Live-action three-dimensional model construction method and device of vehicle-mounted mobile measurement system and vehicle-mounted mobile measurement system

Abstract

The invention discloses a live-action three-dimensional model construction method and device based on a vehicle-mounted mobile measurement system and the vehicle-mounted mobile measurement system, and relates to the technical field of three-dimensional model construction.The method comprises the steps that after time synchronization of all sensors is completed, acceleration and angular velocity are collected through inertial navigation; vehicle advancing mileage information is collected based on a speedometer; calculating a first position and a first attitude of the vehicle carrier which are not optimized according to the acceleration, the angular velocity and the mileage information; optimizing the first position and the first attitude based on the three-dimensional laser radar to obtain optimized pose information; calculating three-dimensional point cloud data geographic coordinates and a panoramic image pose according to the optimized pose information; processing the geographic coordinates of the three-dimensional point cloud data through the panoramic image pose to obtain target three-dimensional point cloud data, and calculating the image pose of the digital camera through the panoramic image pose; and constructing a live-action three-dimensional model through the target three-dimensional point cloud data and the digital camera image pose.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of three-dimensional model construction, in particular to a real scene three-dimensional model construction method and device based on a vehicle-mounted mobile measurement system and the vehicle-mounted mobile measurement system. BACKGROUND

[0002] Mobile mapping technology (MMS) is a new technology in the forefront of the surveying and mapping industry today, which is widely used in real scene three-dimensional ground multi-source data acquisition and processing, and is combined with aerial data and unmanned aerial vehicle data to construct a real scene three-dimensional model, so as to achieve the goal of expressing road and road surrounding landscape and building facade information. The vehicle-mounted mobile measurement system is an automatic data acquisition and processing system which takes a common vehicle or a patrol vehicle as a mobile platform, and is equipped with multiple types of sensors, core processors, synchronous controllers and other functional modules, so as to realize one-time acquisition, automatic fusion of image, laser point cloud, position and attitude data, and quickly meet the application requirements of multiple fields.

[0003] The core of the vehicle-mounted mobile measurement system is the high integration of multiple functional modules. The main sensors of the vehicle-mounted mobile measurement system include a global navigation satellite system (GNSS) positioning antenna and receiver for positioning and attitude determination, an inertial navigation system (IMU) and an odometer, a panoramic camera and a digital camera for acquiring image data, and a laser radar for acquiring three-dimensional point cloud. The time synchronization controller can realize synchronous control of multiple source sensors and record the trigger time of each sensor. The core processor can control and display the system state, unify the spatial coordinate reference system of different image data and point cloud data to realize fusion processing, and realize two-dimensional or three-dimensional expression of real world geographic scenes and geographic entities. At the same time, the platform design of the vehicle-mounted mobile measurement system should have practicality and flexibility, and can quickly respond and work efficiently in complex and variable scenes. The GNSS satellite signal and inertial navigation combined positioning technology used in the existing vehicle-mounted mobile measurement system can only achieve high-precision positioning and attitude determination under the condition of open field of view and no obvious influence of surrounding environment. In the complex urban scene, GNSS satellite signal shielding, high-power radio interference such as microwave towers and transmitting antennas, and serious multipath reflection in large-area water, iron sheds and metal areas seriously restrict high-precision positioning and attitude determination. GNSS satellite signal loss causes the inertial navigation and odometer drift to be unable to be corrected, and the positioning accuracy rapidly decreases to several meters to tens of meters, which cannot meet the application requirements.

[0004] The model construction of the basic geographic entity in the real three-dimensional construction requires collecting high-resolution and high-overlapping images to realize the joint modeling of space and ground images, so as to construct the real model of the street road and the landscape and building on both sides, and express the vertical information of the road and the landscape and building on both sides of the street scene. The existing vehicle-mounted mobile measurement system generally uses a panoramic camera to collect images. The panoramic camera can realize 360° horizontal field of view angle image collection and splicing, and is used for true color coloring of point cloud data. However, due to the low pixel resolution and obvious distortion of panoramic images (or segmented single-lens images), it cannot be used for real three-dimensional modeling. If a digital camera is mounted on the vehicle-mounted mobile measurement system, high-pixel resolution images can be collected, but the field of view angle of the digital camera lens is small, and it is difficult to realize high-overlapping of the front and rear frame images during the movement of the vehicle-mounted platform, resulting in modeling failure. On the other hand, the digital camera and the laser radar are not rigidly connected, and the installation angle of the digital camera differs in each installation process, so the automatic calibration of the digital camera is difficult. SUMMARY

[0005] The main purpose of the present application is to provide a real three-dimensional model construction method, device and vehicle-mounted mobile measurement system based on a vehicle-mounted mobile measurement system, aiming at solving the technical problem that the current real three-dimensional model construction effect is poor.

[0006] To achieve the above-mentioned purpose, the present application provides a real three-dimensional model construction method based on a vehicle-mounted mobile measurement system. The vehicle-mounted mobile measurement system comprises: an inertial navigation system, a GNSS receiver, an odometer, a panoramic camera, a plurality of digital cameras, a plurality of three-dimensional laser radars, a positioning antenna and a synchronous controller. The odometer is installed on the wheel, the plurality of digital cameras are installed around the vehicle, the plurality of three-dimensional laser radars are installed in front of and on both sides of the vehicle, and the panoramic camera and the plurality of three-dimensional laser radars are rigidly connected. The real three-dimensional model construction method based on the vehicle-mounted mobile measurement system comprises: After the time synchronization of the inertial navigation system, the GNSS receiver, the odometer, the panoramic camera, the plurality of three-dimensional laser radars, the plurality of digital cameras and the positioning antenna is completed, the acceleration and angular velocity are collected by the inertial navigation system; The mileage information of the vehicle forward movement is collected based on the odometer; The first position and the first attitude of the vehicle carrier which are not optimized are calculated according to the acceleration, the angular velocity and the mileage information; The first position and the first attitude are optimized based on the three-dimensional laser radar, and the optimized pose information is obtained; The geographic coordinates and the panoramic image pose of the three-dimensional point cloud data are calculated according to the optimized pose information; The three-dimensional point cloud data geographical coordinates are processed through the panoramic image pose to obtain target three-dimensional point cloud data, and a digital camera image pose is calculated through the panoramic image pose; A real scene three-dimensional model is constructed through the target three-dimensional point cloud data and the digital camera image pose.

[0007] In an embodiment, the step of calculating the first position and the first attitude of the vehicle carrier which are not optimized according to the acceleration, the angular velocity and the mileage information comprises: The positioning antenna and the GNSS receiver are controlled based on the inertial navigation to perform loop tracking to obtain navigation satellite pseudo-range and carrier phase observation data; The navigation satellite pseudo-range and carrier phase observation data are subjected to navigation solution to obtain geographical coordinate system positioning; The inertial navigation is corrected based on the geographical coordinate system positioning to obtain a corrected inertial navigation, and a first corrected acceleration and a first corrected angular velocity collected by the corrected inertial navigation are obtained; The first corrected acceleration, the first corrected angular velocity and the mileage information are subjected to tight combination filtering estimation to obtain the first position and the first attitude of the vehicle carrier which are not optimized.

[0008] In an embodiment, the plurality of three-dimensional laser radars comprises a first laser radar; The step of optimizing the first position and the first attitude based on the three-dimensional laser radar to obtain the optimized pose information comprises: The acceleration and the angular velocity accumulated over time by the inertial navigation are corrected through the first position and the first attitude to obtain a second corrected acceleration and a second corrected angular velocity; First laser point cloud data are collected through the first laser radar; The first laser point cloud data are subjected to feature matching to obtain a vehicle motion state; The second corrected acceleration and the second corrected angular velocity are optimized in loose combination filtering through the vehicle motion state to obtain the optimized pose information.

[0009] In an embodiment, the plurality of three-dimensional laser radars comprises a second laser radar, and the number of laser beams scanned by the second laser radar is greater than the number of laser beams scanned by the first laser radar; The step of calculating the three-dimensional point cloud data geographical coordinates and the panoramic image pose according to the optimized pose information comprises: Second laser three-dimensional point cloud data are collected through the second laser radar; The second laser three-dimensional point cloud data are subjected to multi-return elimination and noise removal to obtain processed second laser three-dimensional point cloud data; The processed second laser three-dimensional point cloud data is subjected to feature extraction and semantic processing by a deep learning algorithm to obtain geometric information and spatial structure information of the building object in the three-dimensional scene. The geographic coordinates of the three-dimensional point cloud data are calculated by the optimized pose information, the geometric information and the spatial structure information. The panoramic image collected by the panoramic camera is acquired. The time stamp of each frame of the panoramic image is aligned with the time sequence of the optimized pose information, and the optimized pose information before and after the panoramic image time stamp is subjected to interpolation calculation to obtain the panoramic image pose.

[0010] In an embodiment, the step of obtaining the geometric information and the spatial structure information of the building object in the three-dimensional scene by the deep learning algorithm to extract features from the processed second laser three-dimensional point cloud data includes: The point cloud features are obtained by extracting features from the processed second laser three-dimensional point cloud data by a deep learning algorithm. The points or grid elements in the three-dimensional scene are semantically labeled according to the point cloud features to obtain different semantic categories. The regions of different semantic categories are segmented by a deep learning model to obtain segmented regions of different semantic categories. The geometric information and the spatial structure information of the building object in the three-dimensional scene are determined by the segmented regions of different semantic categories.

