Paving pavement boundary line determination method and device, electronic equipment and storage medium

Abstract

The invention provides a paved pavement boundary line determination method and device, electronic equipment and a computer readable storage medium. The method comprises the following steps: after acquiring preprocessing data of a target paving pavement acquired and processed by each sensor, determining corrected point cloud data according to point cloud, positioning and inertia preprocessing data, determining fusion data according to the corrected point cloud data and image preprocessing data, determining a feature point set according to the fusion data and the image preprocessing data in the preprocessing data, and determining a target paving pavement according to the feature point set. And generating first boundary line data of the target paving pavement based on the feature point set, optimizing the first boundary line data according to the correction point cloud data to obtain optimized boundary line data, and converting the optimized boundary line data according to a preset target format to generate target boundary line information. The boundary line is determined according to the road surface data directly measured by the sensors, the requirements for hardware and operators and the field risk are reduced through the multi-sensor fusion technology, and the measurement precision, efficiency and flexibility are improved.

Description

Technical Field

[0001] The present application relates to the technical field of road pavement engineering measurement, and particularly to a method, device, electronic device and computer-readable storage medium for determining the boundary line of a paved road surface. Background Art

[0002] In existing pavement engineering, an unmanned construction system based on unmanned paving and unmanned compaction has been gradually applied to multiple engineering projects.

[0003] However, the determination of the paving and compaction boundary line still mainly relies on traditional manual RTK (Real-Time Kinematic) layout, installing baffles or directly using the design line data, but this method has low efficiency; the accuracy of the method of indirectly calculating the boundary line by measuring the paving width is easily affected by factors such as the camber of the screed and assembly adjustment, and the flexibility is poor; the paving width detection method based on in-vehicle image acquisition devices still has problems of insufficient accuracy and poor adaptability; the method of obtaining orthophotos by drones and extracting the boundary line has high requirements for hardware and operators, and there are on-site risks and data redundancy problems.

[0004] Therefore, the current methods for determining the boundary line of a paved road surface have technical problems such as low efficiency and accuracy, low flexibility, high requirements for hardware and operations, and on-site risks and data redundancy, and it is difficult to meet the needs of the unmanned construction system and needs to be improved. Summary of the Invention

[0005] The present application provides a method, device, electronic device and computer-readable storage medium for determining the boundary line of a paved road surface, which is used to alleviate the technical problems existing in the current methods for determining the boundary line of a paved road surface, such as low efficiency and accuracy, low flexibility, high requirements for hardware and operations, and on-site risks and data redundancy.

[0006] To solve the above technical problems, the present application provides the following technical solutions: The present application provides a method for determining the boundary line of a paved road surface, including: Obtaining preprocessed data of the target paved road surface collected and processed by each sensor, where the preprocessed data includes point cloud preprocessed data, positioning preprocessed data, inertial preprocessed data, and image preprocessed data; Determining corrected point cloud data according to the point cloud preprocessed data, the positioning preprocessed data, and the inertial preprocessed data; Determining fusion data according to the corrected point cloud data and the image preprocessed data; Determining a feature point set according to the fusion data and the image preprocessed data, and generating first boundary line data of the target paved road surface based on the feature point set; Optimize the first boundary line data according to the corrected point cloud data to obtain optimized boundary line data; Convert the optimized boundary line data according to a preset target format to generate target boundary line information of the target paving road surface.

[0007] Correspondingly, the present application further provides a device for determining the boundary line of a paving road surface, including: A data acquisition module, configured to acquire preprocessed data of the target paving road surface collected and processed by each sensor, where the preprocessed data includes point cloud preprocessed data, positioning preprocessed data, inertial preprocessed data, and image preprocessed data; A point cloud correction module, configured to determine corrected point cloud data according to the point cloud preprocessed data, the positioning preprocessed data, and the inertial preprocessed data; A data fusion module, configured to determine fusion data according to the corrected point cloud data and the image preprocessed data; A feature extraction module, configured to determine a feature point set according to the fusion data and the image preprocessed data, and generate first boundary line data of the target paving road surface based on the feature point set; A boundary line optimization module, configured to optimize the first boundary line data according to the corrected point cloud data to obtain optimized boundary line data; A data format conversion module, configured to convert the optimized boundary line data according to a preset target format to generate target boundary line information of the target paving road surface.

[0008] Meanwhile, the present application provides an electronic device, which includes a processor and a memory. The memory is used to store a computer program, and the processor is used to run the computer program in the memory to execute the steps in the above method for determining the boundary line of a paving road surface.

[0009] In addition, the present application further provides a computer-readable storage medium, which stores multiple instructions, and the instructions are suitable for being loaded by a processor to execute the steps in the above method for determining the boundary line of a paving road surface.

