High-precision map construction method and system based on laser point cloud and live-action three dimensions

Abstract

The invention discloses a high-precision map construction method and system based on laser point cloud and live-action three dimensions. The method comprises the following steps: acquiring laser point cloud data and live-action three-dimensional image data; obtaining total element topographic data based on the laser point cloud data and the live-action three-dimensional image data; and reading corresponding element data from the total element topographic data according to a preset layer logic to construct a high-precision map, or reading corresponding live-action semantic data to construct a road component-level live-action three-dimensional model according to the live-action semantic data. According to the method, the first database and the second database are independently constructed, data from different sources can be managed in a unified mode, when data processing is conducted, data matching and loading can be accurately conducted through the association logic between the first database and the second database, and the data processing efficiency is improved. A high-precision map can be quickly constructed, or a road part-level real scene three-dimensional model can be independently constructed according to specific requirements.

Description

Technical Field

[0001] The present invention belongs to the field of map data processing, and particularly relates to a high-precision map construction method and system based on laser point cloud and real scene three-dimensional Background Art

[0002] In the field of autonomous driving with the integrated development of vehicle-road-cloud, high-precision maps are undoubtedly a very important part. Due to the huge amount of high-precision map data, and the data collected by different acquisition devices and different acquisition methods cannot be well fused, when processing data, if unit source data is used, it may lead to low data accuracy and precision. And if multi-source data needs to be fused, it needs to be separately loaded by different software and then fused, resulting in high processing complexity. In addition, for scenarios with high requirements for real-time update, the existing technology needs to separately load and update each data source, making the update efficiency slow. Summary of the Invention

[0003] In order to solve the above problems existing in the prior art, the present invention provides a high-precision map construction method and system based on laser point cloud and real scene three-dimensional. The technical problems to be solved by the present invention are realized through the following technical solutions: A high-precision map construction method based on laser point cloud and real scene three-dimensional, comprising: Obtaining laser point cloud data and real scene three-dimensional image data; Obtaining all-element terrain data based on the laser point cloud data and the real scene three-dimensional image data; Reading corresponding element data from the all-element terrain data according to a preset layer logic to construct a high-precision map and a road component-level real scene three-dimensional model.

[0004] In a specific embodiment, obtaining laser point cloud data and real scene three-dimensional image data includes: Constructing a first database and a second database; In response to receiving the original laser point cloud data sent by a number of point cloud data sources, extracting the point cloud core data of the original laser point cloud data in each point cloud data source, classifying the core data of each point cloud data source and storing it in the first database as laser point cloud data, and the first database includes a laser point cloud data index; In response to receiving the original real scene three-dimensional image data sent by a number of real scene data sources, extracting the real scene core data of the original real scene three-dimensional image data in each real scene data source, classifying the real scene core data of each real scene data source and storing it in the second database as real scene three-dimensional image data, and the second database includes a real scene three-dimensional image data index; Among them, the first database and the second database have a preset association relationship, and the preset association relationship is determined according to the laser point cloud data and the real scene three-dimensional image data.

[0005] In a specific embodiment, the point cloud core data includes point cloud coordinate data, point cloud RGB data, and point cloud classification data; the real scene core data includes real scene coordinate data, real scene texture data, and real scene semantic data; the real scene semantic data has a real scene coding field, a real scene attribute field, a first real scene association field, and a second real scene association field; Correspondingly, after classifying the real scene core data of each real scene data source, it is stored in the second database as real scene three-dimensional image data, including: Read the real scene coordinate data; When it is determined that the real scene coordinate data and the coordinate data in other real scene data sources are in an inclusion relationship, the real scene coding field and the inclusion status of the other real scene data source are stored in the first real scene association field; or, when it is determined that the real scene coordinate data and the coordinate data in other real scene data sources are in an intersection relationship, it is judged whether the intersection area between the real scene coordinate data and the coordinate data in the other real scene data source is greater than a first threshold. If so, the real scene coding field and the intersection status of the other real scene data source are stored in the first real scene association field; When it is determined that the real scene coordinate data and the point cloud coordinate data are in an inclusion relationship, the point cloud classification data and the inclusion status are stored in the second real scene association field; or, when it is determined that the real scene coordinate data and the point cloud coordinate data are in an intersection relationship, it is judged whether the intersection area between the real scene coordinate data and the point cloud coordinate data is greater than a first threshold. If so, the real scene coding field and the intersection status of the other real scene data source are stored in the second real scene association field.

