An underground pipeline augmented reality dynamic mapping method and system

Abstract

The application discloses an underground pipeline augmented reality dynamic mapping method and system, the method comprising: acquiring dynamic mapping device pose data, environment data and underground pipeline data respectively and preprocessing; performing dynamic calibration on the preprocessed pose data to obtain dynamic calibrated pose data, and performing data verification together with the preprocessed environment data and underground pipeline data, if the verification fails, performing abnormal annotation, if the verification passes, obtaining the corresponding pipeline three-dimensional model according to the verified pose data and performing LOD optimization and multi-factor coupled adaptive deformation compensation on the obtained pipeline three-dimensional model; performing AR rendering on the pipeline three-dimensional model after the LOD optimization and multi-factor coupled adaptive deformation compensation, generating an underground pipeline augmented reality dynamic mapping image and outputting to a user terminal for display. The application can realize high-precision dynamic AR visualization mapping of underground pipelines with sub-meter accuracy in complex environments.

Description

Technical Field

[0001] This invention belongs to the field of augmented reality (AR) technology, specifically relating to an augmented reality dynamic mapping method and system for underground pipelines. Background Technology

[0002] In scenarios such as digital management of urban underground spaces and pipeline construction and maintenance, augmented reality dynamic mapping of underground pipelines is typically achieved through multi-source sensor positioning and AR visualization. However, traditional GNSS positioning can have an error of more than 5 meters in complex urban environments, resulting in insufficient positioning accuracy; existing AR pipeline overlay methods suffer from display offsets exceeding 20cm when the equipment moves, leading to distortion in virtual-real registration; and BIM model loading delays are generally above 500ms, resulting in lagging data updates and failing to meet real-time requirements. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides an augmented reality dynamic mapping method and system for underground pipelines. By spatiotemporal fusion of multi-source positioning data, dynamic positioning calibration based on motion state recognition, and adaptive deformation compensation through real-time multi-factor coupling of the pipeline 3D model, the spatiotemporal fusion error of multi-source positioning data, the dynamic loading delay of the pipeline model, and the virtual-real registration angle error are reduced, thereby achieving high-precision dynamic AR visualization mapping of underground pipelines with sub-meter accuracy in complex environments.

[0004] The present invention adopts the following technical solution.

[0005] The first aspect of this invention proposes an augmented reality dynamic mapping method for underground pipelines, comprising the following steps:

[0006] S1: Acquire the pose data of the dynamic mapping device, environmental data, and underground pipeline data respectively;

[0007] S2: Preprocess the pose data, environmental data, and underground pipeline data respectively to obtain preprocessed equipment pose data, environmental data, and underground pipeline data;

[0008] S3: Perform dynamic calibration on the preprocessed pose data to obtain dynamically calibrated pose data, and perform data verification together with the preprocessed environmental data and underground pipeline data. If the verification fails, an anomaly label is made; if the verification passes, proceed to S4.

[0009] S4: Obtain the corresponding 3D model of the pipeline based on the verified pose data, and perform LOD optimization and multi-factor coupling adaptive deformation compensation on the obtained 3D model of the pipeline.

[0010] S5: Perform AR rendering on the 3D pipeline model after LOD optimization and multi-factor coupling adaptive deformation compensation, generate augmented reality dynamic mapping images of underground pipelines, and output them to the user terminal for display.

[0011] Preferably, in S1, the pose data includes device positioning information, pose information, and three-dimensional space, environmental features, and auxiliary positioning data;

[0012] The environmental data includes environmental image data and magnetic field data;

[0013] The underground pipeline data includes 3D models of the pipelines, scanned data of underground facilities, and pipeline attribute information.

[0014] Preferably, in S2, spatiotemporal alignment of the pose data includes: performing coordinate system transformation on the data; aligning the clocks of the multi-source sensors using PPS pulse signals to achieve hardware-level synchronization; performing software-level compensation and prediction of the pose information through linear interpolation; and synchronizing the timestamps of the multi-source sensors using a timestamp synchronization model.

[0015] Denoise the environmental data;

[0016] Perform topology analysis on underground pipeline data to check and correct abnormal data.