[0010] The present application provides a method, device, electronic device and computer-readable storage medium for determining the boundary line of a paved road surface. Specifically, the method first obtains the preprocessed data of the target paved road surface collected and processed by each sensor. The preprocessed data includes point cloud preprocessed data, positioning preprocessed data, inertial preprocessed data, and image preprocessed data. Then, based on the point cloud preprocessed data, positioning preprocessed data, and inertial preprocessed data, corrected point cloud data is determined. Based on the corrected point cloud data and image preprocessed data, fused data is determined. Then, based on the fused data and image preprocessed data, a set of feature points is determined, and the first boundary line data of the target paved road surface is generated based on the set of feature points. Next, the first boundary line data is optimized according to the corrected point cloud data to obtain optimized boundary line data. Finally, the optimized boundary line data is converted according to a preset target format to generate target boundary line information. This method measures and collects the road surface data of the target paved road surface through each sensor, and then determines the boundary line based on these data. This multi-sensor fusion technology reduces the requirements for hardware and operators and on-site risks, and the direct measurement and data fusion methods improve the measurement accuracy, efficiency, and flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings. Apparently, the described embodiments are only a part of the embodiments of the present application, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0012] Figure 1 FIG. is the system architecture diagram of the paving road surface boundary line determination system provided by the embodiment of the present application.

[0013] Figure 2 FIG. is the flowchart of the paving road surface boundary line determination method provided by the embodiment of the present application.

[0014] Figure 3 FIG. is the scenario diagram of the paving road surface boundary line determination method provided by the embodiment of the present application.

[0015] Figure 4 FIG. is the structural diagram of the paving road surface boundary line determination device provided by the embodiment of the present application.

[0016] Figure 5 FIG. is the structural diagram of the electronic device provided by the embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are only a part of the embodiments of the present application, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0018] The terms "including" and "having" and any variations thereof in the description and claims of this application are intended to cover non-exclusive inclusion; the division of modules in this application is only a logical division, and in actual implementation, there may be other division methods. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0019] This application provides a method, device, electronic device, and computer-readable storage medium for determining the boundary line of a paved road surface. Among them, the device for determining the boundary line of a paved road surface can be integrated into an electronic device, and the electronic device can be a handheld terminal or a terminal mounted on a mobile trolley, etc.

[0020] Please refer to Figure 1 , Figure 1 which is the system architecture diagram of the system for determining the boundary line of a paved road surface provided by the embodiments of this application. As Figure 1 shown, the system can include terminals and devices. The terminals, devices, and between the terminals and devices are connected and communicate through the Internet composed of various gateways. Among them, the system at least includes a sensor terminal 101, a server 102, and an automatic construction device 103: The sensor terminal 101 refers to a SLAM three-dimensional scanning terminal composed of at least one sensor. The sensor terminal 101 can include a radar module (such as a 360° lidar), a positioning module (such as a GNSS positioning module), an inertial measurement module (such as an inertial navigation unit), and an image acquisition module (such as a panoramic camera). The sensor terminal 101 is mainly used to collect various road surface data of the target paved road surface. For example, the radar module mainly collects point cloud data, the positioning module mainly collects positioning data, the inertial measurement module mainly collects inertial data, and the image acquisition module mainly collects image data.

[0021] The server 102 can be an independent server or a server network or server cluster composed of servers; for example, the server described in this application includes, but is not limited to, a computer, a network host, a database server, and an application server, or a cloud server composed of multiple servers, where the cloud server is composed of a large number of computers or network servers based on cloud computing (CloudComputing). A data processing module can be integrated in the server 102 to perform preprocessing, data fusion, coordinate transformation, data optimization, format conversion, and other processing on various data collected by the sensor terminal.

[0022] The automatic construction device 103 refers to an unmanned device for implementing road surface paving. The automatic construction device 103 mainly includes an unmanned paver and an unmanned roller.

[0023] A communication link is provided between the sensor terminal 101, the server 102, and the automatic construction equipment 103 to enable information interaction. The type of the communication link may include a wired or wireless communication link, an optical fiber cable, etc., which is not limited in this application. Among them: The sensor terminal 101 collects various types of road surface data of the target paving road surface through each sensor therein, mainly including point cloud data, positioning data, inertial data, and image data, and transmits these data to the server 102 for preprocessing to obtain preprocessed data corresponding to various types of road surface data. The preprocessed data mainly includes point cloud preprocessed data, positioning preprocessed data, inertial preprocessed data, and image preprocessed data. Then, the server 102 determines the corrected point cloud data according to the point cloud preprocessed data, the positioning preprocessed data, and the inertial preprocessed data, and then determines the fused data according to the corrected point cloud data and the image preprocessed data. Next, the server 102 determines the feature point set according to the fused data and the image preprocessed data, and generates the first boundary line data of the target paving road surface based on the feature point set. Then, the server 102 optimizes the first boundary line data according to the corrected point cloud data to obtain the optimized boundary line data. The server 102 converts the optimized boundary line data according to a preset target format to generate the target boundary line information. Finally, the server 102 sends the generated target boundary line information to the automatic construction equipment 103, and the automatic construction equipment 103 performs construction according to the received target boundary line information.

