Transformer substation construction site three-dimensional map construction device and method

Abstract

The invention discloses a transformer substation construction site three-dimensional map construction device and method, and the device comprises a three-dimensional scanning laser radar which is used for collecting three-dimensional point cloud data; the RTK positioning module is used for acquiring position data; the panoramic camera is used for acquiring panoramic image data of a construction site; the attitude sensor is used for acquiring dynamic attitude information; the main control unit is used for receiving the collected data information and carrying out data processing, data fusion and three-dimensional map construction; the communication module is used for sending the three-dimensional map constructed by the main control unit to the sink node; the power supply unit is used for providing power support; and the main control unit fuses the image data and the three-dimensional point cloud data through a texture mapping algorithm to generate a three-dimensional map of the true color point cloud. By adopting a multi-sensor fusion technology and an efficient algorithm processing method, an accurate three-dimensional map can be constructed in real time in a dynamic construction environment, the intelligent level of construction site management is improved, and efficient implementation of the construction process is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of machine vision, and particularly to a device and method for constructing a three-dimensional map of a substation construction site. Background Art

[0002] With the development of intelligent and digital technologies, the construction and operation and maintenance processes of substations are gradually transforming towards automation and intelligence. Substation construction is an important link in the construction of the power system. Precise management and progress control of the construction site are of great significance for ensuring project quality and construction safety. However, currently, in the process of substation construction, traditional three-dimensional map construction methods mainly rely on manual measurement and two-dimensional drawings. This method is not only inefficient but also has large human errors, and cannot reflect the actual changes in the construction site in a timely manner, resulting in insufficient accuracy of construction management and control, and thus affecting the construction progress and quality.

[0003] Existing substation three-dimensional map construction technologies mainly rely on technical means such as lidar (LiDAR), photogrammetry, and global positioning system (GPS). However, these technologies still face some problems in actual applications:

[0004] (1) The point cloud data and image data generated by traditional lidar and photogrammetry technologies often require a large amount of post-processing. The data processing process is cumbersome and requires high-performance computing resources, which makes it difficult to generate a three-dimensional map of the construction site in real time. Especially when the amount of data at the construction site is large, the processing process may experience delays.

[0005] (2) The accuracy of lidar and traditional photogrammetry technologies is prone to interference in some special environments, such as in the presence of obstacles, bad weather, or complex construction site environments, resulting in a reduction in the accuracy of the data. For example, in a relatively narrow substation construction site, the ranging error of lidar may be large, thus affecting the accuracy of the three-dimensional map and making it impossible to accurately reflect the actual situation on site.

[0006] (3) Although some high-end lidar systems have good data acquisition capabilities, it is still difficult to update the three-dimensional map in real time at the construction site. The update frequency of the three-dimensional maps in the existing technologies is relatively low, and it usually takes several hours or longer to complete a data update, which makes the dynamic monitoring of the construction progress and quality not timely enough. Summary of the Invention

[0007] In view of the deficiencies of the prior art, the present invention provides a three-dimensional map construction device and method for a substation construction site to solve the problems of complex data processing, poor real-time performance, insufficient accuracy, and high equipment costs in the prior art. By adopting multi-sensor fusion technology and an efficient algorithm processing method, it is possible to construct an accurate three-dimensional map in real time in a dynamic construction environment, improve the intelligent level of construction site management, and promote the efficient implementation of the construction process.

[0008] To achieve the above objectives, the present invention is realized through the following technical solutions:

[0009] A three-dimensional map construction device for a substation construction site, comprising:

[0010] A three-dimensional scanning lidar for scanning the construction site and collecting three-dimensional point cloud data;

[0011] An RTK positioning module for obtaining the position data of the location where the three-dimensional scanning lidar is located;

[0012] A panoramic camera for obtaining panoramic image data of the construction site and providing visual data for texture mapping of the three-dimensional map;

[0013] An attitude sensor for real-time monitoring of the dynamic attitude information of the three-dimensional scanning lidar;

[0014] A main control unit for receiving the data information collected by the three-dimensional scanning lidar, RTK positioning module, panoramic camera, and attitude sensor, and performing data processing, data fusion, and three-dimensional map construction;

[0015] A communication module for sending the three-dimensional map constructed by the main control unit to the aggregation node;

[0016] A power supply unit for providing power support for the three-dimensional scanning lidar, RTK positioning module, panoramic camera, attitude sensor, main control unit, and communication module;

[0017] The data of the panoramic camera and the three-dimensional scanning lidar are synchronously transmitted to the main control unit, and the main control unit fuses the image data and the three-dimensional point cloud data through a texture mapping algorithm to generate a three-dimensional map of true-color point cloud.