[0011] In an embodiment, the step of processing the geographic coordinates of the three-dimensional point cloud data by the panoramic image pose to obtain target three-dimensional point cloud data includes: The conversion relationship between the panoramic image coordinate system and the three-dimensional point cloud data coordinate system is acquired. The panoramic image coordinate conversion is performed according to the conversion relationship, the panoramic image pose and the geographic coordinates of the three-dimensional point cloud data to color the three-dimensional point cloud data by the panoramic image to obtain target three-dimensional point cloud data.

[0012] In an embodiment, the plurality of digital cameras includes a first group of digital cameras and a second group of digital cameras, the number of cameras in the first group of digital cameras and the second group of digital cameras is the same, and the first group of digital cameras and the second group of digital cameras alternately shoot. Before the step of calculating the digital camera image pose by the panoramic image pose, it further includes: The first group of digital cameras and the second group of digital cameras are controlled to alternately collect images, and during the collection process, the installation distance of the cameras along the vehicle advancing direction, the camera field of view angle, the outward rotation angle, the imaging size, the collection distance and the collection vehicle speed are acquired. calculating an image horizontal coverage value according to the camera field of view angle, the outward rotation angle and the collection distance; calculating an image vertical coverage value according to the image horizontal coverage value and the imaging size; calculating an overlap degree between the first group of digital cameras and the second group of digital cameras according to the image horizontal coverage value, the image vertical coverage value, the installation distance and the collection vehicle speed; when the overlap degree is less than a preset overlap degree, reducing the collection vehicle speed and marking an image corresponding to the preset overlap degree; controlling the first group of digital cameras and the second group of digital cameras to alternately collect images through the reduced collection vehicle speed.

[0013] In an embodiment, the step of calculating the digital camera image pose through the panoramic image pose comprises: obtaining multiple frames of panoramic images through the panoramic image pose; obtaining digital camera images alternately collected by the first group of digital cameras and the second group of digital cameras; coarsely registering multiple frames of the panoramic images and multiple frames of the digital camera images according to time stamps, extracting first feature points in the panoramic images and corresponding second feature points in the digital camera images; matching the first feature points and the second feature points through a feature matching algorithm to obtain a first matching feature point pair; obtaining a point cloud feature point corresponding to a second feature point of the digital camera according to a conversion relationship between a panoramic image coordinate system and a three-dimensional point cloud data coordinate system and the first matching feature point pair; obtaining a second matching feature point pair according to the second feature point and the corresponding point cloud feature point; calculating a transformation matrix from a digital image coordinate system to a three-dimensional point cloud data coordinate system according to the second matching feature point pair; obtaining a digital camera image pose through the transformation matrix.

[0014] In addition, to achieve the above object, the application further provides a real scene three-dimensional model construction device based on a vehicle-mounted mobile measurement system, which comprises: a time synchronization module, configured to collect acceleration and angular velocity through an inertial navigation system after time synchronization of the inertial navigation system, a GNSS receiver, an odometer, a panoramic camera, multiple three-dimensional laser radars, multiple digital cameras and a positioning antenna is completed; a collection module, configured to collect mileage information of vehicle advancement based on the odometer; a calculation module configured to calculate a first position and a first attitude of a vehicle carrier that are not optimized according to the acceleration, the angular velocity, and the mileage information; an optimization module configured to optimize the first position and the first attitude based on the three-dimensional laser radar to obtain optimized pose information; The calculation module is further configured to calculate three-dimensional point cloud data geographical coordinates and panoramic image pose according to the optimized pose information. A processing module is configured to process the three-dimensional point cloud data geographical coordinates through the panoramic image pose to obtain target three-dimensional point cloud data, and calculate a digital camera image pose through the panoramic image pose. A construction module is configured to construct a real scene three-dimensional model through the target three-dimensional point cloud data and the digital camera image pose.

[0015] In addition, to achieve the above-mentioned purpose, the application further provides a vehicle-mounted mobile measurement system, which comprises an inertial navigation system, a GNSS receiver, an odometer, a panoramic camera, a plurality of digital cameras, a plurality of three-dimensional laser radars, a positioning antenna, and a synchronous controller.

[0016] In addition, to achieve the above-mentioned purpose, the application further provides a storage medium, which is a computer-readable storage medium, and the storage medium stores a computer program.

[0017] In addition, to achieve the above-mentioned purpose, the application further provides a computer program product, which comprises a computer program.

[0018] The method of the application has the following beneficial effects relative to the prior art: 1) The application designs a vehicle-mounted mobile measurement system and method for real scene three-dimensional construction. 2) The application can more accurately estimate the initial pose (position and attitude) of the vehicle by synchronously collecting acceleration, angular velocity and vehicle forward information, making up for the shortcomings of a single sensor in complex environments. Based on the optimized pose, the three-dimensional point cloud geographic coordinates are calculated: to ensure that the generated point cloud data has real geographic location information, providing reliable geographic reference for subsequent modeling. Process point cloud data in combination with panoramic image pose: further enhance spatial consistency, making point cloud data more consistent with the actual scene structure.

[0019] 3) By obtaining the conversion relationship between the panoramic image coordinate system and the three-dimensional point cloud data coordinate system; and according to the conversion relationship, the panoramic image pose and the three-dimensional point cloud data geographic coordinates, the panoramic image coordinate conversion is performed to color the three-dimensional point cloud data through the panoramic image, so as to realize the true color attribute assignment of the three-dimensional point cloud data, and realize efficient collection and processing of true color three-dimensional laser point cloud data with high precision pose attribute and high resolution and high overlap image data in complex scenes; 4) The image and three-dimensional point cloud collected and processed by the laser radar both have high precision pose attribute, and the digital camera is set to alternately collect digital camera images. The image collected and processed by the digital camera image grouping has the characteristics of high resolution and high overlap, which can truly reflect the street scene road and the landscape and building facade information on both sides, realize the two-dimensional or three-dimensional expression of the three-dimensional model to the real world, facilitate flexible operation of the operator, and meet the urgent application needs of real scene three-dimensional construction data collection and processing. BRIEF DESCRIPTION OF DRAWINGS

[0020] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present application and, together with the specification, serve to explain the principles of the application.

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0022] Figure 1 The flowchart provided by the first embodiment of the real scene three-dimensional model construction method based on the vehicle-mounted mobile measurement system of the present application is shown in the figure; Figure 2 The structure diagram of the vehicle-mounted mobile measurement system is shown in the figure; Figure 3 The flowchart provided by the second embodiment of the real scene three-dimensional model construction method based on the vehicle-mounted mobile measurement system of the present application is shown in the figure; Figure 4A flowchart provided for the third embodiment of the real scene three-dimensional model construction method based on the vehicle-mounted mobile measurement system of the present application; Figure 5 A brief flowchart provided for the first embodiment of the real scene three-dimensional model construction method based on the vehicle-mounted mobile measurement system. Figure 6 A module structure diagram of the real scene three-dimensional model construction device based on the vehicle-mounted mobile measurement system of the present application.

[0023] Legend of reference numerals: positioning antenna 4, synchronization controller 5; Panoramic camera Q1, first group of digital cameras A1-A3, second group of digital cameras B1-B3, first laser radar L2, second laser radar L1.

[0024] The purposes, functional features and advantages of the present application will be further explained with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0025] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application, and are not used to limit the present application.

[0026] In order to better understand the technical solutions of the present application, the specific embodiments will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0027] The main solution of the embodiments of the present application is: after the time synchronization of inertial navigation, GNSS receiver, odometer, panoramic camera, multiple three-dimensional laser radars, multiple digital cameras and positioning antenna is completed, the acceleration and angular velocity are collected by the inertial navigation; the mileage information of the vehicle forward is collected based on the odometer; the first position and the first attitude of the vehicle carrier which are not optimized are calculated according to the acceleration, the angular velocity and the mileage information; the first position and the first attitude are optimized based on the three-dimensional laser radar, and the optimized pose information is obtained; the three-dimensional point cloud data geographical coordinates and the panoramic image pose are calculated according to the optimized pose information; the target three-dimensional point cloud data is obtained by processing the three-dimensional point cloud data geographical coordinates through the panoramic image pose, and the digital camera image pose is calculated through the panoramic image pose; the real scene three-dimensional model is constructed through the target three-dimensional point cloud data and the digital camera image pose.

[0028] The existing technology adopts a GNSS satellite signal and an inertial navigation combined positioning technology, which can only realize high-precision positioning and pose determination under the condition that the field of view is wide and the surrounding environment has no obvious influence. In a complex urban scene, GNSS satellite signals are blocked, high-power radio interference such as microwave towers and transmitting antennas affects, and multipath reflection is serious in large areas of water, iron sheds, metal areas and the like, which seriously restricts high-precision positioning and pose determination. The GNSS satellite signal loses lock, which leads to the inability to correct the inertial navigation and the drift of the odometer, and the positioning accuracy rapidly decreases to several meters to tens of meters, which cannot meet the application requirements.