[0024] Optionally, the sensor terminal 101 and the server 102 may be integrated in the same device or equipment. The surveyor can hold the device or equipment to measure the target paving road surface, or install the device or equipment on an automatic driving trolley to achieve automatic measurement of the target paving road surface. The device or equipment can be combined with the automatic construction equipment into a road surface unmanned construction system to form a complete process unmanned / less manned construction system.

[0025] In the above process of determining the paving road surface boundary line, the road surface data of the target paving road surface is measured and collected through each sensor in the sensor terminal 101, and the server 102 determines the boundary line according to these data. The server 102 sends the target boundary line information to the construction equipment 103 to achieve the leading function, replacing the surveyor to collect the actual paving boundary line in the early stage of construction, providing reliable navigation data for the unmanned construction system. This multi-sensor fusion technology reduces the requirements for hardware and operators and the on-site risks, and the direct measurement and data fusion method improves the measurement accuracy, efficiency, and flexibility.

[0026] It should be noted that Figure 1The system architecture diagram shown is only an example. The terminals, devices, and scenarios described in the embodiments of this application are used to more clearly illustrate the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those of ordinary skill in the art can know that with the evolution of the system and the emergence of new business scenarios, the technical solutions provided by the embodiments of this application are equally applicable to similar technical problems. The following will be described in detail respectively. It should be noted that the description order of the following embodiments does not limit the preferred order of the embodiments.

[0027] In the embodiments of this application, please refer to Figure 2 as shown Figure 2 is a schematic flowchart of the method for determining the boundary line of the paved road surface provided by the embodiments of this application. This method at least includes the following steps: S201: Obtain the preprocessed data of the target paved road surface collected and processed by each sensor. The preprocessed data includes point cloud preprocessed data, positioning preprocessed data, inertial preprocessed data, and image preprocessed data.

[0028] In the embodiments of this application, each sensor may include a radar module (such as a 360° lidar), a positioning module (such as a GNSS positioning module), an inertial measurement module (such as an inertial navigation unit), and an image acquisition module (such as a panoramic camera). Among them, the radar module can collect local point cloud data of the target paved road surface, the positioning module can collect positioning data of the target paved road surface, the inertial measurement module can collect inertial data of the target paved road surface, and the image acquisition module can collect image data of the target paved road surface.

[0029] It should be noted that the radar module is applicable to most road construction scenarios, can collect complete three-dimensional information of the equipment surrounding environment, three-dimensional geometric information of the road surface (including the boundary line) (such as curb stones, steel formworks, etc.), and can accurately describe the fine features of the road surface and the boundary (such as the edge of the curb stone, the corner of the steel formwork, etc.), to ensure the comprehensiveness and continuity of the boundary line generation and high boundary line generation accuracy; the positioning module can obtain RTK coordinates with centimeter-level accuracy in real time, realize high-precision real-time positioning, is applicable to dynamic construction environments, and provides high-precision global positioning information for the SLAM (Simultaneous Localization and Mapping) algorithm, ensuring the accuracy of the boundary line generation; the inertial measurement module supports real-time data collection and processing, adapts to dynamic construction environments, and this module is mainly used to correct the attitude deviation of the equipment, ensure the accuracy of sensor data, and provide high-precision and high-reliability attitude and motion state information for the unmanned construction system, ensuring the accuracy of the boundary line generation; the image acquisition module supports real-time video stream and static image acquisition, can capture visually rich detailed information, provides high-precision visual feature points, and assists in the generation of the boundary line.

[0030] In one embodiment, step S201 includes: performing filtering and denoising on the local point cloud data to obtain preprocessed point cloud data; performing calibration on the positioning data to obtain preprocessed positioning data; performing calibration on the inertial navigation data based on the preprocessed positioning data to obtain preprocessed inertial data; and performing distortion correction on the image data to obtain preprocessed image data.