[0018] Preferably, the data processing, data fusion, and three-dimensional map construction performed by the main control unit include:

[0019] Point cloud data processing. For the point cloud data collected by the three-dimensional scanning lidar, the main control unit performs denoising, registration, and sparsification processing operations to generate a complete three-dimensional point cloud model;

[0020] Data fusion and 3D modeling, combining the generated 3D point cloud model with the image data of the panoramic camera to generate a real 3D construction site map; through the fusion of the positioning data of the RTK positioning module, the attitude sensor and the point cloud, the precise positioning of the 3D map is ensured to generate a 3D map based on the world coordinate system.

[0021] Preferably, the 3D scanning lidar includes a fixed base, a pan-tilt motor is fixedly installed in the middle of the upper surface of the fixed base, the upper end of the pan-tilt motor is rotatably connected to a lidar fixing plate, and a lidar is installed on the lidar fixing plate; a control board is installed inside the fixed base, and the signal output end of the control board is connected to the lidar and the pan-tilt motor.

[0022] The present invention also discloses a method for constructing a 3D map of a substation construction site, including the following steps:

[0023] Step S1: Equipment deployment and initialization

[0024] Install equipment: Reasonably arrange a 3D scanning lidar, an RTK positioning module, a panoramic camera and an attitude sensor at the substation construction site, and make the 3D scanning lidar able to cover the key areas of the construction site. The panoramic camera is used to capture the visual data of the construction area, the attitude sensor monitors the dynamic changes of the 3D scanning lidar, and the RTK positioning module provides centimeter-level precise positioning for the 3D scanning lidar;

[0025] Equipment initialization: Start the 3D scanning lidar, RTK positioning module, panoramic camera and attitude sensor and perform initialization settings;

[0026] Network connection: Through the communication module, ensure that the device can be connected to the edge server through a wireless network;

[0027] Step S2: Real-time data acquisition

[0028] 3D scanning lidar data acquisition: The 3D scanning lidar starts to scan the construction site to obtain the 3D point cloud data of the construction site;

[0029] RTK positioning module data acquisition: The RTK positioning module receives satellite signals in real time and provides the precise position data of the 3D scanning lidar;

[0030] Panoramic camera data acquisition: The panoramic camera captures panoramic image data at the construction site;

[0031] Attitude sensor data acquisition: The attitude sensor monitors the dynamic attitude information of the 3D scanning lidar in real time and provides the pitch, yaw and roll angle data of the 3D scanning lidar;

[0032] Step S3: Data synchronization

[0033] The point cloud data of the 3D scanning lidar, the image data of the panoramic camera, the data of the attitude sensor, and the position data of the RTK positioning module are sent to the main control unit in real time; and then the main control unit synchronizes them through timestamps;

[0034] Step S4: Data processing and 3D map construction

[0035] Point cloud data processing: The main control unit performs denoising, registration, and sparsification processing operations on the collected point cloud data;

[0036] Fusion of point cloud and image data: Using image processing and texture mapping technologies, the image data collected by the panoramic camera is fused with the 3D point cloud data of the 3D scanning lidar to generate a 3D map with real textures;

[0037] Fusion of position data and point cloud: Combining the high-precision position information provided by the RTK positioning module with the point cloud data to ensure the accurate positioning of the generated 3D map in space;

[0038] Attitude data correction: According to the angle data provided by the attitude sensor, correct the attitude information of the point cloud;

[0039] Generate a 3D construction site map: Through the fusion of point cloud data, the attachment of image textures, and coordinate alignment, generate a 3D map of the construction site;

[0040] Step S5: Real-time update and display

[0041] Real-time map update: Combining the scanning period of the 3D scanning lidar and the actual requirements on site, perform timed map updates;

[0042] Map display: Through an integrated visualization interface, view the real-time updated 3D map on the terminal device;

[0043] Multi-device collaborative work: Through the communication module, the 3D map data of multiple areas are synchronized in real time and uploaded to the edge server for aggregation and merging to generate a complete 3D map of the construction site.

[0044] Preferably, in the step S4, the fusion of the point cloud and the image data specifically includes the following steps:

[0045] Step S41: Data preprocessing;

[0046] Step S42: Coordinate system conversion and point cloud data alignment;

[0047] Step S43: Texture mapping;

[0048] Step S44: Texture mapping optimization.

[0049] Preferably, the step S41 specifically includes the following steps:

[0050] Step S411: Point cloud data processing

[0051] Perform denoising, registration, and sparsification processing on the three-dimensional point cloud data; assume that the point cloud data generated by the lidar is P = {p1, p2,..., p n}, where each point p i in the point cloud has coordinates (x i , y i , z i ), representing the position of the point in three-dimensional space; the denoised point cloud data is P′, where

[0052] Step S412: Image data processing

[0053] Assume that the image data collected by the panoramic camera is I = {I1, I2,..., I m}, where I i represents the image of each perspective;

[0054] The panoramic camera then stitches multiple images into a panoramic image I pano through an image stitching algorithm and performs distortion correction to correct the perspective distortion of the image.