[0029] The present application provides a solution, which designs a vehicle-mounted mobile measurement system and method for real three-dimensional construction. Highly integrated inertial navigation, positioning antenna and receiver, odometer, panoramic camera, digital camera, three-dimensional laser radar and other sensors are used to realize automatic calibration of targetless laser radar-digital camera, and high-precision pose attribute true color three-dimensional laser point cloud data and high-resolution, high-overlap image data with efficient acquisition and processing in complex scenes can be realized. The system platform device adopts intelligent modular design integration to facilitate flexible operation of the operator, and meets the urgent application requirements of real three-dimensional construction data acquisition and processing.

[0030] It should be noted that the execution subject of the present embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone and the like, or an electronic device, a vehicle-mounted mobile measurement system and the like capable of realizing the above functions. The vehicle-mounted mobile measurement system is taken as an example to describe the present embodiment and the following embodiments.

[0031] Based on this, the present application provides a real three-dimensional model construction method based on a vehicle-mounted mobile measurement system, which is described with reference to Figure 1 , Figure 1 The present application provides a real three-dimensional model construction method based on a vehicle-mounted mobile measurement system.

[0032] In the present embodiment, as shown in Figure 2 , Figure 2A schematic diagram of a vehicle-mounted mobile measurement system, the vehicle-mounted mobile measurement system comprising: an inertial navigation system (not shown in the figure), a GNSS receiver (not shown in the figure), an odometer (not shown in the figure), the inertial navigation system, the GNSS receiver and the odometer being arranged below a vehicle body, a panoramic camera Q1, a plurality of digital cameras, the plurality of digital cameras comprising digital cameras A1, A2, A3, B1, B2 and B3, a plurality of three-dimensional laser radars, the plurality of three-dimensional laser radars comprising a first laser radar L2 and a second laser radar L1, a positioning antenna 4 and a synchronization controller 5, wherein the odometer is mounted on a vehicle wheel, the plurality of digital cameras are mounted around the vehicle, the plurality of three-dimensional laser radars are mounted at the front and both sides of the vehicle, and the panoramic camera Q1 and the plurality of three-dimensional laser radars are rigidly connected; it should be noted that the digital cameras A1-A3 and B1-B3 are arranged on both sides of the vehicle.

[0033] In the embodiment, the real scene three-dimensional model construction method based on the vehicle-mounted mobile measurement system comprises steps S10-S70. Step S10: after time synchronization of the inertial navigation system, the GNSS receiver, the odometer, the panoramic camera, the plurality of three-dimensional laser radars, the plurality of digital cameras and the positioning antenna is completed, acceleration and angular velocity are collected by the inertial navigation system.

[0034] It should be noted that the vehicle-mounted mobile measurement system further comprises a time-frequency signal driving device, and the time-frequency signal driving device and the synchronization controller 5 constitute a time synchronization function module. Before real scene three-dimensional model construction, in order to improve the accuracy of data acquisition, all sensors can be time-synchronized first, and therefore the time synchronization function module constituted by the time-frequency signal driving device and the synchronization controller 5 can be used to time synchronize the inertial navigation system, the GNSS receiver, the odometer, the panoramic camera, the plurality of three-dimensional laser radars, the plurality of digital cameras and the positioning antenna, specifically, each functional module sensor is triggered first, and after each frame of image of the panoramic camera is collected, the digital camera is triggered by the synchronization controller to collect digital images in groups, and a return time stamp of each sensor is recorded, the return time stamp is checked, invalid time stamps are removed, and time synchronization is completed.

[0035] In a specific implementation, after time synchronization of each sensor is completed, subsequent positioning and attitude determination can be performed, specifically, the inertial navigation system is a high-precision inertial navigation system, the number of laser beams scanned between the second laser radar and the first laser radar is different, for example, the first laser radar is a 16-line / second laser radar, and the first laser radar is a 250-line / second laser radar. The positioning and attitude determination mainly comprises and is realized by one set of high-precision inertial navigation system, one set of positioning antenna and receiver, one set of odometer and 16-line / second laser radar, and is used for positioning and attitude determination of a vehicle carrier.

[0036] Therefore, after time synchronization is completed, 200Hz high-frequency acceleration and angular velocity information can be collected by the inertial navigation, so that the position and attitude of the inertial navigation sensor can be calculated.

[0037] Step S20: collecting mileage information of the vehicle based on the odometer.

[0038] It should be noted that during the driving of the vehicle carrier, the odometer wheel speed device counts and collects the mileage of the vehicle, smooths the vehicle motion trajectory, so as to obtain the mileage information of the vehicle.

[0039] Step S30: calculating the first position and the first attitude of the vehicle carrier which are not optimized according to the acceleration, the angular velocity and the mileage information.

[0040] It should be understood that the position and the attitude of the vehicle carrier can be calculated according to the acceleration, the angular velocity and the mileage information by a fusion algorithm such as Kalman filtering or particle filtering, so as to obtain the first position and the first attitude of the vehicle carrier which are not optimized. The acceleration and the angular velocity information reflect the dynamic changes of the vehicle carrier, and the mileage information provides the absolute displacement information of the vehicle carrier during driving, and the combination of the three can improve the positioning and attitude determination accuracy.

[0041] In a feasible implementation, step S30 can include steps A11-A14: Step A11: controlling the positioning antenna and the GNSS receiver to perform loop tracking based on the inertial navigation, so as to obtain pseudorange and carrier phase observation data of navigation satellites; It should be noted that the inertial navigation can assist the positioning antenna and the GNSS receiver to perform loop tracking. In the vehicle-mounted mobile measurement system, the inertial navigation is one of the core sensors, which can provide real-time acceleration and angular velocity information of the vehicle carrier. These information are used to assist the positioning antenna and the GNSS receiver to perform accurate loop tracking. Loop tracking is a key step in GNSS navigation, which involves receiving and processing the signals transmitted by navigation satellites to obtain pseudorange and carrier phase observation data of the satellites. These data are the basis for subsequent navigation calculation and positioning correction.

[0042] Step A12: performing navigation calculation on the pseudorange and carrier phase observation data of the navigation satellites, so as to obtain positioning in a geographic coordinate system; In a specific implementation, after obtaining the pseudorange and carrier phase observation data of the navigation satellites, the vehicle-mounted mobile measurement system performs navigation calculation using these data. Navigation calculation is a complex process, which involves multiple steps such as processing of observation data, correction of errors and conversion of coordinate systems. Finally, navigation calculation outputs the position information of the vehicle carrier in the geographic coordinate system, which is the key to real-time positioning of the vehicle carrier.

[0043] The navigation solution can specifically include: (1) Data preprocessing: detecting and repairing cycle slip of raw observation data output by the GNSS receiver, and eliminating the influence of multipath effect.

[0044] A ionosphere-free combination observation equation is constructed using dual-frequency observation values: P_{IF} =frac{f_1^2 P_1 - f_2^2 P_2}{f_1^2 - f_2^2} Wherein, P_1 、 P_2 are L1 and L2 frequency pseudorange observation values, f_1 and f_2 are corresponding frequencies; (2) Error correction: ionospheric delay: eliminated by Klobuchar model or dual-frequency observation values; tropospheric delay: corrected by Saastamoinen model; satellite orbit error: compensated by precise ephemeris data; receiver clock error: solved by observation equation adjustment; (3) Positioning solution: construct a pseudorange observation equation: rho =sqrt{(X_{sv}-X)^2 + (Y_{sv}-Y)^2 + (Z_{sv}-Z)^2} + c·δt +ε Wherein, (X_{sv},Y_{sv},Z_{sv}) is the satellite coordinate, (X,Y,Z) is the GNSS receiver coordinate, δt is the clock error, ε is the residual term, and c is the speed of light; (4) Coordinate conversion: the positioning result in the WGS84 coordinate system is solved by the least square method, and then converted into the local geographic coordinate system by Gauss projection.

[0045] The geographic coordinate system positioning is obtained by navigation solution of the navigation satellite pseudorange and carrier phase observation data, the inertial navigation output 200Hz high frequency pose data, and the GNSS navigation satellite data solution can provide absolute geographic coordinate system positioning and correct the inertial navigation drift.

[0046] Step A13: correcting the inertial navigation based on the geographic coordinate system positioning to obtain the corrected inertial navigation, and acquiring the first corrected acceleration and the first corrected angular velocity collected by the corrected inertial navigation; Since the inertial navigation may produce cumulative error after a long time of running, it needs to be corrected by external information.

[0047] In this embodiment, the inertial navigation is corrected by using the geographic coordinate system positioning information. The corrected inertial navigation will have higher precision and stability, and can more accurately reflect the motion state of the vehicle carrier. At the same time, the corrected inertial navigation will collect the first corrected acceleration and the first corrected angular velocity, which will be used for subsequent tightly coupled filtering estimation.

[0048] Step A14: performing tightly coupled filter estimation on the first corrected acceleration, the first corrected angular velocity, and the odometry information to obtain a first unoptimized position and a first attitude of the vehicle carrier.

[0049] Tightly coupled filter estimation is an advanced data processing method that can fuse multiple sensor data to improve the accuracy and stability of positioning.

[0050] In this embodiment, the first corrected acceleration, the first corrected angular velocity, and the odometry information are subjected to tightly coupled filter estimation. In this way, the complementarity of various sensor data can be fully utilized to improve the accuracy of vehicle carrier positioning. Ultimately, the tightly coupled filter estimation will output the first unoptimized position and the first attitude of the vehicle carrier, which will be used for subsequent optimization and real scene three-dimensional model construction.