[0017] Preferably, in S3, dynamic calibration is triggered when a pre-stored positioning marker is detected. An improved extended Kalman filter algorithm is used to dynamically calibrate the pose data, generating calibrated pose data. The specific details of using the improved extended Kalman filter algorithm to dynamically calibrate the pose data are as follows:

[0018]

[0019]

[0020]

[0021] in, , These are the posterior state estimate and the prior state estimate at time k, respectively; The pose data sensor measures the position at time k. Kalman gain; An observation model for a GNSS receiver unit; is the observation model of the IMU sensor; x is the state vector; The observation matrix; It is the magnetic field compensation factor; This represents the original geomagnetic field strength. This is the reference intensity of the geomagnetic field. It is the unit of geomagnetic field strength; It is a 3-order identity matrix; Represents the norm.

[0022] Preferably, the improved extended Kalman filter algorithm adaptively adjusts the window length for data processing based on the device's movement speed, as shown in the formula:

[0023]

[0024] in, Real-time motion speed; The baseline speed threshold is set according to the type of work scenario. This is the initial window length; This is the floor function.

[0025] Preferably, in S4, the corresponding area's 3D pipeline model is extracted from the underground pipeline database based on the verified pose data; and the data query and loading efficiency is improved through spatial index optimization, progressive transmission, and BIM model lightweighting; the pipeline database triggers incremental updates when the deviation between the pipeline position detected by the lidar and the pipeline position recorded in the database exceeds a threshold.

[0026] Preferably, in S4, the LOD optimization of the pipeline 3D model includes: dynamically adjusting the model accuracy according to the line-of-sight distance, parsing the connection semantics of the model and optimizing and adjusting it.

[0027] Preferably, in S4, the multi-factor coupled adaptive deformation compensation model for the pipeline 3D model is as follows:

[0028]

[0029] in: The pipeline coordinates are after adaptive deformation compensation due to multi-factor coupling; The basis functions are cubic B-spline functions. These are deformation parameters; Environmental weighting factors are the result of multi-factor coupling. represents the original 3D coordinates of the i-th control point in the pipeline 3D model; n represents the number of control points.

[0030] Preferably, environmental weighting factor The following multi-factor coupling model is used for calculation:

[0031]

[0032] in, T ref , B ref , ρ refAn adaptive threshold for scene temperature, magnetic field strength, and humidity; Δ T Δ B Δ ρ These represent the deviations from the reference values ​​for temperature, magnetic field strength, and humidity, respectively; the sensitivity coefficient α+β+γ=1.

[0033] Preferably, in S5, an AR rendering engine is used to perform virtual-real fusion rendering based on ambient lighting to achieve sub-pixel-level alignment display between the pipeline 3D model and the environmental scene image.

[0034] A second aspect of the present invention provides an augmented reality dynamic mapping system for underground pipelines, comprising a dynamic mapping device, wherein the dynamic mapping device includes a pose data acquisition unit, an environmental data acquisition unit, an underground pipeline database, a processing unit, and an AR unit;

[0035] The pose data acquisition unit, environmental data acquisition unit, and underground pipeline database are respectively used to acquire pose data, environmental data, and underground pipeline data of the dynamic mapping device;

[0036] The processing unit is used to preprocess the pose data, environmental data, and underground pipeline data respectively to obtain preprocessed equipment pose data, environmental data, and underground pipeline data; to dynamically calibrate the preprocessed pose data to obtain dynamically calibrated pose data, and to perform data verification together with the preprocessed environmental data and underground pipeline data. If the verification fails, an anomaly is marked. If the verification passes, the corresponding pipeline 3D model is obtained based on the verified pose data, and the obtained pipeline 3D model is subjected to LOD optimization and multi-factor coupled adaptive deformation compensation.

[0037] The AR unit is used to perform AR rendering on the pipeline 3D model after LOD optimization and multi-factor coupling adaptive deformation compensation, generate underground pipeline augmented reality dynamic mapping images, and output them to the user terminal for display.

[0038] A third aspect of the present invention provides a terminal, including a processor and a storage medium; the storage medium is used to store instructions;

[0039] The processor is configured to operate according to the instructions to execute the steps of the method.

[0040] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.