[0031] In the embodiment of the present application, the radar module first collects local point cloud data (including three-dimensional coordinate information) of the target paving road surface, and then performs preprocessing operations such as filtering, denoising, and feature point extraction (such as curb stones, steel formworks, etc.) on it, removing noise and non-view points (such as vehicles, pedestrians, etc.) in the point cloud data to obtain preprocessed point cloud data, thereby improving the quality of the point cloud data. The positioning module first collects positioning data (including pseudorange, carrier phase, satellite status, etc.) of the device where the module is located, and then performs preprocessing operations such as data verification, error correction, differential correction, data filtering, and coordinate transformation on the collected positioning data to obtain preprocessed positioning data; among them, data verification is mainly to eliminate invalid or low-quality positioning data; error correction is mainly to correct atmospheric errors, correct multipath effects, correct satellite clock errors, and correct orbit errors to obtain preliminarily corrected positioning data; differential correction is mainly to calculate high-precision positioning information in real time through differential correction data provided by a reference station or CORS (Continuous Operating Reference Station System), and perform differential correction using reference station data after the event to obtain preliminarily corrected positioning data, further improving the positioning accuracy and achieving centimeter-level positioning accuracy; then use a filtering algorithm to smooth the preliminarily corrected positioning data to eliminate noise and jitter in the data to obtain smoothed positioning data, ensuring the continuity and stability of the positioning data; finally, convert the WGS84 coordinate system to the local coordinate system required for construction to obtain preprocessed positioning data (i.e., corrected high-precision positioning data). The inertial measurement module first collects inertial data (such as attitude information (pitch angle, roll angle, and yaw angle) and displacement information) of the device where the module is located, and then performs preprocessing operations such as verification, filtering, zero bias correction, temperature compensation, coordinate system alignment, attitude solution, speed and position solution on it to obtain preprocessed inertial data (i.e., corrected attitude and displacement data). The image acquisition module first collects high-resolution image data of the target paving road surface, and then performs preprocessing operations such as image stitching, distortion correction, image enhancement, feature point extraction, image segmentation, and data compression to obtain preprocessed image data (i.e., image data after distortion correction and feature point extraction).

[0032] The data collected by each sensor after preprocessing provides high-precision and highly reliable point cloud data, positioning data, inertial data, and visual information for the unmanned construction system, supporting real-time navigation, global map construction, environmental perception, and boundary line generation, ensuring the accuracy and efficiency of the construction process.

[0033] S202: Determine the calibrated point cloud data based on the preprocessed point cloud data, the preprocessed positioning data, and the preprocessed inertial data.

[0034] In one embodiment, step S202 includes: determining the first point cloud data based on the preprocessed point cloud data and the preprocessed positioning data; and determining the calibrated point cloud data based on the first point cloud data and the preprocessed inertial data. Among them, the preprocessed point cloud data is referenced to a local coordinate system (such as the coordinate system of the device where the sensor is located); the preprocessed positioning data is referenced to a global coordinate system (such as the WGS84 coordinate system); the first point cloud data refers to the local point cloud data in the global coordinate system; and the calibrated point cloud data refers to the locally point cloud data after attitude correction.

[0035] Specifically, using the preprocessed positioning data as a reference, convert the preprocessed point cloud data from the local coordinate system to the global coordinate system to obtain the first point cloud data, so as to determine the position of the device where the sensor is located in the global coordinate system and support the construction of the global map; then perform attitude correction on the first point cloud data based on the preprocessed inertial data to correct the attitude deviation (such as pitch angle, roll angle, yaw angle) of the point cloud data to obtain the calibrated point cloud data, ensuring the consistency of the point cloud data in the global coordinate system.

[0036] S203: Determine the fused data based on the calibrated point cloud data and the preprocessed image data.

[0037] In the embodiments of the present application, the fused data refers to three-dimensional point cloud data with texture information. Specifically, perform data fusion and coordinate transformation on the calibrated point cloud data and the preprocessed image data: use the calibration parameters (such as internal parameters and external parameters) of the image acquisition module to perform coordinate system transformation, convert the calibrated point cloud data from the global coordinate system to the image coordinate system to obtain the point cloud data in the image coordinate system, then extract geometric feature points (such as edges, corners, etc.) from the point cloud data in the image coordinate system, extract visual feature points (such as SIFI, SURF, ORB, etc.) from the preprocessed image data, and use a feature point matching algorithm (such as nearest neighbor matching, RANSAC, etc.) to find the corresponding relationship between the geometric feature points and the visual feature points to obtain the paired feature points after matching, use a registration algorithm (such as the ICP algorithm) to align the point cloud data with the image data, and eliminate the registration error through an optimization algorithm (such as the least squares method) to obtain the registered point cloud data and the registered image data, map the texture information in the registered image data to the registered point cloud data to obtain three-dimensional point cloud data with texture information, fuse it with the registered image data, and use a filtering algorithm to smooth the fused data to obtain the fused data.

[0038] S204: Determine a set of feature points based on the fused data and the image pre - processing data, and generate first boundary line data of the target paving road surface based on the set of feature points.

[0039] In one embodiment, step S204 includes: extracting geometric feature points from the fused data and visual feature points from the image pre - processing data; obtaining a set of feature points after matching and fusion according to the geometric feature points and the visual feature points; processing the set of feature points to generate first boundary line data of the target paving road surface.

[0040] The boundary line refers to a virtual or actual marking line used to define the paving area or construction scope during road construction or maintenance. It is an important reference during construction, ensuring that paving materials (such as asphalt, concrete, etc.) are evenly laid within the specified area and avoiding material waste or exceeding the design scope. Please refer to Figure 3 , the boundary line AD is the dividing line between the target road surface AB and the non - road surface CD.