[0051] This embodiment designs a joint processing method of tightly coupled + loosely coupled. The inertial navigation output is 200Hz high-frequency attitude data, GNSS navigation satellite data solution can provide absolute geographic coordinate system positioning and correct inertial navigation drift, and the odometer can prevent the jump of the moving track of the vehicle-mounted system and keep the track smooth. Through the tightly coupled filter, the inertial navigation, GNSS, and odometer raw data are fused to calculate the unoptimized positioning and attitude data and the variance covariance matrix representing the precision.

[0052] Through the above steps, real-time and accurate positioning and attitude calculation of the vehicle carrier can be realized, which provides strong support for subsequent real scene three-dimensional model construction.

[0053] Step S40: optimizing the first position and the first attitude based on the three-dimensional laser radar to obtain optimized attitude information.

[0054] In specific implementation, laser point cloud data can be collected by the three-dimensional laser radar, so as to estimate the vehicle motion state according to the collected laser point cloud data, and further optimize the vehicle position and attitude information in the loosely coupled filter according to the vehicle motion state, improve the attitude information precision in complex scenes, and output the optimized high-frequency high-precision attitude information.

[0055] In a feasible implementation manner, step S40 can include steps A21-A24: The plurality of three-dimensional laser radars includes a first laser radar, and the first laser radar is a 16-line / second laser radar.

[0056] Step A21: correcting the acceleration and angular velocity accumulated over time of the inertial navigation through the first position and the first attitude to obtain a second corrected acceleration and a second corrected angular velocity; It should be understood that, since there is a deviation in inertial navigation, which will cause positioning deviation, the inertial navigation drift error can be compensated in real time by the first position and the first attitude output by the GNSS / INS tight combination, for example, an error state equation is established: δx_{k+1}=Φ_k·δx_k + w_k, wherein Φ_k is a state transition matrix, and w_k is process noise. Thus, the acceleration and angular velocity drift error accumulated over time by the inertial navigation is corrected by the error state equation, and the corrected acceleration and angular velocity, i.e., the second corrected acceleration and the second corrected angular velocity, are obtained.

[0057] Step A22: acquiring first laser point cloud data by the first laser radar; The first laser radar (16 lines per second) acquires three-dimensional point cloud data at a frequency of 20 Hz, and the point cloud density reaches 1600 points per square meter, so as to obtain the first laser point cloud data.

[0058] Step A23: performing feature matching on the first laser point cloud data to obtain a vehicle motion state; It should be noted that the feature matching can be performed on the point cloud data between frames in the first laser point cloud data, so that the vehicle motion state can be estimated. Specifically, a feature extraction method based on curvature value can be used to extract ground marker corner points and plane features from the point cloud, and a feature matching constraint condition is constructed: Σ(θ_i·(n_i^T·(R·p_i + t - q_i)))^2<ε1, wherein n_i is a normal vector, θ_i is a weight coefficient, and ε1 is a matching threshold or error tolerance. The feature matching constraint condition is used to perform feature matching on the point cloud data between frames, so that the pixel displacement and motion trajectory of the same physical feature point in the continuous image frames are calculated by tracking the position change of the same physical feature point, the translation, rotation speed and direction of the vehicle are solved by using a geometric transformation model (such as rigid body motion and perspective transformation), and thus the overall motion state of the vehicle is inferred.

[0059] Step A24: optimizing the second corrected acceleration and the second corrected angular velocity in loose combination filtering by using the vehicle motion state to obtain optimized pose information.

[0060] In a specific implementation, the vehicle motion state estimated by using the point cloud data can be used to further optimize the vehicle carrier position and attitude information in the loose combination filter, so that, on the basis of obtaining the unoptimized pose by the time synchronization and tight combination filtering, the pose optimization is realized by using the laser radar data. The positioning and attitude data are optimized by the loose combination filtering, so as to improve the reliability in the case of GNSS positioning lock loss in a complex scene. The joint processing method of tight combination + loose combination can maintain high precision of positioning and attitude, and at the same time, the high complexity of laser radar data processing is considered, the real-time processing efficiency is ensured, and the high-precision pose information of the vehicle system in a complex scene can be obtained.

[0061] Step S50: Calculate the geographic coordinates of the three-dimensional point cloud data and the panorama image pose based on the optimized pose information.

[0062] It should be noted that the point cloud data is a collection of points in three-dimensional space obtained by sensors such as LiDAR or stereo vision. According to the optimized pose information, the coordinates of these points can be converted from the local coordinate system of the sensor to the global geographic coordinate system (such as the Earth coordinate system or the projection coordinate system). The purpose of this is to map the three-dimensional point cloud data to the real world location for subsequent processing.

[0063] Panoramic images are usually formed by taking a series of images with a camera and stitching them together. The optimized pose information also helps to determine the shooting position and orientation of the panoramic image, i.e. the pose of the image. This information is crucial for the subsequent registration of images and three-dimensional point cloud data.

[0064] Step S60: Process the geographic coordinates of the three-dimensional point cloud data through the panorama image pose to obtain the target three-dimensional point cloud data, and calculate the digital camera image pose through the panorama image pose.

[0065] By correlating the panorama image pose and the geographic coordinates of the three-dimensional point cloud data, geometric registration is performed. That is, according to the pose information of the panoramic image, it needs to be combined with the three-dimensional point cloud data to ensure that these data are in the same spatial reference frame. For example, if the coordinates of the point cloud data are in a certain position, while the panoramic image is taken in another position, through calibration and coordinate conversion, they can be corresponded to the same scene. After processing the panorama image pose, a calibrated, adjusted and colored three-dimensional point cloud data is obtained. These point clouds may be filtered, denoised, registered and colored, etc. to generate true color three-dimensional point cloud data, which can better represent the three-dimensional spatial structure of the real world.

[0066] In specific implementation, the pose information of the panoramic image can be used to further calculate the position and orientation of the digital camera when shooting. This is to correspond the shooting information of the camera with the three-dimensional scene, so that a real scene model can be constructed by combining images and three-dimensional point clouds.

[0067] Step S70: Construct a real scene three-dimensional model through the target three-dimensional point cloud data and the digital camera image pose.

[0068] A real scene three-dimensional model is created by combining target three-dimensional point cloud data and digital camera image poses. The three-dimensional point cloud provides spatial geometric information of the scene, while the digital camera image pose provides corresponding image texture information. The point cloud data describes the spatial positions of various points in the scene, which form the geometric shape of the scene. The pose of the digital camera image helps determine the position and direction of the image in three-dimensional space.

[0069] In combination with point cloud data and image pose, some point cloud rendering and texture mapping techniques are usually used to project the texture information of the image onto the point cloud data. In this way, the generated three-dimensional model not only has a geometric shape, but also reflects the real visual effect and presents rich details.

[0070] The embodiment provides a real scene three-dimensional model construction method based on a vehicle-mounted mobile measurement system. By time-synchronized acquisition of acceleration, angular velocity and vehicle forward information, the initial pose (position and attitude) of the vehicle can be more accurately estimated, making up for the shortcomings of a single sensor in complex environments. Based on the optimized pose, three-dimensional point cloud geographic coordinates are calculated: ensuring that the generated point cloud data has real geographic position information, providing reliable geographic reference for subsequent modeling. Process the point cloud data in combination with the panoramic image pose: further enhance the spatial consistency, so that the point cloud data is more in line with the actual scene structure. The digital camera image pose is derived from the panoramic image: ensuring the spatio-temporal consistency between the image and the three-dimensional point cloud, avoiding geometric distortion caused by asynchronization. Joint modeling of target three-dimensional point cloud and image pose: realizing accurate matching of texture and geometry, and constructing a high-quality, high-realism real scene three-dimensional model.

[0071] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as the above embodiment one can refer to the above introduction, and the subsequent will not be described in detail. On this basis, please refer to Figure 3 , step S50 includes steps S501-S506: In this embodiment, the three-dimensional laser radar includes a second laser radar, and the second laser radar is a 250 line / s laser radar. The number of laser beams scanned by the second laser radar is greater than the number of laser beams scanned by the first laser radar.

[0072] Step S501: acquiring second laser three-dimensional point cloud data through the second laser radar.

[0073] It should be noted that the main function of the second laser radar is point cloud processing. High-density three-dimensional point cloud data, i.e., second laser three-dimensional point cloud data, can be acquired through the second laser radar.

[0074] Step S502: performing multi-return elimination and noise removal on the second laser three-dimensional point cloud data to obtain processed second laser three-dimensional point cloud data.

[0075] In a specific implementation, after the second laser three-dimensional point cloud data is acquired, the acquired point cloud data can be subjected to multi-echo elimination and noise removal, so as to improve the accuracy of the acquired point cloud data.

[0076] Since the laser beams emitted by the laser radar can be reflected by multiple objects, multiple reflection signals (echoes) can be generated. The multi-echo elimination technique is used to screen out valid echoes from different echoes and remove interference echoes (such as reflections of background or moving objects) from unrelated objects. Generally, the radar selects the strongest echo or the earliest echo, depending on the actual application. The point cloud data can contain some errors or irregular noise, such as erroneous data caused by environmental interference, sensor failure, etc. Noise removal is to remove useless data points by algorithm, and only information reflecting the real environment is retained.