[0041] Compared with the prior art, the beneficial effects of the present invention include at least the following:

[0042] 1. This invention performs spatiotemporal alignment preprocessing on pose data and dynamic calibration on the preprocessed pose data. The spatiotemporal alignment preprocessing includes coordinate system transformation, hardware-level synchronization based on PPS pulse signals, software-level compensation prediction of pose data based on linear interpolation compensation, and timestamp synchronization using a timestamp synchronization model, resulting in a spatiotemporal fusion error of <0.3m for multi-source positioning data. The dynamic calibration uses an improved extended Kalman filter algorithm. When the lidar identifies a pre-stored marker, pose calibration is triggered. By introducing a magnetic field compensation factor, magnetic field interference is suppressed, and the data processing window length is adaptively adjusted according to the device's movement speed, dynamically linking the device's movement state with the data processing window. A tiered allocation of computing resources is achieved through a rounding function, which can adapt to the movement speed characteristics of different operating scenarios and improve stability.

[0043] 2. This invention performs topology analysis preprocessing on underground pipeline data, checks and corrects abnormal data such as connection errors, spatial conflicts, missing or incorrect attributes, and performs real-time incremental updates through data lightweighting and index optimization, so that the dynamic loading delay of the pipeline 3D model is less than 200ms.

[0044] 3. This invention performs LOD optimization on the pipeline 3D model and adaptive deformation compensation based on multi-factor coupling using environmental weight factors. It also performs virtual-real fusion rendering based on ambient lighting, achieving sub-pixel-level alignment between the pipeline 3D model and the environmental scene, resulting in a virtual-real registration angle error of <1°. By calculating environmental weight factors through multi-factor coupling modeling, and simultaneously integrating three key interference sources—temperature, magnetic field, and humidity—it better reflects the actual interference characteristics of complex underground environments. The multi-factor interference intensity is mapped to the [0,1] interval, achieving a smooth transition of the weight factors. Furthermore, the sensitivity coefficient can be dynamically adjusted according to the specific scene, improving compensation accuracy, optimizing computational efficiency, and demonstrating strong engineering feasibility. Attached Figure Description

[0045] Figure 1 This is a flowchart of the method of the present invention;

[0046] Figure 2 This is a flowchart illustrating the implementation of the method of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, other embodiments obtained by those skilled in the art without creative effort are all within the protection scope of this invention.

[0048] Embodiment 1 of the present invention provides an augmented reality dynamic mapping method for underground pipelines, implemented based on a dynamic mapping device. The dynamic mapping device includes a pose data acquisition unit, an environmental data acquisition unit, an underground pipeline database, a processing unit, and an AR unit, such as... Figure 1 and 2 As shown, the method includes the following steps:

[0049] S1: Acquire dynamic mapping device pose data, environmental data, and underground pipeline data based on the pose data acquisition unit, environmental data acquisition unit, and underground pipeline database, respectively;

[0050] (1) The pose data acquisition unit integrates the following multi-source sensors to acquire the spatial pose data of the device:

[0051] a. GPS module / GNSS receiver unit: Deploy a dual-frequency RTK-GNSS antenna array to support RTK differential positioning (frequency ≥ 10Hz, horizontal error < 2cm).

[0052] b. IMU sensor / inertial measurement unit: including a three-axis MEMS gyroscope (range ±2000° / s) and an accelerometer (noise density 100μg / √Hz).

[0053] c. LiDAR unit: Equipped with a 16-line rotating scanning head (angular resolution 0.1°).

[0054] d.UWB positioning module, as an auxiliary positioning source, adopts TDoA ranging protocol (6-channel antenna array).

[0055] (2) The environmental data acquisition unit uses an RGBD depth camera to scan environmental data and a magnetic field sensor to acquire magnetic field data.

[0056] (3) The underground pipeline database includes:

[0057] a. BIM Model Library: Stores 3D models of pipelines in IFC format;

[0058] b. Point cloud database: contains underground facility scan data;

[0059] c. Attribute database: Records attributes such as pipe diameter and material.

[0060] S2: The processing unit preprocesses the pose data, environmental data, and underground pipeline data respectively to obtain preprocessed equipment pose data, environmental data, and underground pipeline data;

[0061] (1) The processing unit performs spatiotemporal alignment on the multi-source positioning data:

[0062] (1.1) Timestamp synchronization; data synchronization mechanisms include:

[0063] a. Use PPS pulse signals to align the clocks of multi-source sensors to achieve hardware-level synchronization;

[0064] b. Timestamp alignment uses linear interpolation to compensate for communication delays. The calculation model is as follows:

[0065]

[0066] In the formula The timestamp after synchronization (unit: seconds) is used to unify the time reference of multi-sensor data; The original timestamp of the GPS module (unit: seconds (s)) is obtained from the NMEA message of the GNSS receiver; The timestamp is a local timestamp for the IMU sensor (unit: seconds (s)). Clock drift exists (typically ±20ppm), and periodic calibration is required. The IMU sampling frequency (unit: Hz) must be matched with the communication baud rate (e.g., a maximum of 400Hz is supported when the baud rate is 115200bps).