[0055] Preferably, the step S42 specifically includes the following steps:

[0056] Step S421: Coordinate system conversion

[0057] Assume that the lidar coordinate system is C L , and the panoramic camera coordinate system is C C . Convert the three-dimensional point cloud data P′ of the lidar to the panoramic camera coordinate system C C ;

[0058] Through the rotation matrix R and the translation vector T, the point cloud of the lidar is converted from the coordinate system C L to the coordinate system C C , that is:

[0059] P C = RP L + T

[0060] where P L is the point cloud data in the lidar coordinate system, and P C is the converted point cloud data;

[0061] Step S422: Feature matching and point cloud-image alignment

[0062] Through the feature matching algorithm in the image Ipano Identify significant feature points and match these feature points with the corresponding points in the point cloud data P C ; for each feature point and the corresponding point in the point cloud data P C , calculate its UV coordinates and map it from the three-dimensional point cloud space to the two-dimensional image space.

[0063] Preferably, the step S43 specifically includes the following steps:

[0064] Step S431: UV coordinate calculation

[0065] Suppose a point p C in the point cloud data P C = (x C , y C , z C ) corresponds to a pixel point p pano = (u, v) in the image I T . Perform projection conversion through the internal parameter matrix K and the external parameter matrix [R|T] of the panoramic camera;

[0066] The camera projection matrix Q is expressed as:

[0067] Q = K[R|T]

[0068] where K is the camera internal parameter matrix, R is the rotation matrix, and T is the translation vector:

[0069] Project the point p C in the three-dimensional space into the two-dimensional image coordinate system to obtain its UV coordinates:

[0070]

[0071] Project the point p C in the three-dimensional space onto the image plane through the above formula to obtain the corresponding coordinates (u, v) in the two-dimensional image;

[0072] Step S432: Texture mapping

[0073] According to the calculated UV coordinates, map the pixel values on the image I pano to the corresponding points in the point cloud; for each point p C in the point cloud, obtain its corresponding pixel value through the texture mapping algorithm to generate a three-dimensional point cloud with texture;

[0074] The formula is as follows:

[0075] P textured = {(p C , I pano (u, v))|p C ∈ P C}

[0076] In the formula, P textured is a three-dimensional point cloud with texture, and I pano (u, v) is the pixel value at the position of the corresponding point (u, v) in the image I pano , and (u, v) are the projected UV coordinates.

[0077] Preferably, the step S44 specifically includes the following steps:

[0078] Step S441: Texture compression and optimization

[0079] Multi-resolution texture mapping: Generate texture maps with different resolutions and perform texture mapping at different resolutions;

[0080] Texture compression: Optimize texture data through texture compression algorithms;

[0081] Step S442: Fine adjustment and correction

[0082] For large-scale point cloud data, perform fine adjustment during image stitching and texture mapping, especially for seams and edge regions, and use color adjustment and gradient processing.

[0083] The present invention provides a three-dimensional map construction device and method for a substation construction site. It has the following beneficial effects: By using a three-dimensional scanning lidar, an RTK positioning module, a panoramic camera, and an attitude sensor in cooperation with each other, and through multi-sensor data fusion technology, high-precision three-dimensional map construction can be achieved to meet the high requirements for accuracy in substation construction. Through automated data collection and processing, the efficiency of map construction at the construction site is significantly improved, and the manual operation time is reduced. The three-dimensional map can be updated in real time to ensure a high degree of consistency between the map content and the construction site, and the data update delay is low; the data update frequency is adjustable, greatly improving the accuracy and efficiency of construction management. By combining with the construction progress, intelligent management of the construction process can be realized, providing the real-time status of the construction site, and then effectively promoting the efficient implementation of the construction process. Brief Description of the Drawings

[0084] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the prior art.

[0085] Figure 1 Structural schematic diagram of the present invention;

[0086] Figure 2 Structural schematic diagram of the three-dimensional scanning lidar in the present invention. Detailed Embodiments

[0087] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the accompanying drawings in the present invention.

[0088] As Figure 1-2 shown, the present invention proposes a three-dimensional map construction device for a substation construction site, including: a three-dimensional scanning lidar, an RTK positioning module, a panoramic camera, an attitude sensor, a communication module, a main control unit, and a power supply unit.

[0089] Among them, the three-dimensional scanning lidar is used to scan the construction site and collect three-dimensional point cloud data. Specifically, the three-dimensional scanning lidar includes a fixed base 1, a pan-tilt motor 2 is fixedly installed in the middle of the upper surface of the fixed base 1, a lidar fixing plate 3 is rotatably connected to the upper end of the pan-tilt motor 2, and a lidar 4 is installed on the lidar fixing plate 3; by connecting the lidar 4 to the pan-tilt motor 2 through the lidar fixing plate 3, the scanning area of the lidar 4 can be changed from 270° in the vertical direction to 360° in the horizontal direction × 270° in the vertical direction. A control board is installed inside the fixed base 1, and the signal output end of the control board is connected to the lidar 4 and the pan-tilt motor 2 to control the scanning of the lidar and receive the data of the lidar, and to control the rotation speed and start and stop of the pan-tilt motor. By controlling the rotation speed of the pan-tilt motor, different angular resolutions of the three-dimensional lidar can be adjusted, such as 0.3°, 0.6°, and 0.9°, etc. The smaller the angular resolution, the greater the point cloud density of the scan.