[0077] Through multi-echo elimination and noise removal, more accurate point cloud data that removes noise and invalid echoes is obtained, which is more consistent with the actual physical environment.

[0078] Step S503: performing feature extraction and semantic processing on the processed second laser three-dimensional point cloud data by a deep learning algorithm to obtain geometric information and spatial structure information of the building object in the three-dimensional scene.

[0079] The deep learning algorithm (such as convolutional neural network CNN, PointNet, PointNet++, etc.) can be applied to three-dimensional point cloud data to automatically identify and extract features. Through training, the algorithm can identify different objects in the point cloud data, such as buildings, roads, trees, etc.

[0080] Therefore, the processed second laser three-dimensional point cloud data can be subjected to feature extraction and semantic processing by a deep learning algorithm, and the building in the three-dimensional scene can be subjected to feature extraction, automatic semantic recognition and classification based on the deep learning method, so as to automatically extract the geometric information and spatial structure information of the building object in the three-dimensional scene.

[0081] In a feasible implementation, step S503 can include steps B11-B14: Step B11: performing feature extraction on the processed second laser three-dimensional point cloud data by a deep learning algorithm to obtain point cloud features; In a specific implementation, the unordered laser point cloud can be subjected to feature extraction based on the deep learning method of the PointNet, PointNet++ point cloud processing network to obtain point cloud features.

[0082] The point cloud features include geometric features and spatial structure features. The geometric features reflect the geometric shape and local structure of the point cloud, expressing the local features of the point cloud. The spatial structure features reflect the building topology and hierarchy in the three-dimensional scene.

[0083] Step B12: semantically labeling points or grid cells in the three-dimensional scene according to the point cloud features, to obtain different semantic categories; It should be noted that each point or grid cell in the three-dimensional scene can be semantically labeled according to the feature information of the point cloud features, and classified into different semantic categories, such as buildings, roads, vegetation, etc. The purpose of semantic processing is to assign labels to each point or point cloud region and define their specific meanings (such as walls, windows, doors, ceilings, etc.), thereby obtaining semantic information of buildings or other objects in the environment.

[0084] Step B13: segmenting regions of different semantic categories by a deep learning model to obtain segmented regions of different semantic categories; It can be understood that the deep learning model can be trained by training a large number of labeled data sets, and the regions of different semantic categories can be automatically recognized and segmented by the deep learning model, thereby obtaining the segmented regions of different semantic categories, and the classification performance can also be evaluated according to the semantic segmentation index and the instance segmentation index.

[0085] Step B14: determining the geometric information and spatial structure information of the building object in the three-dimensional scene through the segmented regions of different semantic categories.

[0086] It should be noted that the segmented regions allow each semantic category region to be analyzed individually, thereby extracting its geometric information. For example, the shape, size, and relative position of the walls, windows, and roof of a building. The geometric information usually includes the spatial position, size, angle, etc. of the point cloud, which can accurately describe the shape of the building. The spatial structure information describes the layout and relationship of buildings or other objects in three-dimensional space. It includes the relative position, direction, and distance between objects. By analyzing the relative positions of different semantic category regions, the system can understand the spatial structure of the building, such as knowing the relationship between the walls and windows, the size and layout of the rooms, etc.

[0087] Step S504: calculating the geographic coordinates of the three-dimensional point cloud data based on the optimized pose information, the geometric information, and the spatial structure information.

[0088] The pose includes the spatial position (coordinates) and orientation (direction) of the camera or radar. Based on the optimized pose information and geometric structure data, the point cloud data can be converted from a local coordinate system to a global geographic coordinate system (such as the WGS-84 coordinate system), so that the actual position of the three-dimensional point cloud on the earth can be obtained, i.e. the geographic coordinates of the three-dimensional point cloud data.

[0089] Step S505: Obtain the panoramic image captured by the panoramic camera.

[0090] The panoramic camera captures images from multiple perspectives and stitches them into a complete 360-degree panoramic image. In this step, the image captured by the panoramic camera is obtained, usually covering a 360-degree view of the scene, for subsequent image and three-dimensional data fusion. For example, the panoramic camera takes pictures at a certain distance and stitches the 6-lens image into a 360° panoramic image.

[0091] Step S506: Align the timestamp of each frame of the panoramic image with the time sequence of the optimized pose information, and perform interpolation calculation on the optimized pose information before and after the timestamp of the panoramic image to obtain the panoramic image pose.

[0092] Each frame of the panoramic image has a corresponding timestamp, indicating that it was taken at a certain time. The optimized pose information also has a timestamp, indicating the position and orientation of the sensor at a certain time point. In order to accurately align the panoramic image with the three-dimensional point cloud data, the timestamps of the panoramic image and the pose data need to be matched. By aligning the timestamps, it can be ensured that the image and pose data are synchronized at the same time.

[0093] The time synchronization function module realizes the unification of the time reference between the positioning and pose determination function module and the panoramic camera, so that the 200Hz high-precision pose time sequence can be aligned according to the timestamp of each frame of the panoramic image, and the high-precision pose information before and after the timestamp of the panoramic image is used for interpolation calculation. The interpolation calculation method is spline interpolation. Since the timestamps of the panoramic image and the pose information may not match completely, there may be a time deviation. In order to obtain accurate panoramic image pose, the pose information between the previous and subsequent time points needs to be calculated through a difference algorithm (such as linear interpolation), so as to calculate the accurate pose at a specific time point. This process ensures that the camera position and direction at the time of image capture can be accurately reflected when the image and point cloud data are fused.

[0094] The second laser radar collects second laser three-dimensional point cloud data; multi-echo elimination and noise removal are performed on the second laser three-dimensional point cloud data to obtain processed second laser three-dimensional point cloud data; feature extraction and semantic processing are performed on the processed second laser three-dimensional point cloud data through a deep learning algorithm to obtain geometric information and spatial structure information of a building object in a three-dimensional scene; geographical coordinates of three-dimensional point cloud data are calculated through the optimized pose information, the geometric information, and the spatial structure information; panoramic images collected by the panoramic camera are obtained; the time stamp of each frame of the panoramic images is aligned with the time sequence of the optimized pose information, and the optimized pose information before and after the panoramic image time stamp is calculated by interpolation to obtain panoramic image pose. By combining laser radar data, a deep learning algorithm, panoramic images, and optimized pose information, a three-dimensional scene model is accurately constructed. Laser radar provides detailed three-dimensional point cloud data, deep learning can identify objects such as buildings in the scene, and panoramic images provide visual texture for the model. Through accurate pose calibration, all data can be finally fused to generate an accurate three-dimensional geographical model.

[0095] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as the above-mentioned first embodiment can be referred to the above introduction, and will not be described in detail. On this basis, please refer to Figure 4 , step S60 includes steps S601-S610: Step S601: Obtain the conversion relationship between the panoramic image coordinate system and the three-dimensional point cloud data coordinate system.

[0096] It should be noted that the panoramic camera and the laser sensor are rigidly connected, so the conversion relationship between the panoramic image coordinate system and the three-dimensional point cloud data coordinate system is fixed. The calibration relationship can be reused to perform panoramic image coordinate transformation, so the conversion relationship between the panoramic image coordinate system and the three-dimensional point cloud data coordinate system, i.e. the calibration relationship, can be obtained.

[0097] Step S602: Perform panoramic image coordinate conversion according to the conversion relationship, the panoramic image pose, and the three-dimensional point cloud data geographical coordinates to color the three-dimensional point cloud data through the panoramic image to obtain target three-dimensional point cloud data.

[0098] In specific implementation, the conversion relationship, the panoramic image pose, and the three-dimensional point cloud data geographical coordinates can be used to perform coordinate conversion of the panoramic image, so that the panoramic image can be used to color the three-dimensional point cloud data to generate true color three-dimensional point cloud data, i.e. target three-dimensional point cloud data, and realize true color attribute assignment of the three-dimensional point cloud data.

[0099] Step S603: Obtain multiple frames of panoramic images through the panoramic image pose.

[0100] It should be noted that, based on the non-target camera parameter calibration method, the digital camera and the panoramic camera calibration relationship can be constructed through visual feature point matching, based on the calibration relationship between the panoramic camera and the laser of the mobile measurement vehicle, the corresponding relationship or geometric constraint of the connection points is established in an indirect way by constructing the homonymous points between the digital camera image and the point cloud obtained by the laser, and then the transformation matrix of the camera sensor in the reference coordinate system is estimated, therefore, a plurality of panoramic images can be obtained according to the panoramic image pose.

[0101] Step S604: acquiring the digital camera images alternately collected by the first group of digital cameras and the second group of digital cameras.

[0102] It should be noted that the digital cameras in the embodiment are divided into two groups for alternate collection, thereby improving the collection effect, and therefore the digital camera images alternately collected by the first group of digital cameras and the second group of digital cameras can be acquired.