[0006] In a specific embodiment, obtaining the full-element terrain data based on the laser point cloud data and the real scene three-dimensional image data includes: Read the real scene core data from the second database; Classify the real scene core data to obtain full-element grid data, where the full-element data includes point element data, line element data, surface element data, and volume element data; Read the point cloud core data from the first database to generate full-element elevation data corresponding to the full-element grid data through the preset association relationship between the first database and the second database; Overlay the full-element grid data and the full-element elevation data to obtain the full-element terrain data.

[0007] In a specific embodiment, reading corresponding element data from the full-element terrain data according to the preset layer logic to construct a high-precision map includes: Obtain the topological relationships between layers, within layers, and between features of the high-precision map according to the preset layer logic; Extract the corresponding all-feature data from the first database and the second database in sequence according to the topological relationships between layers, within layers, and between features to construct a high-precision map, or read the corresponding real-scene semantic data to construct a road component-level real-scene three-dimensional model based on the real-scene semantic data.

[0008] The present invention also provides a high-precision map construction system based on laser point cloud and real-scene three dimensions, including: A data acquisition module for acquiring laser point cloud data and real-scene three-dimensional image data; An all-feature terrain data generation module for obtaining all-feature terrain data based on the laser point cloud data and the real-scene three-dimensional image data; A map model construction module for reading the corresponding feature data from the all-feature terrain data according to the preset layer logic to construct a high-precision map and a road component-level real-scene three-dimensional model.

[0009] In a specific embodiment, the data acquisition module includes: A database construction unit for constructing a first database and a second database; A laser point cloud data processing unit for, in response to receiving the original laser point cloud data sent by several point cloud data sources, extracting the point cloud core data of the original laser point cloud data in each point cloud data source, classifying the core data of each point cloud data source and storing it in the first database as laser point cloud data, and the first database includes a laser point cloud data index; A real-scene three-dimensional image data processing unit for, in response to receiving the original real-scene three-dimensional image data sent by several real-scene data sources, extracting the real-scene core data of the original real-scene three-dimensional image data in each real-scene data source, classifying the real-scene core data of each real-scene data source and storing it in the second database as real-scene three-dimensional image data, and the second database includes a real-scene three-dimensional image data index; Wherein, the first database and the second database have a preset association relationship, and the preset association relationship is determined according to the laser point cloud data and the real-scene three-dimensional image data.

[0010] In a specific embodiment, the point cloud core data includes point cloud coordinate data, point cloud RGB data, and point cloud classification data; the real-scene core data includes real-scene coordinate data, real-scene texture data, and real-scene semantic data; the real-scene semantic data has a real-scene coding field, a real-scene attribute field, a first real-scene association field, and a second real-scene association field; The real-scene three-dimensional image data processing unit specifically further includes: A real - scene coordinate reading sub - unit for reading real - scene coordinate data; A first real - scene association field obtaining sub - unit. When it is determined that the real - scene coordinate data and the coordinate data in other real - scene data sources are in an inclusion relationship, the real - scene coding field and the inclusion status of this other real - scene data source are stored in the first real - scene association field; or, when it is determined that the real - scene coordinate data and the coordinate data in other real - scene data sources are in an intersection relationship, it is judged whether the intersection area between the real - scene coordinate data and the coordinate data in this other real - scene data source is greater than a first threshold. If so, the real - scene coding field and the intersection status of this other real - scene data source are stored in the first real - scene association field; A second real - scene association field obtaining sub - unit. When it is determined that the real - scene coordinate data and the point - cloud coordinate data are in an inclusion relationship, the point - cloud classification data and the inclusion status are stored in the second real - scene association field; or, when it is determined that the real - scene coordinate data and the point - cloud coordinate data are in an intersection relationship, it is judged whether the intersection area between the real - scene coordinate data and the point - cloud coordinate data is greater than a first threshold. If so, the real - scene coding field and the intersection status of this other real - scene data source are stored in the second real - scene association field.

[0011] In a specific embodiment, the all - element terrain data generation module includes: A real - scene core data reading unit for reading real - scene core data from the second database; An all - element grid data processing unit for classifying the real - scene core data to obtain all - element grid data, where the all - element data includes point - element data, line - element data, surface - element data, and volume - element data; An all - element elevation data processing unit for reading point - cloud core data from the first database to generate all - element elevation data corresponding to the all - element grid data through a preset association relationship between the first database and the second database; An all - element terrain data generation unit for superimposing the all - element grid data and the all - element elevation data to obtain all - element terrain data.