[0041] Specifically, use a point cloud processing algorithm (such as a method based on curvature or normal vector) to extract geometric feature points (such as the edge points of the curb) of the formwork or curb on both sides of the target paving road surface from the fused data; use an edge detection algorithm (such as the Canny algorithm) to extract edge features from the image pre - processing data, and use a corner detection algorithm (such as the Harris corner detection) to extract corner features from the image pre - processing data to obtain visual feature points (such as edges and corners in the image); match the geometric feature points with the visual feature points to find the corresponding relationship, and generate a set of feature points after matching and fusion through a fusion algorithm (such as weighted average or least squares method); finally, perform clustering, fitting, and optimization processing on the set of feature points to generate first boundary line data of the target paving road surface.

[0042] In one embodiment, the steps of processing the set of feature points to generate first boundary line data of the target paving road surface include: performing clustering processing on the set of feature points to obtain classified feature point clusters; performing fitting processing on the feature point clusters to obtain preliminary boundary line data; performing smoothing processing on the preliminary boundary line data to obtain first boundary line data of the target paving road surface.

[0043] Specifically, use a clustering algorithm (such as K - means or DBSCAN) to cluster the feature points, distinguish different boundary regions, extract the feature point clusters on both sides of the target paving road surface, and obtain classified feature point clusters; use a curve fitting algorithm (such as polynomial fitting or spline curve fitting) to fit the feature point clusters to generate preliminary boundary line data; use a smoothing algorithm (such as Gaussian filtering or moving average) to smooth the boundary line, and perform segmented optimization on the boundary line according to construction requirements to obtain first boundary line data of the target paving road surface.

[0044] S205: Optimize the first boundary line data according to the calibrated point cloud data to obtain optimized boundary line data.

[0045] In one embodiment, step S205 includes: constructing a global point cloud map based on the calibrated point cloud data; optimizing the first boundary line data based on the global point cloud map to obtain optimized boundary line data. Among them, the calibrated point cloud data is the locally calibrated point cloud data after pose calibration.

[0046] Specifically, use the SLAM technology to splice the locally calibrated point cloud data obtained after multiple acquisitions and processing in the foregoing steps into a preliminary global point cloud map, align the local point cloud data through a point cloud registration algorithm (such as the ICP algorithm) to eliminate the errors in the overlapping area, use a filtering algorithm (such as Gaussian filtering) to remove the noise in the preliminary global point cloud map, and smooth the map to improve the map quality to obtain a global point cloud map; compare the first boundary line information with the global point cloud map, use a registration algorithm (such as the ICP algorithm) to correct the position deviation of the boundary line to obtain a registration verification result, and then dynamically adjust the boundary line according to the registration verification result to correct the error, ensuring the accuracy and stability of the boundary line to obtain optimized boundary line data.

[0047] It should be noted that the global point cloud map serves as the reference data for boundary line generation, ensuring the global consistency of the boundary line, while supporting the dynamic update and accuracy verification of the boundary line, adapting to the changes in construction conditions, and providing high-precision and high-reliability environmental information for the unmanned construction system.

[0048] S206: Convert the optimized boundary line data according to a preset target format to generate target boundary line information.

[0049] In one embodiment, step S206 includes: obtaining the actual construction requirements of the paved road surface; performing segmented processing on the optimized boundary line data based on the actual construction requirements to obtain segmented boundary line data; performing format conversion on the segmented boundary line data according to the preset target format to generate target boundary line information. Among them, the actual construction requirements refer to the construction requirements that can meet aspects such as construction accuracy, construction efficiency, construction quality, and construction safety; the preset target format refers to a data format that is manually / automatically set and is convenient for transmission and use. The preset target format in this application can be the WGS84 format.

[0050] Specifically, divide the optimized boundary line data into several segments according to the requirements of different construction areas to obtain segmented boundary line data; then convert the segmented boundary line data into a preset target format for easy transmission and use, thereby generating the target boundary line information of the target paved road surface.

[0051] In one embodiment, after step S206, the method further includes: establishing a communication connection with a construction device; and sending the target boundary line information of the target paving road surface to the construction device. The construction device may be an unmanned paver and an unmanned roller control device.

[0052] In the embodiment of the present application, a communication connection with the construction device can be established through a wireless communication method such as a 4G / 5G network to achieve data transmission; the target boundary line information is transmitted to the construction device in the form of a navigation file or text information to achieve a navigation function. The construction device can determine the boundary line according to the received target boundary line information and set an appropriate construction width.

[0053] Please refer to Figure 3 , Figure 3 which is a schematic diagram of a scenario of a method for determining the boundary of a paving road surface provided by the embodiment of the present application. Through steps S201 to S206, the sensor terminal scans or point-measures the boundary line marks (such as the boundary line AD shown in Figure 3 , which can be a steel formwork or a curbstone) on both sides of the target paving road surface through various sensors to obtain preprocessed data. The server performs combined edge operations on these preprocessed data to obtain the target boundary line information, and segments and outputs the target boundary line information in the form of a WGS84 format navigation file or text information, and transmits it to the construction device through wireless communication to achieve the navigation function, replacing the surveyor to collect the actual paving boundary line in the early stage. This multi-sensor fusion technology reduces the requirements for hardware and operators and on-site risks, and the direct measurement and data fusion methods improve the measurement accuracy, efficiency, and flexibility.