[0103] In a possible implementation, the plurality of digital cameras includes a first group of digital cameras and a second group of digital cameras, the number of cameras in the first group of digital cameras and the second group of digital cameras is the same, and the first group of digital cameras and the second group of digital cameras alternately shoot; It should be noted that, as shown in Figure 2 The integrated digital cameras in the embodiment are 6, and there are 2 on each side of the opposite camera vehicle system, and 2 ground cameras, A1, A2 and A3 are A group cameras, B1, B2 and B3 are B group cameras, which are controlled by a synchronous controller to collect images in groups alternately, so as to ensure that the A group and the B group camera images have high overlap. Taking the A1 and B1 opposite cameras as an example to calculate the image coverage range and overlap, A2 and B2 are the same, A3 and B3 are ground cameras, which are placed at a closer distance, and the collection distance is less than that of the opposite cameras, and the overlap is higher than that of the opposite cameras, therefore, only the opposite cameras are calculated.

[0104] Before the step of calculating the digital camera image pose through the panoramic image pose, it further includes: controlling the first group of digital cameras and the second group of digital cameras to alternately collect images, and acquiring the placement distance of the cameras along the vehicle advancing direction, the camera field of view angle, the outward rotation angle, the imaging size, the collection distance and the collection vehicle speed during the collection process; In a specific implementation, the first group of digital cameras and the second group of digital cameras can be controlled to alternately collect images, and the relevant parameters can be acquired during the collection process, the A1 and B1 cameras are placed at a distance l = 0.6 m along the vehicle advancing direction, the camera field of view angle d1 = 86°, the outward rotation angle d2 = 5°, the imaging size is 7360 pixels * 4912 pixels, the collection distance is L = 10 m, the minimum response time between frames is 1 s, and the collection vehicle speed V during the collection is acquired.

[0105] According to the camera field of view, the outward rotation angle and the collection distance, the image horizontal coverage value is calculated; In a specific implementation, the image horizontal coverage value can be calculated according to the camera field of view, the outward rotation angle and the collection distance, and the image horizontal coverage value = tan(d1 / 2+d2)*L+tan(d1 / 2-d2)*L = 18.919 m.

[0106] According to the image horizontal coverage value and the imaging size, the image vertical coverage value is calculated; In a specific implementation, the image vertical coverage value can be calculated according to the image horizontal coverage value and the imaging size, and the image vertical coverage value = 18.919*4912 / 7360 = 12.626 m, and the imaging size is higher than 36 million pixels.

[0107] According to the image horizontal coverage value, the image vertical coverage value, the installation distance and the collection speed, the overlap between the first group of digital cameras and the second group of digital cameras is calculated; It should be noted that the overlap of A1 relative to B1 can be calculated according to the collection speed, the image horizontal coverage value, the image vertical coverage value and the installation distance, and the calculation is as follows: P = (18.919-((V / 3.6-l)-(tan(d1 / 2+d2)*L-tan(d1 / 2-d2)*L))) / 18.919 = 1-(V-6.8933) / 68.1084.

[0108] When the overlap is less than a preset overlap, the collection speed is reduced, and the image less than the preset overlap is marked; It should be noted that the preset overlap can be set, for example, the overlap is not less than 50%, and when the preset overlap is 50%, the collection speed is 48 km / h.

[0109] Using the relationship between the speed and the overlap, the speed warning value can be set according to different overlap requirements to prompt the operator to reduce the speed. In addition, the relationship between the speed and the image overlap can also be used for camera selection and installation setting. According to the foregoing derivation process, the expression of the image overlap of the vehicle-mounted digital camera is as follows:

[0110] As can be seen from the formula, the image overlap degree depends on the vehicle speed, the camera installation distance, the camera field of view angle, the outward rotation angle, and the camera shooting distance. Considering that the vehicle speed and the camera installation distance are limited by the actual road operation condition and the length of the vehicle carrier device in the forward direction, the camera can also be selected according to the target overlap degree. The vehicle speed and the camera installation distance are preset according to the formula, and then the camera field of view angle, the outward rotation angle, and the camera shooting distance are substituted into the formula to calculate the overlap degree, so as to determine whether the theoretical overlap degree meets the target requirement.

[0111] In a specific implementation, when the overlap degree is less than the preset overlap degree, it indicates that the digital camera image collected at this time may not meet the requirement, and therefore the collection vehicle speed can be reduced, and the image that does not meet the overlap degree requirement can be marked to facilitate manual intervention during modeling.

[0112] The first group of digital cameras and the second group of digital cameras alternately collect images at the reduced collection vehicle speed.

[0113] It can be understood that the first group of digital cameras and the second group of digital cameras alternately collect images at the reduced collection vehicle speed, thereby solving the problem that the traditional method only collects panoramic single image with low resolution, large camera distortion, and small frame-to-frame overlap degree, which cannot be used for real scene three-dimensional model construction.

[0114] Step S605: Coarsely registering a plurality of panoramic images and a plurality of digital camera images according to timestamps, and extracting first feature points in the panoramic images and corresponding second feature points in the digital camera images.

[0115] In a specific implementation, the panoramic images and the digital camera images both have timestamps, and the synchronization controller can realize synchronous triggering of each frame of panoramic image and digital camera image. Therefore, coarse registration is first performed according to the timestamps, so as to extract the feature points of the panoramic image, i.e., the first feature points, and extract the corresponding feature points in the digital camera image, i.e., the second feature points.

[0116] Step S606: Matching the first feature points and the second feature points by using a feature matching algorithm to obtain a first matched feature point pair.

[0117] It should be noted that the feature matching algorithm can be used to match the feature points in the panoramic image with the feature points in the digital camera image.

[0118] The feature points can be corner points, edge points or other points that are prominent in the image. The first set of feature points can be matched with the second set of feature points by a feature matching algorithm (such as SIFT, SURF, ORB, etc.). These algorithms are based on the local descriptors of the features, find the similarity between the two sets of points, and establish a matching relationship. After matching, the first matching feature point pair is obtained, and the corresponding relationship of these point pairs provides a basis for subsequent calculation.

[0119] Step S607: obtaining a point cloud feature point corresponding to a second feature point of the digital camera according to the conversion relationship between the panoramic image coordinate system and the three-dimensional point cloud data coordinate system and the first matching feature point pair.

[0120] It should be understood that there is a fixed calibration relationship between the panoramic camera and the laser, and therefore the conversion relationship between the panoramic image coordinate system and the three-dimensional point cloud data coordinate system can be obtained according to the calibration relationship, so that the homonymous point of the point cloud feature point corresponding to the digital camera image feature point pair can be obtained through the conversion relationship and the first matching feature point pair, that is, the point cloud feature point. Therefore, through the geometric relationship, the point in the image is mapped to the point cloud feature point in the three-dimensional space.

[0121] Step S608: obtaining a second matching feature point pair according to the second feature point and the corresponding point cloud feature point.

[0122] In a specific implementation, the second feature point and the corresponding point cloud feature point form the second matching feature point pair, which ensures that the subsequent transformation calculation is more accurate.

[0123] Step S609: calculating a transformation matrix from the digital image coordinate system to the three-dimensional point cloud data coordinate system according to the second matching feature point pair.

[0124] It should be noted that the second matching feature point pair can be used to calculate the transformation matrix from the digital image coordinate system to the point cloud coordinate system by a adjustment algorithm. The transformation matrix describes the geometric relationship between the two coordinate systems, including rotation and displacement. The calculation of the transformation matrix usually depends on the known matching feature point pair, which is solved by a least squares method, a RANSAC algorithm or the like.

[0125] Step S610: obtaining a digital camera image pose through the transformation matrix.

[0126] In a specific implementation, the transformation matrix can be iteratively calculated by using the post-estimation residual information, so as to calculate the high-precision coordinate attribute of the digital camera image. Since there are differences in the installation position and angle of each camera, steps S605-S609 are repeatedly performed on the image data of each camera to realize the pose information calculation of all digital camera images.

[0127] The laser radar-digital camera calibration is performed through the panoramic camera as an intermediary, the panoramic camera and the laser radar are integrated modules, the calibration relationship of the panoramic camera and the laser radar can be repeatedly used only once calibration, in addition, due to the fact that the synchronous controller trigger mechanism ensures that the panoramic image and the digital image trigger time are almost synchronous, therefore, the image visual feature point matching method can be used to construct the single-lens reflex camera and the panoramic camera calibration relationship, the image feature matching can be automatically processed, based on the existing calibration relationship of the mobile measurement vehicle panoramic camera and the laser, the corresponding relationship or geometric constraint of the connection points is established in an indirect way, then the transformation matrix of the camera sensor in the reference coordinate system is estimated. The method does not need to increase additional target calibration work, does not need manual intervention, and can realize the optimal estimation of the calibration relationship matrix.

[0128] The image and the three-dimensional point cloud collected and processed by the embodiment have high-precision pose properties, at the same time, the image collected and processed through the digital camera image grouping has the characteristics of high resolution and high overlap, can truly reflect the street scene road and the landscape on both sides, the building facade information, and realize the two-dimensional or three-dimensional expression of the three-dimensional model to the real world.