[0012] In a specific embodiment, the map model construction module includes: A topological relationship determination unit for obtaining the inter - layer topological relationship, intra - layer topological relationship, and element topological relationship of the high - precision map according to the preset layer logic; A map model construction unit for successively extracting corresponding all - element data from the first database and the second database according to the inter - layer topological relationship, intra - layer topological relationship, and element topological relationship to construct a high - precision map, or reading corresponding real - scene semantic data to construct a road component - level real - scene three - dimensional model according to the real - scene semantic data.

[0013] Advantages of the present invention: The high-precision map construction method based on laser point cloud and real scene three-dimensional of the present invention can uniformly manage data from different sources by separately constructing the first database and the second database. When processing data, accurate data matching and loading can be carried out through the association logic between the first database and the second database, so that a high-precision map can be quickly constructed or a real scene three-dimensional model at the road component level can be independently constructed for specific requirements.

[0014] The present invention will be further described in detail below with reference to the drawings and embodiments. Description of the drawings

[0015] Figure 1 is a schematic flow chart of a high-precision map construction method based on laser point cloud and real scene three-dimensional provided by an embodiment of the present invention; Figure 2 is a schematic block diagram of a high-precision map construction system based on laser point cloud and real scene three-dimensional provided by an embodiment of the present invention; Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0016] The present invention will be further described in detail below with reference to specific embodiments, but the implementation manners of the present invention are not limited thereto. Embodiment

[0017] Please refer to Figure 1 , Figure 1 which is a schematic flow chart of a high-precision map construction method based on laser point cloud and real scene three-dimensional provided by an embodiment of the present invention, and includes: S1. Obtain laser point cloud data and real scene three-dimensional image data; specifically, the laser point cloud data is mainly collected by vehicle-mounted lidar and airborne lidar, and the real scene three-dimensional image data is mainly collected by multi-angle cameras.

[0018] Since the data volume of the laser point cloud data and the real scene three-dimensional image data is huge, if all data is obtained by real-time acquisition, it will consume a large amount of manpower, material resources and time costs. Therefore, the laser point cloud data and the real scene three-dimensional image data in this embodiment can be obtained from a wide range of sources, such as including real-time acquisition data and different specifications of data collected from existing databases. In order to facilitate the storage of laser point cloud data and real scene three-dimensional image data from different sources for subsequent processing, this step can be specifically implemented through the following steps: S11. Construct the first database and the second database; S12. In response to receiving the original laser point cloud data sent by a number of point cloud data sources, extract the point cloud core data of the original laser point cloud data in each point cloud data source, classify the core data of each point cloud data source, and store it in the first database as laser point cloud data. The first database includes a laser point cloud data index. It should be noted that since the sources of point cloud data are extensive and the data content sent by each point cloud data source will be different, in this embodiment, only the point cloud core data is extracted, classified, and stored uniformly. To facilitate database access, a laser point cloud data index is established.

[0019] S13. In response to receiving the original real-scene three-dimensional image data sent by a number of real-scene data sources, extract the real-scene core data of the original real-scene three-dimensional image data in each real-scene data source, classify the real-scene core data of each real-scene data source, and store it in the second database as real-scene three-dimensional image data. The second database includes a real-scene three-dimensional image data index. Among them, the first database and the second database have a preset association relationship, and the preset association relationship is determined according to the laser point cloud data and the real-scene three-dimensional image data. When constructing a high-precision map, it is necessary to combine the laser point cloud data and the real-scene three-dimensional image data. Therefore, when storing the first database and the second database in this embodiment, it is necessary to confirm the association relationship, and through data binding in the database stage, it is possible to avoid data loss and display confusion caused by data superposition errors during the subsequent model superposition process, and can greatly reduce the processing time.

[0020] The high-precision map construction method based on laser point cloud and real-scene three-dimensional in this embodiment can uniformly manage data from different sources by separately constructing the first database and the second database. When processing data, through the association logic between the first database and the second database, data matching and loading can be accurately performed, so that a high-precision map can be quickly constructed or a real-scene three-dimensional model at the road component level can be independently constructed for specific requirements.