[0090] The RTK positioning module is used to obtain high-precision position data of the location where the three-dimensional scanning lidar is located. The RTK positioning module can provide centimeter-level positioning accuracy through satellite signals, thus meeting the high-precision requirements in complex environments.

[0091] The panoramic camera is used to obtain panoramic image data of the construction site and provide visual data for texture mapping of the three-dimensional map. The panoramic camera can provide high-definition image data with a 360° view by simultaneously shooting with multiple cameras. In this embodiment, the panoramic camera is synchronized with the data of the three-dimensional scanning lidar, and through the texture mapping algorithm, the image data and the three-dimensional point cloud data are fused to generate a true-color point cloud three-dimensional map, improving the visualization effect and enabling construction personnel to intuitively view the real-time situation of the construction site.

[0092] The attitude sensor is used to monitor the dynamic attitude information of the three-dimensional scanning lidar in real time and realize the conversion and mapping between the device coordinate system and the world coordinate system. When the three-dimensional scanning lidar moves, it can provide the angle change information of the three-dimensional scanning lidar. Through the real-time feedback of the attitude sensor, the system can automatically correct the spatial position of the three-dimensional scanning lidar to ensure the accurate matching and positioning of the point cloud data.

[0093] The communication module is responsible for transmitting the data collected by multiple 3D map construction devices to ensure real-time data synchronization. The 3D point cloud data, image data, and pose data collected by each 3D map construction device are transmitted to the main control unit, and a spatial 3D map is generated through the map generation algorithm of the main control unit. However, due to the large area of the construction site, a single 3D map construction device is difficult to achieve full coverage of the construction site. Therefore, multiple 3D map construction devices are required to build maps synchronously. The communication module supports wireless data transmission technologies such as Wi-Fi and 4G / 5G networks to ensure real-time data synchronization and efficient transmission of 3D map construction devices at the construction site. The 3D maps established by each 3D map construction device are sent to the edge server through the communication module, and the 3D maps of multiple devices are further merged at the edge server to construct a complete 3D map of the construction site.

[0094] The main control unit is used to receive the data information collected by the 3D scanning lidar, RTK positioning module, panoramic camera, and attitude sensor, and perform data processing, data fusion, and 3D map construction; it mainly includes:

[0095] Point cloud data processing: For the point cloud data collected by the 3D scanning lidar, the main control unit performs denoising, registration, and sparsification processing operations to generate a complete 3D point cloud model;

[0096] Data fusion and 3D modeling: Combine the generated 3D point cloud model with the image data of the panoramic camera to generate a real 3D construction site map; through the fusion of the positioning data of the RTK positioning module, the attitude sensor and the point cloud, ensure the precise positioning of the 3D map to generate a 3D map based on the world coordinate system.

[0097] The power supply unit is used to provide power support for the 3D scanning lidar, RTK positioning module, panoramic camera, attitude sensor, main control unit, and communication module; ensure that each sensor and device can operate continuously and stably. In this embodiment, the power supply unit has sufficient power capacity to meet the long-term operation requirements of devices such as the 3D scanning lidar, RTK positioning module, and panoramic camera. At the same time, the power supply unit also has an intelligent power management function, which can automatically switch to the backup power supply when the power is insufficient to ensure the continuity and stability of the system.

[0098] Through the mutual cooperation of the 3D scanning lidar, RTK positioning module, panoramic camera, and attitude sensor, the present invention can effectively achieve high-precision 3D map construction and meet the high requirements for accuracy in substation construction.

[0099] The present invention also discloses a method for constructing a 3D map of a substation construction site, including the following steps:

[0100] Step S1: Equipment deployment and initialization

[0101] Installation equipment: Reasonably arrange 3D scanning lidar, RTK positioning module, panoramic camera and attitude sensor at the substation construction site, and make the 3D scanning lidar able to cover the key areas of the construction site. The panoramic camera is used to capture the visual data of the construction area, the attitude sensor monitors the dynamic changes of the 3D scanning lidar, and the RTK positioning module provides centimeter-level precise positioning for the 3D scanning lidar;

[0102] Equipment initialization: Start the 3D scanning lidar, RTK positioning module, panoramic camera and attitude sensor and perform initialization settings; specifically include the scanning angle and resolution settings of the lidar, the view angle selection of the panoramic camera, and the calibration operations of the attitude sensor and RTK module.