[0129] Exemplarily, in order to help understand the implementation process of the real scene three-dimensional model construction method based on the vehicle-mounted mobile measurement system obtained by combining the above embodiment one with the embodiment, please refer to Figure 5 , Figure 5A brief flowchart of a real scene three-dimensional model construction method based on a vehicle-mounted mobile measurement system is provided, specifically: step 1: a time-frequency signal driving device synchronously triggers each functional module sensor; step 2: a synchronous controller triggers a digital camera in groups; step 3: return to record each sensor timestamp, and eliminate invalid timestamps; step 4: calculate the pose of an inertial navigation sensor, and assist in tracking of a positioning antenna and a receiver loop; step 5: track and decode the positioning antenna and the receiver, and perform navigation calculation; step 6: collect the mileage of vehicle advancement, and smooth the vehicle motion trajectory; step 7: tightly combine filter estimation, calculate the unoptimized position and attitude information of the vehicle carrier, and calculate the precision information; step 8: correct the acceleration and angular velocity drift error accumulated over time of the inertial navigation; step 9: estimate the vehicle motion state through feature matching between frames of point cloud data; step 10: optimize the position and attitude information of the vehicle carrier in a loosely combined filter, and output the optimized high-frequency high-precision position and attitude information; step 11: take pictures at a fixed distance by a panoramic camera, and splice the images of six lenses into 360° panoramic images; step 12: take pictures alternately in two groups by six digital cameras, and uniformize the light and color of the images; step 13: calculate the image overlap degree, and give a warning and mark when the modeling requirement is not met; step 14: collect high-density three-dimensional point cloud data; step 15: remove noise from the high-density three-dimensional point cloud data through multi-echo elimination; step 16: perform feature extraction, semantic recognition and classification based on a deep learning method; step 17: calculate the geographic coordinate attribute of the three-dimensional point cloud data; step 18: calculate the high-precision position attribute of the panoramic image; step 19: color the point cloud data by using the panoramic image, and generate true-color three-dimensional point cloud data; step 20: estimate the transformation matrix of the camera sensor in the reference coordinate system based on a non-target camera parameter calibration method; and step 21: calculate the high-precision position attribute of the digital camera image, and construct a real scene three-dimensional model. Steps 1 to 3 are realized by a time synchronization functional module, time synchronization is performed, steps 4 to 10 are realized by a positioning and attitude determination functional module, positioning and attitude determination are performed, steps 11 to 13 are realized by an image functional processing module, image processing is performed, steps 14 to 16 are realized by a point cloud processing functional module, point cloud processing is performed, and steps 17 to 21 are realized by a core processor functional module, data processing and real scene three-dimensional model construction are performed.

[0130] It should be noted that the above examples are only used for understanding the present application, and do not constitute a limitation on the real scene three-dimensional model construction method based on the vehicle-mounted mobile measurement system of the present application, and more forms of simple transformation based on this technical concept are within the protection scope of the present application.

[0131] The present application also provides a real scene three-dimensional model construction device based on a vehicle-mounted mobile measurement system, please refer to Figure 6 The real scene three-dimensional model construction device based on the vehicle-mounted mobile measurement system comprises: A time synchronization module 10 is configured to collect acceleration and angular velocity through the inertial navigation system after time synchronization of the inertial navigation system, the GNSS receiver, the odometer, the panoramic camera, the plurality of three-dimensional laser radars, the plurality of digital cameras and the positioning antenna is completed. An acquisition module 20 is configured to collect mileage information of vehicle advancement based on the odometer. A calculation module 30 is configured to calculate a first position and a first attitude of a vehicle carrier that are not optimized according to the acceleration, the angular velocity and the mileage information. An optimization module 40 is configured to optimize the first position and the first attitude based on the three-dimensional laser radars to obtain optimized pose information. The calculation module 30 is further configured to calculate three-dimensional point cloud data geographical coordinates and panoramic image poses according to the optimized pose information. A processing module 50 is configured to process the three-dimensional point cloud data geographical coordinates through the panoramic image poses to obtain target three-dimensional point cloud data, and calculate digital camera image poses through the panoramic image poses. A construction module 60 is configured to construct a real scene three-dimensional model through the target three-dimensional point cloud data and the digital camera image poses.

[0132] The real scene three-dimensional model construction device based on the vehicle-mounted mobile measurement system provided in the application adopts the real scene three-dimensional model construction method based on the vehicle-mounted mobile measurement system in the above embodiment, and can solve the technical problem that the current real scene three-dimensional model construction effect is poor. Compared with the prior art, the real scene three-dimensional model construction device based on the vehicle-mounted mobile measurement system provided in the application has the same beneficial effects as the real scene three-dimensional model construction method based on the vehicle-mounted mobile measurement system provided in the above embodiment, and other technical features in the real scene three-dimensional model construction device based on the vehicle-mounted mobile measurement system are the same as the features disclosed in the above embodiment method, which will not be described here.

[0133] The application provides a vehicle-mounted mobile measurement system, which comprises an inertial navigation system 1, a GNSS receiver 2, an odometer (not shown in the figure), a panoramic camera Q1, a plurality of digital cameras, a plurality of three-dimensional laser radars, a positioning antenna 4 and a synchronization controller 5. The plurality of digital cameras comprises digital cameras A1, A2, A3, B1, B2 and B3. The plurality of three-dimensional laser radars comprises a first laser radar L2 and a second laser radar L1. The odometer is installed on a vehicle wheel, the plurality of digital cameras are installed around the vehicle, the plurality of three-dimensional laser radars are installed in front of the vehicle and on the left and right sides of the vehicle, and the panoramic camera Q1 and the plurality of three-dimensional laser radars are rigidly connected.

[0134] The vehicle-mounted mobile measurement system provided by the present application adopts the real scene three-dimensional model construction method based on the vehicle-mounted mobile measurement system in the above embodiment, and can solve the technical problem of poor real scene three-dimensional model construction effect at present. Compared with the prior art, the vehicle-mounted mobile measurement system provided by the present application has the same beneficial effects as the real scene three-dimensional model construction method based on the vehicle-mounted mobile measurement system provided by the above embodiment, and other technical features in the vehicle-mounted mobile measurement system are the same as the features disclosed in the previous embodiment method, which will not be repeated here.

[0135] It should be understood that parts of the present application can be realized by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0136] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0137] The present application provides a computer readable storage medium having computer readable program instructions (i.e. computer programs) stored thereon, the computer readable program instructions being used to execute the real scene three-dimensional model construction method based on the vehicle-mounted mobile measurement system in the above embodiment.

[0138] The computer readable storage medium provided in the application may be, for example, a U disk, but is not limited to an electric, magnetic, optical, electromagnetic, infrared, or semiconductor system, system, or device, or any combination thereof. More specific examples of the computer readable storage medium may include, but are not limited to, an electric connection with one or more conductive wires, a portable computer disk, a hard disk, a RAM (Random Access Memory), a ROM (Read Only Memory), an EPROM (Erasable Programmable Read Only Memory or flash memory), an optical fiber, a CD-ROM (CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the embodiment, the computer readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system, or device. The program code contained on the computer readable storage medium can be transmitted by any suitable medium, including but not limited to an electric wire, an optical cable, an RF (Radio Frequency), and the like, or any suitable combination thereof.

[0139] The computer readable storage medium described above may be contained in a vehicle-mounted mobile measurement system, or may exist separately without being assembled into the vehicle-mounted mobile measurement system.

[0140] The computer readable storage medium described above carries one or more programs, which, when executed by the vehicle-mounted mobile measurement system, cause the vehicle-mounted mobile measurement system to: after time synchronization of the inertial navigation system, the GNSS receiver, the odometer, the panoramic camera, the plurality of three-dimensional laser radars, the plurality of digital cameras, and the positioning antenna is completed, collect acceleration and angular velocity by the inertial navigation system; collect mileage information of vehicle advancement based on the odometer; calculate an unoptimized first position and a first attitude of a vehicle carrier according to the acceleration, the angular velocity, and the mileage information; optimize the first position and the first attitude based on the three-dimensional laser radar to obtain optimized pose information; calculate three-dimensional point cloud data geographical coordinates and panoramic image pose according to the optimized pose information; process the three-dimensional point cloud data geographical coordinates through the panoramic image pose to obtain target three-dimensional point cloud data, and calculate digital camera image pose through the panoramic image pose; Construct a real three-dimensional model through the target three-dimensional point cloud data and the digital camera image pose.

[0141] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0142] The flow diagrams and the block diagrams in the drawings are illustrations of architectures, functionalities, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of present application. In this regard, each block in the flow diagrams or block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may

[0143] The modules involved in the embodiments of the present application can be implemented in the form of software or in the form of hardware. In some cases, the name of the module does not constitute a limitation on the module itself.

[0144] The readable storage medium provided by the present application is a computer readable storage medium, which stores computer readable program instructions (i.e. computer programs) for executing the above-mentioned real scene three-dimensional model construction method based on a vehicle-mounted mobile measurement system, and can solve the technical problem of poor real scene three-dimensional model construction effect. Compared with the prior art, the computer readable storage medium provided by the present application has the same beneficial effects as the real scene three-dimensional model construction method based on the vehicle-mounted mobile measurement system provided by the above-mentioned embodiments, and will not be described here.

[0145] The application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method for constructing a real-scene three-dimensional model based on a vehicle-mounted mobile measurement system as described above.

[0146] The computer program product provided by the application can solve the technical problem of poor real-scene three-dimensional model construction effect. Compared with the prior art, the beneficial effects of the computer program product provided by the application are the same as those of the method for constructing a real-scene three-dimensional model based on a vehicle-mounted mobile measurement system provided by the above-mentioned embodiments, and are not described here.