[0054] Based on the content of the above embodiment, the embodiment of the present application provides a device for determining the boundary line of a paving road surface. Specifically, please refer to Figure 4 , the device includes: A data acquisition module 301, configured to acquire preprocessed data of the target paving road surface collected and processed by each sensor. The preprocessed data includes point cloud preprocessed data, positioning preprocessed data, inertial preprocessed data, and image preprocessed data; A point cloud correction module 302, configured to determine corrected point cloud data according to the point cloud preprocessed data, positioning preprocessed data, and inertial preprocessed data; A data fusion module 303, configured to determine fusion data according to the corrected point cloud data and the image preprocessed data; A feature extraction module 304, configured to determine a set of feature points according to the fusion data and the image preprocessed data, and generate first boundary line data of the target paving road surface based on the set of feature points; A boundary line optimization module 305, configured to optimize the first boundary line data according to the corrected point cloud data to obtain optimized boundary line data; The data format conversion module 306 is used to convert the optimized boundary line data according to a preset target format to generate target boundary line information.

[0055] In one embodiment, the point cloud correction module 302 includes: The first determination module is used to determine the first point cloud data according to the point cloud preprocessing data and the positioning preprocessing data; The second determination module is used to determine the corrected point cloud data according to the first point cloud data and the inertial preprocessing data.

[0056] In one embodiment, the feature extraction module 304 includes: The first extraction module is used to extract the geometric feature points in the fusion data and the visual feature points in the image preprocessing data; The matching and fusion module is used to obtain the set of feature points after matching and fusion according to the geometric feature points and the visual feature points; The first processing module is used to process the set of feature points to generate the first boundary line data of the target paving road surface.

[0057] In one embodiment, the first processing module includes: The clustering module is used to perform clustering processing on the set of feature points to obtain the classified clusters of feature points; The fitting module is used to perform fitting processing on the clusters of feature points to obtain the preliminary boundary line data; The smoothing processing module is used to perform smoothing processing on the preliminary boundary line data to obtain the first boundary line data of the target paving road surface.

[0058] In one embodiment, the boundary line optimization module 305 includes: The map construction module is used to construct a global point cloud map based on the corrected point cloud data; The information optimization module is used to optimize the first boundary line data based on the global point cloud map to obtain the optimized boundary line data.

[0059] In one embodiment, the data format conversion module 306 includes: The requirement acquisition module is used to acquire the actual construction requirements of the paving road surface; The segmentation processing module is used to perform segmentation processing on the optimized boundary line data based on the actual construction requirements to obtain the segmented boundary line data; The information generation module is used to perform format conversion on the segmented boundary line data according to the preset target format to generate the target boundary line information of the target paving road surface.

[0060] In one embodiment, the paving road surface boundary line determination device further includes: The communication establishment module is used to establish a communication connection with the construction equipment; An information sending module, configured to send the target boundary line information to the construction equipment.

[0061] Different from the current technologies, the paving road surface boundary line determination device provided in this application is provided with a data acquisition module, a point cloud correction module, a data fusion module, a feature extraction module, and a data format conversion module. The preprocessed data of the target paving road surface collected and processed by each sensor is obtained through the data acquisition module, and a series of processes are performed on the preprocessed data through the point cloud correction module, the data fusion module, the feature extraction module, and the data format conversion module, so as to obtain the boundary line information of the target paving road surface. This multi-sensor fusion technology reduces the requirements for hardware and operators and on-site risks, and the direct measurement and data fusion methods improve the measurement accuracy, efficiency, and flexibility.

[0062] Correspondingly, an embodiment of this application also provides an electronic device, such as Figure 5 shown. The electronic device may include a processor 401 with one or more processing cores, a wireless (WiFi, Wireless Fidelity) module 402, a memory 403 with one or more computer-readable storage media, a radar module 404, a positioning module 405, an inertial measurement module 406, an image acquisition module 407, a power supply 408, and a radio frequency (RF, Radio Frequency) circuit 409 and other components. Those skilled in the art can understand that Figure 5 the structure of the electronic device shown in does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements. Among them:

[0063] WiFi belongs to short-distance wireless transmission technology. The electronic device can help users send and receive emails, browse the web, and access streaming media through the wireless module 402. It provides users with wireless broadband Internet access. Although Figure 5The wireless module 402 is shown, but it can be understood that it does not belong to the essential components of the terminal and can be completely omitted within the scope of not changing the essence of the invention as needed.

[0064] The memory 403 can be used to store software programs and modules. The processor 401 executes various functional applications and data processing by running the computer programs and modules stored in the memory 403. The memory 403 mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the terminal (such as audio data, phone book, etc.). In addition, the memory 403 can include a high-speed random access memory and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices. Correspondingly, the memory 403 can also include a memory controller to provide the processor 401 with access to the memory 403.