[0021] In a preferred embodiment, the point cloud core data includes point cloud coordinate data, point cloud RGB data, and point cloud classification data; the real-scene core data includes real-scene coordinate data, real-scene texture data, and real-scene semantic data; the real-scene semantic data has a real-scene coding field, a real-scene attribute field, a first real-scene association field, and a second real-scene association field. Correspondingly, step S13 can specifically include the following steps: S131. Read the real-scene coordinate data. S132. When it is determined that the real-scene coordinate data and the coordinate data in other real-scene data sources are in an inclusion relationship, then store the real-scene coding field and the inclusion status of this other real-scene data source into the first real-scene association field; or, when it is determined that the real-scene coordinate data and the coordinate data in other real-scene data sources are in an intersection relationship, determine whether the intersection area between the real-scene coordinate data and the coordinate data in this other real-scene data source is greater than a first threshold. If so, then store the real-scene coding field and the intersection status of this other real-scene data source into the first real-scene association field; During the high-precision map construction process, the processing of real-scene three-dimensional image data is the most complex, and the processing effect has a greater impact on the map display details. Therefore, it is necessary to ensure the accuracy of data extraction, and at the same time, it is necessary to compare the data from different sources to ensure that the finally selected data has high accuracy. The real-scene coordinate data in this step is the reference coordinate data, and through the comparison and judgment between this reference coordinate data and the coordinate data in other real-scene data sources, the reference coordinate data is corrected. Specifically, if it is determined that the real-scene coordinate data and the coordinate data in other real-scene data sources are in an inclusion relationship, it means that the two are in the same grid. After recording this inclusion relationship, if it is necessary to read the grid data later, all the associated data can be directly obtained through the first real-scene association field, without the need to separately load a certain map. When it is determined that the real-scene coordinate data and the coordinate data in other real-scene data sources are in an intersection relationship, it is considered that there is no direct logical association between the two. For example, there may be unassociated elements between different layers, and no further processing is required. However, due to possible errors between different data sources, data that was originally in an inclusion relationship or the same relationship may be misjudged as an intersection relationship. Therefore, it is necessary to further determine whether the area of the intersection region is much smaller than the area of one of the two data, that is, whether the intersection region is greater than the first threshold. If it is greater than the first threshold, it means that the area of the intersection region is close to the area of one of the two data. Then, combined with the real-scene coding field and the real-scene attribute field, it is judged whether they are consistent, and the more accurate position can be further reconfirmed according to the position of the intersection region. The first threshold in this embodiment can be, for example, 95%.

[0022] S133. When it is determined that the real-scene coordinate data and the point cloud coordinate data are in an inclusion relationship, then store the point cloud classification data and the inclusion status into the second real-scene association field; or, when it is determined that the real-scene coordinate data and the point cloud coordinate data are in an intersection relationship, determine whether the intersection area between the real-scene coordinate data and the point cloud coordinate data is greater than a first threshold. If so, then store the real-scene coding field and the intersection status of this other real-scene data source into the second real-scene association field.

[0023] To establish an association relationship between the first database and the second database, this embodiment performs the association through the second real-scene association field. For the specific implementation method, refer to step S132. Through the second real-scene association field, when loading the real-scene three-dimensional image data, the associated laser point cloud data can be directly called through the database, so as to facilitate local loading and display.

[0024] S2. Obtain the full-element terrain data based on the laser point cloud data and the real-scene three-dimensional image data; S21. Read the real-scene core data from the second database; specifically, it is necessary to read the real-scene coordinate data, real-scene texture data, real-scene semantic data, as well as the real-scene coding field, real-scene attribute field, first real-scene association field, and second real-scene association field in the real-scene semantic data.

[0025] S22. Classify the real-scene core data to obtain the full-element grid data, where the full-element data includes point element data, line element data, surface element data, and volume element data; specifically, the point elements mainly include independent ground features, such as street lights, manhole covers, etc., the line elements mainly include polyline data such as road boundaries, pipeline networks, etc., the surface elements mainly include polygon data such as building outlines, and the volume elements mainly include three-dimensional structures that need to be refined, such as bus stops, waiting halls, underground pipe galleries, etc., which require special processing data.

[0026] S23. Read the point cloud core data from the first database to generate the full-element elevation data corresponding to the full-element grid data through the preset association relationship between the first database and the second database; that is, generate the full-element elevation data corresponding to the full-element grid data through the second real-scene association field. It should be noted that since there may be a lack of elevation data when the point element data, line element data, surface element data, and volume element data in the full-element grid data correspond to the full-element elevation data, at this time, methods such as Kriging interpolation can be used for surface and internal data continuity processing.

[0027] S24. Superimpose the full-element grid data and the full-element elevation data to obtain the full-element terrain data.