[0103] Network connection: Through the communication module, ensure that each set of devices can be connected to the edge server through the wireless network;

[0104] Step S2: Real-time data collection

[0105] 3D scanning lidar data collection: The 3D scanning lidar starts to scan the construction site and obtains the 3D point cloud data of the construction site through its 360°×270° scanning range, and according to actual needs, select different angular resolutions (such as 0.3°, 0.6°, 0.9°) for high-density point cloud data collection.

[0106] RTK positioning module data collection: The RTK positioning module receives satellite signals in real time and provides the precise position data of the 3D scanning lidar; through this position data, it can effectively ensure that the coordinates of each collection point can be accurately mapped to the world coordinate system, providing centimeter-level spatial position accuracy.

[0107] Panoramic camera data collection: The panoramic camera captures panoramic image data at the construction site; the panoramic camera obtains 360° high-definition images through the synchronous shooting of multiple cameras, providing comprehensive visual information of the construction site.

[0108] Attitude sensor data collection: The attitude sensor monitors the dynamic attitude information of the 3D scanning lidar in real time and provides the pitch, yaw, and roll angle data of the 3D scanning lidar; through this data, it helps to correct the position and direction of the 3D scanning lidar to ensure the accuracy of the 3D map spatial coordinates.

[0109] Step S3: Data synchronization

[0110] Send the point cloud data of the 3D scanning lidar, the image data of the panoramic camera, the data of the attitude sensor, and the position data of the RTK positioning module to the main control unit in real time; and then the main control unit synchronizes through the time stamp;

[0111] Step S4: Data Processing and 3D Map Construction

[0112] Point cloud data processing: The point cloud data collected by the 3D scanning lidar is the basic data for constructing the 3D map. However, due to possible interferences such as noise and errors in actual scanning, it is necessary to perform denoising, registration, and sparsification processing operations on the collected point cloud data through the main control unit. Specifically, it includes:

[0113] (1) Point cloud denoising processing

[0114] In the point cloud data collected by the lidar, there are usually some noise points caused by environmental factors (such as rain, fog, reflection, etc.). Through point cloud denoising algorithms (such as statistics-based denoising methods or voxel grid denoising methods), these noise points are removed to ensure the accuracy of the data. The denoising processing retains the valid data and eliminates the abnormal data by setting thresholds or using the nearest neighbor point method.

[0115] (2) Point cloud registration

[0116] Point cloud registration is to align the point cloud data collected at different positions and different time periods into a unified coordinate system. Usually, the ICP (Iterative Closest Point) algorithm is used for registration. This algorithm iteratively optimizes the spatial overlapping part between point clouds to ensure the precise alignment of point cloud data. During the registration process, the high-precision positioning data provided by the RTK positioning module and the angle data of the attitude sensor are used to assist in registration to improve the registration accuracy of the point cloud data.

[0117] (3) Point cloud sparsification processing

[0118] Point cloud sparsification processing is a process to reduce the amount of point cloud data, aiming to optimize the calculation efficiency while ensuring that the point cloud density is high enough. In this embodiment, the sparsification method uses the voxel grid method (Voxel Grid Filter). This method divides the point cloud into multiple small voxel grids, and selects a representative point within each voxel to reduce the number of redundant points. After the sparsification processing, the computational amount of the point cloud data is significantly reduced, which helps with real-time processing and map generation.

[0119] (4) Point cloud segmentation and feature extraction

[0120] Point cloud segmentation is to divide the entire point cloud data set into several parts according to different physical features (such as buildings, equipment, ground, etc.). The segmented point cloud can provide different object models for the 3D map, facilitating visualization and analysis. Feature extraction is to extract features (such as planes, curved surfaces, etc.) that are meaningful for 3D modeling from the point cloud. These features are used for model construction and target recognition.

[0121] Point Cloud and Image Data Fusion: Using image processing and texture mapping techniques, the image data collected by the panoramic camera is texture-mapped with the 3D point cloud data of the 3D scanning lidar to align the image data with the point cloud data. Based on the feature point matching between the panoramic image and the coordinates in the point cloud, the color information of the image is accurately mapped to the corresponding point cloud to obtain a true-color 3D point cloud with texture features, so as to generate a 3D map with real texture. In this embodiment, the image data provided by the panoramic camera can provide high-quality texture mapping for the 3D map, making the map more realistic and intuitive in visual effects. The panoramic image is attached to the 3D point cloud generated by the lidar through texture mapping technology. Texture mapping not only enhances the visibility of the map but also helps construction workers more intuitively understand the details of the construction site. The specific method is as follows:

[0122] Step S41: Data preprocessing;

[0123] (11) Point cloud data processing

[0124] Denoise, register, and sparsify the 3D point cloud data; assume the point cloud data generated by the lidar is P = {p1, p2,..., p n}, where each point p i in the point cloud has coordinates (x i , y i , z i ) representing the position of the point in 3D space; the goals of denoising and sparsifying are to eliminate outliers and reduce the computational amount. The denoised point cloud data is P', where

[0125] (12) Image data processing

[0126] Assume the image data collected by the panoramic camera is I = {I1, I2,..., I m}, where I i represents the image of each perspective; the resolution of the image is w × h. Then, through the image stitching algorithm, multiple images are stitched into a panoramic image I pano with a resolution of w' × h', and distortion correction is performed to correct the perspective distortion of the image.