[0065] The radar module 404 is a sensor that measures distances and constructs three-dimensional point clouds by emitting laser beams and receiving reflected signals. Optionally, its scanning range is: 360° (horizontal direction) × 96° (vertical direction), that is, it can achieve full-circle scanning in the horizontal direction, covering all directions around the device; in the vertical direction, it can scan a range of 96°, covering the area from the ground to a certain height; the point cloud density can reach 64,000 points per second, that is, 64,000 point cloud data can be collected per second; the scanning distance is 30 meters, that is, its maximum effective scanning distance is 30 meters. The radar module 404 can collect complete three-dimensional information of the surrounding environment of the device and three-dimensional geometric information of the road surface (such as curbs, steel formworks, etc.) in real time, ensuring the comprehensiveness and continuity of the boundary line generation, and supporting the boundary line generation in complex construction environments; its high-density point clouds can accurately describe the fine features of the road surface and the boundary (such as the edges of curbs, the corners of steel formworks, etc.) to improve the accuracy of the boundary line generation; its scanning distance is suitable for most road construction scenarios, and it can ensure that in complex construction environments, it can effectively collect road surface and boundary information at a long distance. Optionally, the radar module 404 can be a 360° lidar.

[0066] The positioning module 405 is a high-precision positioning device that supports acting as a rover station or accessing a CORS (Continuous Operating Reference Station System), obtaining RTK (Real-Time Kinematic) coordinates in real time, and supports post-differential data processing to provide real-time deviation correction, verification, and matching for the SLAM path algorithm. Specifically, as a rover station, it can receive correction data from a reference station to achieve high-precision real-time positioning; it can also access a CORS network and utilize correction data from multiple reference stations to improve positioning accuracy and reliability; it can also support post-differential data processing to improve positioning accuracy through post-event correction. Optionally, the positioning module 405 can be a GNSS positioning module.

[0067] The inertial measurement module 406 is a device based on an inertial measurement unit (IMU) that can provide real-time attitude information (such as pitch angle, roll angle, yaw angle) and acceleration information of the device. In the case of GNSS signal loss or occlusion, it can provide precise positioning within a short period of time. Optionally, the inertial measurement module 406 can be an inertial navigation unit.

[0068] The image acquisition module 407 is a device that can provide 360° panoramic visual images. Through multi-lens or single-lens rotation shooting technology, it captures complete visual information of the environment around the device. Optionally, the image acquisition module 407 can be a panoramic camera.

[0069] The electronic device also includes a power supply 408 (such as a battery) that powers each component. Preferably, the power supply can be logically connected to the processor 401 through a power management system, thereby realizing functions such as management of charging, discharging, and power consumption management through the power management system. The power supply 408 can also include any components such as one or more DC or AC power supplies, a recharge system, a power failure detection circuit, a power converter or inverter, and a power status indicator.

[0070] The radio frequency circuit 409 can be used for receiving and transmitting information or signals during a call. Specifically, after receiving the downlink information from the base station, it is handed over to one or more processors 401 for processing. Additionally, data related to the uplink is sent to the base station. Generally, the radio frequency circuit 409 includes, but is not limited to, antennas, at least one amplifier, a tuner, one or more oscillators, a subscriber identity module (SIM) card, a transceiver, a coupler, a low noise amplifier (LNA), a duplexer, etc. In addition, the radio frequency circuit 409 can also communicate with the network and other devices via wireless communication. The wireless communication can use any communication standard or protocol, including but not limited to the Global System of Mobile communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), etc.

[0071] It should be noted that the electronic device may further include other sensors, such as an optical sensor. Specifically, the optical sensor may include an ambient light sensor and a distance sensor. Among them, the ambient light sensor can adjust the brightness of the display panel according to the brightness of the ambient light. As for other sensors that the electronic device can also be configured with, such as a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, etc., they will not be elaborated here.

[0072] Although not shown, the electronic device may further include a Bluetooth module, etc., which will not be elaborated here. Specifically, in this embodiment, the processor 401 in the electronic device will, according to the following instructions, load the executable files corresponding to the processes of one or more application programs into the memory 403, and the processor 401 will run the application programs stored in the memory 403 to achieve the following functions: Obtain the preprocessed data of the target paving road surface collected and processed by each sensor. The preprocessed data includes point cloud preprocessed data, positioning preprocessed data, inertial preprocessed data, and image preprocessed data; Determine the corrected point cloud data based on the point cloud preprocessed data, positioning preprocessed data, and inertial preprocessed data; Determine the fusion data based on the corrected point cloud data and the image preprocessed data; Determine a set of feature points based on the fusion data and the image preprocessing data, and generate first boundary line data of the target paving road surface based on the set of feature points; Optimize the first boundary line data according to the calibrated point cloud data to obtain optimized boundary line data; Convert the optimized boundary line data according to a preset target format to generate target boundary line information of the target paving road surface.

