controlling the influence of the distance.

[0143] Step 103: Set the vectorization model of the road markings, vectorize the recognized road markings, and generate the vector value of each road marking;

[0144] Specifically, the vectorization model of the road markings includes:

[0145]

[0146] Among them, V(C) is the vector value of each road marking in the clustering set C of road markings, x i′ is the abscissa of the i'-th node on the road marking, y i′ is the ordinate of the i'-th node on the road marking, β is the first adjustment factor of the vectorization model of the road markings, L i′+1 is the (i'+1)-th node on the road marking, L i′ is the i'-th node on the road marking, p'' is the second adjustment factor of the vectorization model of the road markings, θ is the direction angle of the road marking, and K is the number of nodes on the road marking.

[0147] Step 104: Obtain the real road network data, set the road network fusion and optimization model, optimize the vector value of the road markings, and make the vector value of the road markings closest to the real road network data, so as to generate a road vector map.

[0148] Specifically, the road network fusion and optimization model includes:

[0149]

[0150] Among them, M is the number of road markings in the real road network data, and R j′ is the vector value of the j'-th road marking in the real road network data. Length(V j′ (C)) is the length of the road marking corresponding to the vector value of the j'-th road marking in the clustering set C of road markings. Curvature(V j′ (C)) is the curvature of the road marking corresponding to the vector value of the j'-th road marking in the clustering set C of road markings. δ is the adjustment factor of the road network fusion optimization model. V j′ (C) is the vector value of the j'-th road marking in the clustering set C of road markings, and is the mean value of the vector values of all road markings in the clustering set C of road markings.

[0151] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0152] In the above embodiments of the present invention, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0153] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the displayed or discussed couplings or direct couplings or communication connections between each other can be through some interfaces. The indirect couplings or communication connections of units or modules can be in electrical or other forms.

[0154] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0155] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0156] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, read-only storage media (ROM, Read-Only Memory), random access storage media (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs.

[0157] Obviously, the above embodiments are merely examples for clear illustration and not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation manners here. The obvious changes or modifications derived therefrom still fall within the protection scope of the present invention.

Claims

1. An automated road vector map generation method based on laser point cloud data, characterized in that, Including: Obtain all the points in the laser point cloud data, set the separation model for ground points and non-ground points, calculate the separation value of each point in the laser point cloud data, so as to separate the ground points and non-ground points, and retain the ground points; The separation model for ground points and non-ground points includes: Among them, D(p) is the separation value of point p in the laser point cloud data. When the separation value D of point p in the laser point cloud data exceeds the preset separation threshold, then point p in the laser point cloud data is a ground point. N is the number of points in the laser point cloud data, a1 is the first adjustment factor of the separation model, b1 is the second adjustment factor of the separation model, z i is the height feature vector of the i-th point p i in the laser point cloud data, c′ is the third adjustment factor of the separation model, d1 is the fourth adjustment factor of the separation model, N neighbors (p i ) is the number of neighboring points of the i-th point p i in the laser point cloud data, α is the fifth adjustment factor of the separation model, p i is the i-th point in the laser point cloud data, p j is the j-th point in the neighboring point set, N′(i) is the neighboring point set of the i-th point p i in the laser point cloud data, and σ is the adjustment factor for controlling the influence range of the neighborhood; Extract the feature vectors of all the points, set the road marking extraction model, calculate the clustering set of road markings, and identify the road markings in the laser point cloud data; The road marking extraction model includes: Among them, C is the clustering set of road markings, representing the set of the center points of the extracted road markings, and c j is the center point of the j-th road marking, and F(p i ) is the feature vector of the i-th point p i in the laser point cloud data. λ is the first adjustment factor of the road marking extraction model, M is the number of center points, p' is the second adjustment factor of the road marking extraction model, μ is the third adjustment factor of the road marking extraction model, and d'(c j , c k ) is the distance between the center point c j of the j-th road marking and the center point c k of the k-th road marking. η is the adjustment factor used to control the influence of the distance; Set the vectorization model of road markings, vectorize the identified road markings, and generate the vector value of each road marking; The vectorization model of road markings includes: Among them, V(C) is the vector value of each road marking in the clustering set C of road markings, x i′ is the abscissa of the i'-th node on the road marking, y i′ is the ordinate of the i'-th node on the road marking, β is the first adjustment factor of the vectorization model of the road marking, L i′+1 is the (i'+1)-th node on the road marking, L i′ is the i'-th node on the road marking, p″ is the second adjustment factor of the vectorization model of the road marking, θ is the direction angle of the road marking, and K is the number of nodes on the road marking; Obtain the real road network data, set the road network fusion optimization model, optimize the vector value of road markings, make the vector value of road markings closest to the real road network data, and thus generate the road vector map.