[0028] S3. Read the corresponding element data from the full-element terrain data according to the preset layer logic to construct a high-precision map and a road component-level real-scene three-dimensional model. Specifically include: S31. Obtain the inter-layer topological relationship, intra-layer topological relationship, and element topological relationship of the high-precision map according to the preset layer logic; After constructing the first database and the second database in this embodiment, it is possible to achieve customized high-precision map construction according to different scenario requirements. Therefore, it is not necessary to generate according to the standardized mode in existing software tools. For the organizational structure of the layer logic, it is generally "map - area - sheet - layer group - layer - feature". The topological relationships within the layer mainly include the association relationships between layers, and the feature topological relationships mainly include the connection relationships and boundary relationships between point feature data, line feature data, surface feature data, and volume feature data.

[0029] S32. Extract the corresponding complete feature data from the first database and the second database in sequence according to the topological relationships between layers, within layers, and feature topological relationships to construct a high-precision map, or read the corresponding real-scene semantic data to construct a road component-level real-scene three-dimensional model based on the real-scene semantic data. It should be noted that since the complete feature data in this implementation is extracted through the first database and the second database, there is no need to import and load through multi-terminal interfaces, and a high-precision map can be directly constructed. It should be noted that for scenarios with relatively low global usage requirements but relatively high local usage requirements, the solution in this implementation can directly construct a road component-level real-scene three-dimensional model or construct a road component-level real-scene three-dimensional model on the already loaded high-precision map without global reloading. At this time, only the corresponding real-scene semantic data needs to be read separately, and the preset layer logic can be adjusted according to the logic of the real-scene semantic data to achieve separate loading according to specific requirements, thereby saving processing resources and improving data processing efficiency. Specifically, the road component-level real-scene three-dimensional model includes a road network layer, a lane network layer, a road marking layer, and a road facility layer.

[0030] In a specific scenario, the high-precision map can be used for the autonomous driving of intelligent connected vehicles. The road component-level real-scene three-dimensional model can be automatically produced based on the road complete feature topographic map, can be used as the data base of the cloud control platform, can also be used as a visual three-dimensional map for the vehicle end, or can be used for urban traffic simulation experiments to assist in various application scenarios such as urban traffic planning.

[0031] Please continue to refer to Figure 2 , this embodiment also provides a high-precision map construction system based on laser point cloud and real-scene three-dimensional, including: A data acquisition module, used to acquire laser point cloud data and real-scene three-dimensional image data; A complete feature terrain data generation module, used to obtain complete feature terrain data based on the laser point cloud data and the real-scene three-dimensional image data; A map model construction module, used to read the corresponding feature data from the complete feature terrain data according to the preset layer logic to construct a high-precision map and a road component-level real-scene three-dimensional model.

[0032] In a specific embodiment, the data acquisition module includes: A database construction unit for constructing a first database and a second database; A laser point cloud data processing unit, configured to, in response to receiving raw laser point cloud data sent by a plurality of point cloud data sources, extract the point cloud core data of the raw laser point cloud data in each point cloud data source, classify the core data of each point cloud data source, and store the classified data in the first database as laser point cloud data. The first database includes a laser point cloud data index; A real scene three-dimensional image data processing unit, configured to, in response to receiving raw real scene three-dimensional image data sent by a plurality of real scene data sources, extract the real scene core data of the raw real scene three-dimensional image data in each real scene data source, classify the real scene core data of each real scene data source, and store the classified data in the second database as real scene three-dimensional image data. The second database includes a real scene three-dimensional image data index; Wherein, the first database and the second database have a preset association relationship, and the preset association relationship is determined according to the laser point cloud data and the real scene three-dimensional image data.

[0033] In a specific embodiment, the point cloud core data includes point cloud coordinate data, point cloud RGB data, and point cloud classification data; the real scene core data includes real scene coordinate data, real scene texture data, and real scene semantic data; the real scene semantic data has a real scene coding field, a real scene attribute field, a first real scene association field, and a second real scene association field; The real scene three-dimensional image data processing unit further specifically includes: A real scene coordinate reading sub-unit for reading real scene coordinate data; A first real scene association field acquisition sub-unit, configured to, when it is determined that the real scene coordinate data and the coordinate data in other real scene data sources are in an inclusion relationship, store the real scene coding field and the inclusion status of the other real scene data source in the first real scene association field; or, when it is determined that the real scene coordinate data and the coordinate data in other real scene data sources are in an intersection relationship, determine whether the intersection area between the real scene coordinate data and the coordinate data in the other real scene data sources is greater than a first threshold. If so, store the real scene coding field and the intersection status of the other real scene data source in the first real scene association field; A second real scene association field acquisition sub-unit, configured to, when it is determined that the real scene coordinate data and the point cloud coordinate data are in an inclusion relationship, store the point cloud classification data and the inclusion status in the second real scene association field; or, when it is determined that the real scene coordinate data and the point cloud coordinate data are in an intersection relationship, determine whether the intersection area between the real scene coordinate data and the point cloud coordinate data is greater than a first threshold. If so, store the real scene coding field and the intersection status of the other real scene data source in the second real scene association field.