[0147] The above only describes some embodiments of the application, and does not limit the patent scope of the application. Any equivalent structural transformation, direct / indirect application in other related technical fields, or direct / indirect application in other related technical fields based on the technical concept of the application and the content of the specification and drawings are included in the patent protection scope of the application.

Claims

1. A method for constructing a real-scene 3D model based on a vehicle-mounted mobile measurement system, characterized in that, The vehicle-mounted mobile measurement system includes: an inertial navigation system, a GNSS receiver, an odometer, a panoramic camera, multiple digital cameras, multiple 3D LiDARs, a positioning antenna, and a synchronization controller. The odometer is mounted on the wheels, the multiple digital cameras are mounted around the vehicle, and the multiple 3D LiDARs are mounted at the front and left and right sides of the vehicle. The panoramic camera and the multiple 3D LiDARs are rigidly connected. The method for constructing a real-scene 3D model based on a vehicle-mounted mobile measurement system includes: After the time synchronization of the inertial navigation system, the GNSS receiver, the odometer, the panoramic camera, the multiple 3D lidars, the multiple digital cameras, and the positioning antenna is completed, the acceleration and angular velocity are collected by the inertial navigation system. The odometer collects information on the distance the vehicle has traveled. The unoptimized first position and first attitude of the vehicle carrier are calculated based on the acceleration, the angular velocity, and the odometer information. Based on the three-dimensional lidar, the first position and the first posture are optimized to obtain optimized pose information; Calculate the geographic coordinates of the 3D point cloud data and the pose of the panoramic image based on the optimized pose information; The geographic coordinates of the 3D point cloud data are processed by the panoramic image pose to obtain the target 3D point cloud data, and the digital camera image pose is calculated by the panoramic image pose. A real-world 3D model is constructed using the target 3D point cloud data and the pose of the digital camera image.

2. The method as described in claim 1, characterized in that, The step of calculating the unoptimized first position and first attitude of the vehicle carrier based on the acceleration, the angular velocity, and the odometer information includes: Based on the inertial navigation system, the positioning antenna and the GNSS receiver are used to perform loop tracking to obtain navigation satellite pseudorange and carrier phase observation data. Navigation calculations are performed on the pseudorange and carrier phase observation data of the navigation satellites to obtain geographic coordinate system positioning; The inertial navigation system is corrected based on the geographic coordinate system positioning to obtain the corrected inertial navigation system, and the first corrected acceleration and the first corrected angular velocity collected by the corrected inertial navigation system are obtained. The first corrected acceleration, the first corrected angular velocity, and the mileage information are subjected to tight combination filtering estimation to obtain the unoptimized first position and first attitude of the vehicle carrier.

3. The method as described in claim 1, characterized in that, The plurality of said three-dimensional lidars include a first lidar; The step of optimizing the first position and the first pose based on the three-dimensional lidar to obtain optimized pose information includes: By correcting the acceleration and angular velocity accumulated over time by the first position and the first attitude, a second corrected acceleration and a second corrected angular velocity are obtained. First laser point cloud data is acquired using the first lidar; The vehicle's motion state is obtained by performing feature matching on the first laser point cloud data. The second corrected acceleration and the second corrected angular velocity are optimized in a loosely combined filter based on the vehicle's motion state to obtain optimized pose information.

4. The method as described in claim 1, characterized in that, The plurality of said three-dimensional lidars include a second lidar, wherein the number of laser beams scanned by the second lidar is greater than the number of laser beams scanned by the first lidar; The steps for calculating the geographic coordinates of the 3D point cloud data and the pose of the panoramic image based on the optimized pose information include: The second laser radar is used to collect second laser three-dimensional point cloud data; The second laser three-dimensional point cloud data is subjected to multi-echo cancellation and noise removal to obtain the processed second laser three-dimensional point cloud data. By using deep learning algorithms to extract features and perform semantic processing on the processed second laser 3D point cloud data, geometric and spatial structure information of building objects in the 3D scene can be obtained. The geographic coordinates of the 3D point cloud data are calculated using the optimized pose information, the geometric information, and the spatial structure information. Acquire panoramic images captured by the panoramic camera; Align the timestamp of each frame of the panoramic image with the time sequence of the optimized pose information, and interpolate the optimized pose information before and after the timestamp of the panoramic image to obtain the panoramic image pose.

5. The method as described in claim 4, characterized in that, The step of extracting features and performing semantic processing on the processed second laser 3D point cloud data using deep learning algorithms to obtain the geometric and spatial structure information of building objects in the 3D scene includes: The point cloud features are obtained by extracting features from the processed second laser 3D point cloud data using deep learning algorithms. Based on the point cloud features, semantic annotation is performed on points or grid units in the 3D scene to obtain different semantic categories; By using a deep learning model to segment regions of different semantic categories, the segmented regions of different semantic categories are obtained; The geometric and spatial structural information of architectural objects in a 3D scene is determined by segmenting different semantic category regions.

6. The method as described in claim 1, characterized in that, The step of processing the geographic coordinates of the 3D point cloud data using the pose of the panoramic image to obtain the target 3D point cloud data includes: Obtain the transformation relationship between the panoramic image coordinate system and the 3D point cloud data coordinate system; Based on the transformation relationship, the pose of the panoramic image, and the geographic coordinates of the 3D point cloud data, a panoramic image coordinate transformation is performed to colorize the 3D point cloud data using the panoramic image, thereby obtaining the target 3D point cloud data.

7. The method as described in claim 1, characterized in that, The plurality of digital cameras include a first group of digital cameras and a second group of digital cameras, wherein the number of cameras in the first group of digital cameras and the second group of digital cameras are the same, and the first group of digital cameras and the second group of digital cameras take turns shooting. Before the step of calculating the digital camera image pose using the panoramic image pose, the method further includes: The system controls the first group of digital cameras and the second group of digital cameras to alternately acquire images, and during the acquisition process, it acquires the camera's placement distance along the vehicle's forward direction, camera field of view, outward rotation angle, image size, acquisition distance, and acquisition vehicle speed. The image horizontal coverage value is calculated based on the camera field of view, the outward rotation angle, and the acquisition distance. Calculate the vertical coverage value of the image based on the horizontal coverage value and the imaging size; The overlap between the first group of digital cameras and the second group of digital cameras is calculated based on the image horizontal coverage value, the image vertical coverage value, the placement distance, and the acquisition vehicle speed. When the overlap is less than a preset overlap, the acquisition vehicle speed is reduced, and the images corresponding to the overlap less than the preset overlap are marked. The reduced acquisition vehicle speed controls the first and second sets of digital cameras to acquire images alternately.

8. The method as described in claim 7, characterized in that, The steps for calculating the pose of a digital camera image based on the panoramic image pose include: Multiple panoramic images are obtained by using the pose of the panoramic images; Acquire digital camera images alternately captured by the first group of digital cameras and the second group of digital cameras; Based on the timestamp, coarse registration is performed on multiple frames of the panoramic image and multiple frames of the digital camera image, and the first feature point in the panoramic image and the corresponding second feature point in the digital camera image are extracted. The first feature point and the second feature point are matched using a feature matching algorithm to obtain a first matching feature point pair; Based on the transformation relationship between the panoramic image coordinate system and the 3D point cloud data coordinate system and the first matching feature point pair, the point cloud feature points corresponding to the second feature points of the digital camera are obtained. A second matching feature point pair is obtained based on the second feature point and the corresponding point cloud feature point; Calculate the transformation matrix from the digital image coordinate system to the three-dimensional point cloud data coordinate system based on the second matching feature point pair; The pose of the digital camera image is obtained through the transformation matrix.

9. A device for constructing a real-scene 3D model based on a vehicle-mounted mobile measurement system, characterized in that, The device includes: The time synchronization module is used to collect acceleration and angular velocity through the inertial navigation system after the time synchronization of the inertial navigation system, GNSS receiver, odometer, panoramic camera, multiple 3D LiDAR, multiple digital cameras and positioning antenna is completed. The data acquisition module is used to collect mileage information of the vehicle based on the odometer. The calculation module is used to calculate the unoptimized first position and first attitude of the vehicle carrier based on the acceleration, the angular velocity and the mileage information; The optimization module is used to optimize the first position and the first posture based on the three-dimensional lidar to obtain optimized pose information; The calculation module is also used to calculate the geographic coordinates of the 3D point cloud data and the pose of the panoramic image based on the optimized pose information. The processing module is used to process the geographic coordinates of the three-dimensional point cloud data through the panoramic image pose to obtain the target three-dimensional point cloud data, and to calculate the digital camera image pose through the panoramic image pose. A construction module is used to construct a real-scene 3D model using the target 3D point cloud data and the pose of the digital camera image.

10. A vehicle-mounted mobile measurement system, characterized in that, The vehicle-mounted mobile measurement system includes: an inertial navigation system, a GNSS receiver, an odometer, a panoramic camera, multiple digital cameras, multiple 3D LiDARs, a positioning antenna, and a synchronization controller. The odometer is mounted on the wheels, the multiple digital cameras are mounted around the vehicle, and the multiple 3D LiDARs are mounted at the front and left and right sides of the vehicle. The panoramic camera and the multiple 3D LiDARs are rigidly connected. The vehicle-mounted mobile measurement system is configured to implement the steps of the real-scene 3D model construction method based on the vehicle-mounted mobile measurement system as described in any one of claims 1 to 8.

Images