[0127] Step S42: Coordinate system conversion and point cloud data alignment;

[0128] (21) Coordinate system conversion

[0129] Assume the lidar coordinate system is C L , and the panoramic camera coordinate system is C C . Convert the 3D point cloud data P' of the lidar to the panoramic camera coordinate system C C ;

[0130] Through the rotation matrix R and the translation vector T, the point cloud of the lidar is transformed from the coordinate system C L to the coordinate system C C , that is:

[0131] P C = RP L + T

[0132] where P L is the point cloud data in the lidar coordinate system, and P C is the transformed point cloud data;

[0133] (22) Feature matching and point cloud - image alignment

[0134] Identify significant feature points in the image I pano through a feature matching algorithm (such as SIFT, SURF, ORB), and match these feature points with the corresponding points in the point cloud data P C . For each feature point and the corresponding point in the point cloud data P C , calculate its UV coordinates and map it from the three - dimensional point cloud space to the two - dimensional image space.

[0135] Step S43: Texture mapping;

[0136] (31) UV coordinate calculation

[0137] Suppose a point p C =(x C , y C , z C ) in the point cloud data P C corresponds to a pixel point p pano =(u, v) in the image I T . Perform projection transformation through the internal parameter matrix K and the external parameter matrix [R|T] of the panoramic camera;

[0138] The camera projection matrix Q is expressed as:

[0139] Q = K[R|T]

[0140] where K is the camera internal parameter matrix, R is the rotation matrix, and T is the translation vector:

[0141] Project the point p C in the three - dimensional space onto the two - dimensional image coordinate system to obtain its UV coordinates:

[0142]

[0143] Project the point p C in the three - dimensional space onto the image plane through the above formula to obtain the corresponding coordinates (u, v) in the two - dimensional image;

[0144] (32) Texture mapping

[0145] Map the pixel values on the image I to the corresponding points in the point cloud according to the calculated UV coordinates; for each point p in the point cloud pano , obtain its corresponding pixel value through the texture mapping algorithm to generate a three-dimensional point cloud with texture; C The formula is as follows:

[0146] The formula is as follows:

[0147] P textured ={(p C , I pano (u, v)) | p C ∈P C}

[0148] In the formula, P textured is the three-dimensional point cloud with texture, I pano (u, v) is the pixel value at the position of the corresponding point (u, v) in the image I pano , and (u, v) are the projected UV coordinates.

[0149] Step S44: Texture mapping optimization.

[0150] (41) Texture compression and optimization

[0151] Problems such as color distortion and texture repetition may exist in the point cloud after texture mapping. To solve these problems, the following optimization methods can be used:

[0152] Multi-resolution texture mapping: Generate texture maps with different resolutions and perform texture mapping at different resolutions to improve the rendering efficiency and quality of the texture;

[0153] Texture compression: Optimize the texture data through texture compression algorithms (such as MIP mapping, texture atlas) to reduce memory occupancy and improve the rendering speed.

[0154] (42) Refined adjustment and correction

[0155] For large-scale point cloud data, perform refined adjustment during image stitching and texture mapping, especially for the seam and edge regions, and use color adjustment and gradient processing to avoid obvious seams and texture distortion.

[0156] Position data and point cloud fusion: Combine the high-precision position information provided by the RTK positioning module with the point cloud data to ensure the accurate positioning of the generated three-dimensional map in space;

[0157] Attitude data correction: Correct the attitude information of the point cloud according to the angle data provided by the attitude sensor; further improve the accuracy of the map;

[0158] Generating a 3D construction site map: Based on the data processed according to the above steps, the main control unit generates a 3D map of the construction site through the fusion of point cloud data, attachment of image textures, and coordinate alignment. This map not only contains spatial position data but also panoramic texture information of the construction site, providing accurate and visual construction site status for construction workers.

[0159] Step S5: Real-time update and display

[0160] Real-time map update: Combining the scanning period of the 3D scanning lidar and the actual requirements on-site, perform timed map updates. The newly collected data is transmitted to the main control unit, and the system updates the 3D map to provide the latest construction site information. Construction workers can view the construction progress, equipment status, and other key data at any time;

[0161] Map display: Through an integrated visualization interface, construction workers can view the real-time updated 3D map on terminal devices (such as tablets, mobile phones, computers, etc.). This helps construction workers quickly understand the on-site situation, discover problems in advance, and make adjustments;

[0162] Multi-device collaborative work: Since the construction site area is large, multiple sets of devices will collect data in different areas. Therefore, through the communication module, the 3D map data of multiple areas can be synchronized in real-time and uploaded to the edge server for aggregation and merging to generate a complete 3D map of the construction site to ensure full-area coverage.