[0034] In a specific embodiment, the all-element terrain data generation module includes: A real-scene core data reading unit for reading real-scene core data from the second database; An all-element grid data processing unit for classifying the real-scene core data to obtain all-element grid data, where the all-element data includes point element data, line element data, surface element data, and volume element data; An all-element elevation data processing unit for reading point cloud core data from the first database to generate all-element elevation data corresponding to the all-element grid data through a preset association relationship between the first database and the second database; An all-element terrain data generation unit for superimposing the all-element grid data and the all-element elevation data to obtain all-element terrain data.

[0035] In a specific embodiment, the map model construction module includes: A topology relationship determination unit for obtaining the inter-layer topology relationship, intra-layer topology relationship, and element topology relationship of the high-precision map according to the preset layer logic; A map model construction unit for sequentially extracting corresponding all-element data from the first database and the second database according to the inter-layer topology relationship, intra-layer topology relationship, and element topology relationship to construct a high-precision map, or reading corresponding real-scene semantic data to construct a road component-level real-scene three-dimensional model according to the real-scene semantic data.

[0036] An embodiment of the present invention also provides an electronic device, as Figure 3 shown, including a processor 31, a communication interface 32, a memory 33, and a communication bus 34, where the processor 31, the communication interface 32, and the memory 33 complete communication with each other through the communication bus 34, The memory 33 is used to store a computer program; When the processor 31 executes the program stored in the memory 33, the following steps are implemented: Obtain laser point cloud data and real-scene three-dimensional image data; Obtain all-element terrain data based on the laser point cloud data and the real-scene three-dimensional image data; Read corresponding element data from the all-element terrain data according to the preset layer logic to construct a high-precision map, or read corresponding real-scene semantic data to construct a road component-level real-scene three-dimensional model according to the real-scene semantic data.

[0037] The communication bus mentioned in the above electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience in representation, only a thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0038] The communication interface is used for communication between the above electronic device and other devices.

[0039] The memory may include a Random Access Memory (RAM), and may also include a Non-Volatile Memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located far from the aforementioned processor.

[0040] The above-mentioned processor may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0041] The method provided by the embodiments of the present invention can be applied to an electronic device. Specifically, the electronic device may be: a desktop computer, a portable computer, a smart mobile terminal, a server, etc. There is no limitation here. Any electronic device that can implement the present invention belongs to the protection scope of the present invention.

[0042] The terminal device applying the embodiments of the present invention may exist in various forms, including but not limited to: (1) Mobile communication devices: The characteristic of this type of device is that it has mobile communication functions and mainly aims to provide voice and data communication. This type of terminal includes: smart phones (such as iPhone), multimedia phones, functional phones, and low-end phones, etc.

[0043] (2) Ultra-mobile personal computer devices: These devices belong to the category of personal computers, have computing and processing functions, and generally also have the feature of mobile Internet access. Such terminals include: PDA, MID, and UMPC devices, etc., such as iPad.

[0044] (3) Portable entertainment devices: These devices can display and play multimedia content. Such devices include: audio and video players (such as iPod), handheld game consoles, e-books, as well as smart toys and portable vehicle navigation devices.

[0045] (4) Other electronic devices with data interaction functions.

[0046] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0047] In the description of this specification, descriptions with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc., mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification.

[0048] Although the present application has been described in connection with various embodiments herein, however, in the process of implementing the claimed present application, those skilled in the art can understand and achieve other variations of the disclosed embodiments by viewing the accompanying drawings, the disclosure content, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "one" does not exclude a plurality of cases. A single processor or other unit can implement several functions recited in the claims. Certain measures are recited in mutually different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0049] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, an apparatus (device), or a computer program product. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects, which are collectively referred to herein as "modules" or "systems". Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code. The computer program is stored / distributed in a suitable medium, provided together with other hardware or as part of the hardware, and can also be in other distribution forms, such as via the Internet or other wired or wireless telecommunication systems.