2. The automated road vector map generation method based on laser point cloud data according to claim 1, wherein, The road network fusion optimization model includes: Among them, M′ is the number of road markings in the real road network data, and R j′ is the vector value of the j′-th road marking in the real road network data, and Length(V j′ (C)) is the length of the road marking corresponding to the vector value of the j′-th road marking in the clustering set C of road markings, and Curvature(V j′ (C)) is the curvature of the road marking corresponding to the vector value of the j′-th road marking in the clustering set C of road markings, δ is the adjustment factor of the road network fusion optimization model, and V j′ (C) is the vector value of the j′-th road marking in the clustering set C of road markings, is the mean value of the vector values of all road markings in the clustering set C of road markings.

3. An automated road vector map generation system based on laser point cloud data, characterized in that, Including: Separation module, used to obtain all the points in the laser point cloud data, set the separation model for ground points and non-ground points, calculate the separation value of each point in the laser point cloud data, so as to separate the ground points and non-ground points, and retain the ground points; The separation model for ground points and non-ground points includes: Among them, D(p) is the separation value of point p in the laser point cloud data. When the separation value D of point p in the laser point cloud data exceeds the preset separation threshold, then point p in the laser point cloud data is a ground point. N is the number of points in the laser point cloud data, a1 is the first adjustment factor of the separation model, b1 is the second adjustment factor of the separation model, z i is the height feature vector of the i-th point p i in the laser point cloud data, c′ is the third adjustment factor of the separation model, d1 is the fourth adjustment factor of the separation model, N neighbors (p i ) is the number of neighboring points of the i-th point p i in the laser point cloud data, α is the fifth adjustment factor of the separation model, p i is the i-th point in the laser point cloud data, p j is the j-th point in the neighboring point set, N′(i) is the neighboring point set of the i-th point p i in the laser point cloud data, and σ is the adjustment factor used to control the influence range of the neighborhood; Marking recognition module, used to extract the feature vectors of all the points, set the road marking extraction model, calculate the clustering set of road markings, and identify the road markings in the laser point cloud data; The road marking extraction model includes: Among them, C is the clustering set of road markings, representing the set of the center points of the extracted road markings, and c j is the center point of the j-th road marking, and F(p i ) is the feature vector of the i-th point p i in the lidar point cloud data. λ is the first adjustment factor of the road marking extraction model, M is the number of center points, p' is the second adjustment factor of the road marking extraction model, μ is the third adjustment factor of the road marking extraction model, and d'(c j , c k ) is the distance between the center point c j of the j-th road marking and the center point c k of the k-th road marking. η is an adjustment factor used to control the influence of the distance; Vectorization module, used to set the vectorization model of road markings, vectorize the identified road markings, and generate the vector value of each road marking; The vectorization model of road markings includes: Among them, V(C) is the vector value of each road marking in the clustering set C of road markings, x i′ is the abscissa of the i'-th node on the road marking, y i′ is the ordinate of the i'-th node on the road marking, β is the first adjustment factor of the vectorization model of the road marking, L i′+1 is the (i'+1)-th node on the road marking, L i′ is the i'-th node on the road marking, p″ is the second adjustment factor of the vectorization model of the road marking, θ is the direction angle of the road marking, and K is the number of nodes on the road marking; Optimization module, used to obtain the real road network data, set the road network fusion optimization model, optimize the vector value of road markings, make the vector value of road markings closest to the real road network data, and thus generate the road vector map.

4. The automated road vector map generation system based on laser point cloud data according to claim 3, characterized in that, The road network fusion optimization model includes: Among them, M′ is the number of road markings in the real road network data, and R j′ is the vector value of the j′-th road marking in the real road network data, and Length(V j′ (C)) is the length of the road marking corresponding to the vector value of the j′-th road marking in the clustering set C of road markings, and Curvature(V j′ (C)) is the curvature of the road marking corresponding to the vector value of the j′-th road marking in the clustering set C of road markings. δ is the adjustment factor of the road network fusion optimization model, and V j′ (C) is the vector value of the j′-th road marking in the clustering set C of road markings, and is the mean value of the vector values of all road markings in the clustering set C of road markings.

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