[0163] The present invention realizes high-precision 3D map construction by using a 3D scanning lidar, an RTK positioning module, a panoramic camera, and an attitude sensor in cooperation with each other and through multi-sensor data fusion technology to meet the high requirements for accuracy in substation construction. Through automated data collection and processing, the efficiency of construction site map construction is significantly improved, and the manual operation time is reduced. In addition, the present invention can obtain construction site data in real-time and update the 3D map in real-time, ensuring a high degree of consistency between the map content and the construction site, and having a low data update delay; the data update frequency is adjustable, greatly improving the accuracy and efficiency of construction management. By combining with the construction progress, intelligent management of the construction process can be realized, providing the real-time status of the construction site, and then effectively promoting the efficient implementation of the construction process.

[0164] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A device for constructing a three-dimensional map of a substation construction site, characterized in that: include: 3D scanning laser radar, used to scan the construction site and collect 3D point cloud data; RTK positioning module, used to obtain the location data of the 3D scanning laser radar; Panoramic camera, used to obtain panoramic image data of the construction site and provide visual data for 3D map texture mapping; Attitude sensor, used to monitor the dynamic attitude information of 3D scanning laser radar in real time; The main control unit is used to receive data information collected by the 3D scanning laser radar, RTK positioning module, panoramic camera and attitude sensor, and perform data processing, data fusion and 3D map construction; A communication module, used to send the three-dimensional map constructed by the main control unit to the aggregation node; Power supply unit, used to provide power support for 3D scanning laser radar, RTK positioning module, panoramic camera, attitude sensor, main control unit and communication module; The data of the panoramic camera and the three-dimensional scanning laser radar are synchronously transmitted to the main control unit, and the main control unit fuses the image data with the three-dimensional point cloud data through a texture mapping algorithm to generate a three-dimensional map of a true color point cloud.

2. A three-dimensional map construction device for a substation construction site according to claim 1, characterized in that: The data processing, data fusion and three-dimensional map construction performed by the main control unit include: Point cloud data processing: For the point cloud data collected by the 3D scanning laser radar, the main control unit performs denoising, registration and sparse processing operations to generate a complete 3D point cloud model; Data fusion and 3D modeling combine the generated 3D point cloud model with the image data of the panoramic camera to generate a realistic 3D construction site map. Through the fusion of the positioning data of the RTK positioning module, the attitude sensor and the point cloud, the precise positioning of the 3D map is ensured to generate a 3D map based on the world coordinate system.

3. A three-dimensional map construction device for a substation construction site according to claim 1, characterized in that: The three-dimensional scanning laser radar comprises a fixing seat (1), a pan-tilt motor (2) is fixedly mounted in the middle of the upper surface of the fixing seat (1), the upper end of the pan-tilt motor (2) is rotatably connected to a laser radar fixing plate (3), and a laser radar (4) is mounted on the laser radar fixing plate (3); a control board is mounted inside the fixing seat (1), and a signal output end of the control board is connected to the laser radar (4) and the pan-tilt motor (2).

4. A method for constructing a three-dimensional map of a substation construction site, characterized in that: The following steps are involved: Step S1: Device deployment and initialization Equipment installation: 3D scanning laser radar, RTK positioning module, panoramic camera and attitude sensor are reasonably arranged at the substation construction site, and the 3D scanning laser radar can cover the key areas of the construction site. The panoramic camera is used to capture the visual data of the construction area, the attitude sensor monitors the dynamic changes of the 3D scanning laser radar, and the RTK positioning module provides centimeter-level precise positioning for the 3D scanning laser radar; Equipment initialization: Start the 3D scanning laser radar, RTK positioning module, panoramic camera and attitude sensor and perform initialization settings; Network connection: through the communication module, ensure that the device can connect to the edge server through the wireless network; Step S2: Real-time data collection 3D scanning laser radar data acquisition: The 3D scanning laser radar starts scanning the construction site to obtain 3D point cloud data of the construction site; RTK positioning module data acquisition: The RTK positioning module receives satellite signals in real time and provides accurate position data of the 3D scanning lidar; Panoramic camera data acquisition: Panoramic cameras capture panoramic image data at the construction site; Attitude sensor data acquisition: The attitude sensor monitors the dynamic attitude information of the 3D scanning laser radar in real time and provides the pitch, yaw and roll angle data of the 3D scanning laser radar; Step S3: Data synchronization The point cloud data of the 3D scanning laser radar, the image data of the panoramic camera, the data of the attitude sensor and the position data of the RTK positioning module are sent to the main control unit in real time; the main control unit then synchronizes them through timestamps; Step S4: Data processing and 3D map construction Point cloud data processing: The main control unit performs denoising, registration and sparse processing on the collected point cloud data; Fusion of point cloud and image data: Using image processing and texture mapping technology, the image data collected by the panoramic camera is fused with the 3D point cloud data of the 3D scanning laser radar to generate a 3D map with real texture; Fusion of position data and point cloud: Combining the high-precision position information provided by the RTK positioning module with the point cloud data to ensure the accurate positioning of the generated 3D map in space; Attitude data correction: Correct the attitude information of the point cloud according to the angle data provided by the attitude sensor; Generate 3D construction site map: Generate 3D map of the construction site through the fusion of point cloud data, attachment of image texture and coordinate alignment; Step S5: Real-time update and display Real-time map update: Combine the scanning cycle of the 3D scanning laser radar with the actual needs of the site to perform regular map updates; Map display: Through the integrated visualization interface, real-time updated 3D maps can be viewed on terminal devices; Multi-device collaboration: Through the communication module, the 3D map data of multiple areas are synchronized in real time and uploaded to the edge server for aggregation and merging to generate a complete 3D map of the construction site.