[0050] The present application is described with reference to the flowcharts and / or block diagrams of the methods, apparatuses (devices), and computer program products of the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or multiple flows and / or blocks

[0051] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one or more of the flows Figure 1 or multiple flows and / or blocks

[0052] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or multiple flows and / or blocks

[0053] The above content is a further detailed description of the present invention in combination with specific preferred embodiments. It cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention pertains, without departing from the concept of the present invention, several simple deductions or substitutions can still be made, which should all be regarded as falling within the protection scope of the present invention.

Claims

1. A method for constructing a high-precision map based on laser point cloud and real-scene three-dimensional, characterized in that: include: Obtain laser point cloud data and real-scene 3D image data; Obtaining full-factor terrain data based on the laser point cloud data and the real-scene three-dimensional image data; According to the preset layer logic, the corresponding element data is read from the all-element terrain data to construct a high-precision map and a road component-level real-scene three-dimensional model.

2. The method for constructing a high-precision map based on laser point cloud and real scene three-dimensional according to claim 1, characterized in that: Obtaining laser point cloud data and real-scene 3D image data includes: constructing a first database and a second database; In response to receiving raw laser point cloud data sent by a plurality of point cloud data sources, extracting point cloud core data of the raw laser point cloud data in each point cloud data source, classifying the core data of each point cloud data source and storing them in a first database as laser point cloud data, wherein the first database includes a laser point cloud data index; In response to receiving original real-scene 3D image data sent by a plurality of real-scene data sources, extracting real-scene core data of the original real-scene 3D image data in each real-scene data source, classifying the real-scene core data of each real-scene data source and storing them in a second database as real-scene 3D image data, wherein the second database includes a real-scene 3D image data index; The first database and the second database have a preset association relationship, and the preset association relationship is determined according to the laser point cloud data and the real-scene three-dimensional image data.

3. The method for constructing a high-precision map based on laser point cloud and real scene three-dimensional according to claim 2, characterized in that: The point cloud core data includes point cloud coordinate data, point cloud RGB data and point cloud classification data; the real scene core data includes real scene coordinate data, real scene texture data and real scene semantic data; the real scene semantic data has a real scene coding field, a real scene attribute field, a first real scene association field and a second real scene association field; Accordingly, the real scene core data of each real scene data source is classified and stored in the second database as real scene three-dimensional image data, including: Read real scene coordinate data; When it is determined that the real-scene coordinate data and the coordinate data in other real-scene data sources are in an inclusion relationship, the real-scene coding field and the inclusion status of the other real-scene data source are stored in the first real-scene associated field; or, when it is determined that the real-scene coordinate data and the coordinate data in other real-scene data sources are in an intersection relationship, it is determined whether the intersection area between the real-scene coordinate data and the coordinate data in the other real-scene data source is greater than a first threshold, and if so, the real-scene coding field and the intersection status of the other real-scene data source are stored in the first real-scene associated field; When it is determined that the real-scene coordinate data and the point cloud coordinate data are in an inclusion relationship, the point cloud classification data and the inclusion status are stored in the second real-scene associated field; or, when it is determined that the real-scene coordinate data and the point cloud coordinate data are in an intersection relationship, it is determined whether the intersection area of ​​the real-scene coordinate data and the point cloud coordinate data is greater than a first threshold. If so, the real-scene coding field and the intersection status of the other real-scene data source are stored in the second real-scene associated field.

4. The method for constructing a high-precision map based on laser point cloud and real scene three-dimensional according to claim 2, characterized in that: Obtaining full-factor terrain data based on the laser point cloud data and the real-scene three-dimensional image data includes: Read the real scene core data from the second database; Classifying the real scene core data to obtain full-factor grid data, wherein the full-factor data includes point factor data, line factor data, surface factor data and volume factor data; Reading point cloud core data from the first database to generate full-factor elevation data corresponding to the full-factor grid data through a preset association relationship between the first database and the second database; The full-element grid data and the full-element elevation data are superimposed to obtain full-element terrain data.

5. The method for constructing a high-precision map based on laser point cloud and real scene three-dimensional according to claim 2, characterized in that: Reading corresponding element data from the full-element terrain data according to the preset layer logic to construct a high-precision map and a road component-level real-scene three-dimensional model, including: According to the preset layer logic, the topological relationship between layers, the topological relationship within layers and the topological relationship of elements of the high-precision map are obtained; According to the topological relationship between layers, the topological relationship within layers and the topological relationship of elements, the corresponding full-factor terrain data are extracted from the first database and the second database in turn to build a high-precision map, or the corresponding real-scene semantic data is read to build a road component-level real-scene three-dimensional model based on the real-scene semantic data.