[0073] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by controlling relevant hardware through instructions. The instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0074] Therefore, an embodiment of the present application provides a computer-readable storage medium, in which multiple instructions are stored, and the instructions can be loaded by a processor to implement the functions of the above method for determining the boundary line of the paving road surface.

[0075] Among them, the computer-readable storage medium may include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), a magnetic disk, an optical disc, etc.

[0076] The above has introduced in detail the method, device, electronic device, and computer-readable storage medium for determining the boundary line of the paving road surface provided by the embodiments of the present application. Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for determining the boundary line of a paved road surface, characterized in that, Comprising: Obtain the preprocessed data of the target paving road surface collected and processed by each sensor, where the preprocessed data includes point cloud preprocessed data, positioning preprocessed data, inertial preprocessed data, and image preprocessed data; Determine the calibrated point cloud data according to the point cloud preprocessed data, the positioning preprocessed data, and the inertial preprocessed data; Determine the fused data according to the calibrated point cloud data and the image preprocessed data; Determine a feature point set according to the fused data and the image preprocessed data, and generate the first boundary line data of the target paving road surface based on the feature point set; Optimize the first boundary line data according to the calibrated point cloud data to obtain optimized boundary line data; Convert the optimized boundary line data according to a preset target format to generate the target boundary line information of the target paving road surface.

2. The method for determining the boundary line of the paved road surface according to claim 1, characterized in that The step of determining the calibrated point cloud data according to the point cloud preprocessed data, the positioning preprocessed data, and the inertial preprocessed data includes: Determine the first point cloud data according to the point cloud preprocessed data and the positioning preprocessed data; Determine the calibrated point cloud data according to the first point cloud data and the inertial preprocessed data.

3. The method for determining the boundary line of the paved road surface according to claim 1, wherein The step of determining a feature point set according to the fused data and the image preprocessed data, and generating the first boundary line data of the target paving road surface includes: Extract the geometric feature points in the fused data and the visual feature points in the image preprocessed data; Obtain a feature point set after matching and fusion according to the geometric feature points and the visual feature points; Process the feature point set to generate the first boundary line data of the target paving road surface.

4. The method for determining the boundary line of the paved road surface according to claim 3, characterized in that The step of processing the feature point set to generate the first boundary line data of the target paving road surface includes: Perform clustering processing on the feature point set to obtain classified feature point clusters; Perform fitting processing on the feature point clusters to obtain preliminary boundary line data; Perform smoothing processing on the preliminary boundary line data to obtain the first boundary line data of the target paving road surface.

5. The method for determining the boundary line of the paved road surface according to claim 1, wherein The step of optimizing the first boundary line data according to the calibrated point cloud data to obtain optimized boundary line data includes: Construct a global point cloud map based on the calibrated point cloud data; Optimize the first boundary line data based on the global point cloud map to obtain optimized boundary line data.

6. The method for determining the boundary line of the paved road surface according to claim 1, characterized in that, The step of converting the optimized boundary line data according to a preset target format to generate the target boundary line information of the target paving road surface includes: Obtain the actual construction requirements of the paving road surface; Perform segmented processing on the optimized boundary line data based on the actual construction requirements to obtain segmented boundary line data; Perform format conversion on the segmented boundary line data according to a preset target format to generate the target boundary line information.

7. The method for determining the boundary line of the paved road surface according to claim 1, wherein After the step of converting the optimized boundary line data according to a preset target format to generate the target boundary line information of the target paving road surface, it further includes: Establish a communication connection with the construction equipment; Send the target boundary line information to the construction equipment.

8. A device for determining the boundary line of a paved road surface, characterized in that, Comprising: A data acquisition module, configured to acquire preprocessed data of a target paving road surface collected and processed by each sensor, where the preprocessed data includes point cloud preprocessed data, positioning preprocessed data, inertial preprocessed data, and image preprocessed data; A point cloud correction module, configured to determine corrected point cloud data according to the point cloud preprocessed data, the positioning preprocessed data, and the inertial preprocessed data; A data fusion module, configured to determine fused data according to the corrected point cloud data and the image preprocessed data; A feature extraction module, configured to determine a set of feature points according to the fused data and the image preprocessed data, and generate first boundary line data of the target paving road surface based on the set of feature points; A boundary line optimization module, configured to optimize the first boundary line data according to the corrected point cloud data to obtain optimized boundary line data; A data format conversion module, configured to convert the optimized boundary line data according to a preset target format to generate target boundary line information of the target paving road surface.

9. An electronic device, characterized in that, It includes a processor and a memory. The memory is used to store a computer program, and the processor is used to run the computer program in the memory to execute the steps in the paving road surface boundary line determination method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores multiple instructions, and the instructions are suitable for being loaded by a processor to execute the steps in the paving road surface boundary line determination method according to any one of claims 1 to 7.

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