5. A method for constructing a three-dimensional map of a substation construction site according to claim 4, characterized in that: In step S4, the fusion of point cloud and image data specifically includes the following steps: Step S41: data preprocessing; Step S42: coordinate system conversion and point cloud data alignment; Step S43: texture mapping; Step S44: Texture mapping optimization.

6. A method for constructing a three-dimensional map of a substation construction site according to claim 5, characterized in that: The step S41 specifically includes the following steps: Step S411: Point cloud data processing De-noising, registration and sparse processing are performed on the three-dimensional point cloud data; suppose the point cloud data generated by the laser radar is P = {p1, p2, ..., p n }, where each point p in the point cloud i The coordinates of (x i ,y i ,z i ), represents the position of the point in three-dimensional space; the denoised point cloud data is P′, where Step S412: Image data processing Assume that the image data collected by the panoramic camera is I = {I1, I2, ..., I m }, where I i An image representing each viewpoint; The panoramic camera then stitches multiple images into a panoramic image using an image stitching algorithm. pano , and distortion correction is performed to correct the perspective distortion of the image.

7. A method for constructing a three-dimensional map of a substation construction site according to claim 6, characterized in that: The step S42 specifically includes the following steps: Step S421: Coordinate system conversion Assume the laser radar coordinate system is C L , the panoramic camera coordinate system is C C , transform the three-dimensional point cloud data P′ of the laser radar into the panoramic camera coordinate system C C ; By rotating the matrix R and translating the vector T, the point cloud of the laser radar is transformed from the coordinate system C L Transform to coordinate system C C ,Right now: P C =RP L +T Among them, P L is the point cloud data in the laser radar coordinate system, P C is the converted point cloud data; Step S422: Feature matching and point cloud-image alignment Through the feature matching algorithm in image I pano Identify the salient feature points in the point cloud data P C Match the corresponding points in the point cloud data P C , calculate its UV coordinates, and map it from the three-dimensional point cloud space to the two-dimensional image space.

8. A method for constructing a three-dimensional map of a substation construction site according to claim 7, characterized in that: The step S43 specifically includes the following steps: Step S431: UV coordinate calculation Suppose that the point cloud data P C A point p in C =(x C ,y C , z C ) corresponds to image I pano A pixel point p T =(u,v), projection transformation is performed through the internal parameter matrix K and external parameter matrix [R|T] of the panoramic camera; The camera projection matrix Q is expressed as: Q=K[R|T] Among them, K is the camera intrinsic parameter matrix, R is the rotation matrix, and T is the translation vector: Let point p in three-dimensional space C Project it into the two-dimensional image coordinate system to get its UV coordinates: By using the above formula, the point p in the three-dimensional space C Project it onto the image plane and get the corresponding coordinates (u, v) in the two-dimensional image; Step S432: Texture mapping According to the calculated UV coordinates, image I pano The pixel values ​​on the point cloud are mapped to the corresponding points in the point cloud; for each point p in the point cloud C , the corresponding pixel value is obtained through the texture mapping algorithm to generate a three-dimensional point cloud with texture; The formula is as follows: P textured ={(p C ,I pano (u,v))|p C ∈P C } Where P textured is a 3D point cloud with texture, I pano (u,v) is the image I pano The pixel value of the corresponding point (u, v) in the image, (u, v) is the UV coordinate after projection.

9. A method for constructing a three-dimensional map of a substation construction site according to claim 8, characterized in that: The step S44 specifically includes the following steps: Step S441: Texture compression and optimization Multi-resolution texture mapping: by generating texture maps of different resolutions, texture mapping can be performed at different resolutions; Texture compression: Optimize texture data through texture compression algorithm; Step S442: Fine-tuning and correction For large-scale point cloud data, fine adjustments are made during the image stitching and texture mapping process, especially for seams and edge areas, using color adjustment and gradient processing.

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