6. A high-precision map construction system based on laser point cloud and real-scene three-dimensional, characterized in that: include: Data acquisition module, used to acquire laser point cloud data and real-scene three-dimensional image data; A full-factor terrain data generation module, used to obtain full-factor terrain data based on the laser point cloud data and the real-scene three-dimensional image data; The map model construction module is used to read the corresponding element data from the full-element terrain data according to the preset layer logic to construct a high-precision map and a road component-level real-scene three-dimensional model.

7. The high-precision map construction system based on laser point cloud and real scene three-dimensional according to claim 6 is characterized in that: The data acquisition module comprises: A database construction unit, used to construct a first database and a second database; a laser point cloud data processing unit, configured to extract, in response to receiving original laser point cloud data sent by a plurality of point cloud data sources, point cloud core data of the original laser point cloud data in each point cloud data source, and classify the core data of each point cloud data source and store them in a first database as laser point cloud data, wherein the first database includes a laser point cloud data index; a real-scene 3D image data processing unit, configured to extract, in response to receiving original real-scene 3D image data sent by a plurality of real-scene data sources, real-scene core data of the original real-scene 3D image data from each real-scene data source, and classify the real-scene core data of each real-scene data source and store them in a second database as real-scene 3D image data, wherein the second database includes a real-scene 3D image data index; The first database and the second database have a preset association relationship, and the preset association relationship is determined according to the laser point cloud data and the real-scene three-dimensional image data.

8. The high-precision map construction system based on laser point cloud and real scene three-dimensional according to claim 7, characterized in that: The point cloud core data includes point cloud coordinate data, point cloud RGB data and point cloud classification data; the real scene core data includes real scene coordinate data, real scene texture data and real scene semantic data; the real scene semantic data has a real scene coding field, a real scene attribute field, a first real scene association field and a second real scene association field; The real-scene three-dimensional image data processing unit specifically includes: A real scene coordinate reading subunit, used for reading real scene coordinate data; A first real scene associated field acquisition subunit is used to, when it is determined that the real scene coordinate data and the coordinate data in other real scene data sources are in an inclusion relationship, store the real scene coding field and the inclusion status of the other real scene data source into the first real scene associated field; or, when it is determined that the real scene coordinate data and the coordinate data in other real scene data sources are in an intersection relationship, determine whether the intersection area between the real scene coordinate data and the coordinate data in the other real scene data source is greater than a first threshold, and if so, store the real scene coding field and the intersection status of the other real scene data source into the first real scene associated field; The second real-scene associated field acquisition subunit is used to store the point cloud classification data and the inclusion status in the second real-scene associated field when it is determined that the real-scene coordinate data and the point cloud coordinate data are in an inclusion relationship; or, when it is determined that the real-scene coordinate data and the point cloud coordinate data are in an intersection relationship, determine whether the intersection area of ​​the real-scene coordinate data and the point cloud coordinate data is greater than a first threshold, and if so, store the real-scene coding field and the intersection status of the other real-scene data source in the second real-scene associated field.

9. The high-precision map construction system based on laser point cloud and real scene three-dimensional according to claim 7, characterized in that: The full-factor terrain data generation module includes: A real scene core data reading unit, used for reading the real scene core data from the second database; A full-element grid data processing unit, used for classifying the real scene core data to obtain full-element grid data, wherein the full-element data includes point element data, line element data, surface element data and volume element data; A full-factor elevation data processing unit, configured to read point cloud core data from the first database, so as to generate full-factor elevation data corresponding to the full-factor grid data through a preset association relationship between the first database and the second database; The full-factor terrain data generating unit is used to superimpose the full-factor grid data and the full-factor elevation data to obtain the full-factor terrain data.

10. The high-precision map construction system based on laser point cloud and real scene three-dimensional according to claim 7, characterized in that: The map model building module includes: A topological relationship determination unit, used to obtain the inter-layer topological relationship, the intra-layer topological relationship and the element topological relationship of the high-precision map according to the preset layer logic; The map model construction module unit is used to extract the corresponding full-factor data from the first database and the second database in sequence according to the topological relationship between layers, the topological relationship within the layer and the topological relationship of the elements to construct a high-precision map, or to read the corresponding real-scene semantic data to construct a road component-level real-scene three-dimensional model according to the real-scene semantic data.

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