[0067] c. Apply motion prediction algorithms to IMU data for software-level compensation prediction, as shown in the following model;

[0068]

[0069] In the formula The predicted angle at time t (unit: radians (rad)) is used to compensate for sensor delay and improve the real-time performance of attitude estimation. The angle (unit: radians (rad)) is measured at time t-1 as a prediction benchmark, and the sampling frequency is required to be ≥100Hz to prevent integral drift. Instantaneous angular velocity (unit: rad / s) reflects the speed of equipment rotation and requires temperature compensation and zero-bias correction; The sampling time interval (unit: seconds (s)) determines the prediction step size, which must be matched with the sensor sampling rate (e.g., 200Hz corresponds to Δt=0.005s). Angular acceleration (unit: rad / s) 2 This reflects the rate of rotational change, which cannot be ignored in highly dynamic scenarios.

[0070] The present invention also includes a feature matching unit, which performs SIFT feature point comparison to assist in realizing augmented reality dynamic mapping.

[0071] (1.2) The coordinate system transformation chain is as follows:

[0072] WGS84 (Geocentric Coordinate System) → UTM Coordinate System (Projected Coordinate System) → Local Tangent Plane Coordinate System → Equipment Coordinate System;

[0073] (2) The processing unit denoises the environmental data.

[0074] (3) The processing unit performs topology analysis on the underground pipeline data, checks and corrects abnormal data such as connection errors, spatial conflicts, missing attributes or errors.

[0075] S3: The processing unit performs dynamic calibration on the preprocessed pose data to obtain dynamically calibrated pose data, and performs data verification together with the preprocessed environmental data and underground pipeline data. If the verification fails, an anomaly is marked. If the verification passes, proceed to S4.

[0076] S301: The processing unit performs dynamic calibration on the preprocessed pose data through a dynamic calibration engine, wherein the dynamic calibration engine includes:

[0077] (1) Closed-loop correction module based on underground features: When the lidar identifies a pre-stored positioning marker, pose correction is automatically triggered; the marker recognition threshold is set to 80% similarity.

[0078] (2) Error compensation unit: The improved extended Kalman filter algorithm (improved EKF algorithm) is used to dynamically compensate for the positioning error of the pose data and generate calibrated pose data;

[0079] In the standard EKF state update equation, the observation matrix Nonlinear observation function The Jacobian matrix does not consider the impact of geomagnetic disturbances on attitude estimation.

[0080] After the improvement of this invention, in the observation matrix Introducing a magnetic field compensation factor The improved extended Kalman filter algorithm is as follows:

[0081]

[0082] , These are the posterior state estimate and the prior state estimate at time k, respectively; This is the state vector of the sensor measurement value, i.e., the sensor's measurement result at time k; Kalman gain; An observation model for a GNSS receiver unit; is the observation model of the IMU sensor; x is the state vector; The observation matrix; It is the magnetic field compensation factor; This represents the original geomagnetic field strength. This is the reference intensity of the geomagnetic field; It is the unit of geomagnetic field strength; is a 3rd-order identity matrix; represents the norm.

[0083] In the observation matrix introduce a magnetic field compensation factor :

[0084]

[0085]

[0086] where , is the reference intensity of the geomagnetic field.

[0087] (3) Adopt a sliding window optimization algorithm. The error compensation unit adaptively adjusts the window length of data processing according to the movement speed of the device. The formula is:

[0088]

[0089] where is the real-time movement speed (unit: m / s); is the reference speed threshold (unit: m / s), set according to the type of operation scenario; is the initial window length (unit: seconds); is the ceiling function.

[0090] The principle of dynamic adjustment of the above formula is: when the device is moving at high speed (v > v0): increase the window length to improve positioning stability; when the device is moving at low speed (v < v0): decrease the window length to improve response speed. In this formula, when the speed exceeds v0, the window length increases proportionally, but forms a stepped change through the ceiling function to avoid frequent adjustment. The recommended values of the parameters are as follows: urban road scenario: v0 = 1.2 - 1.8 m / s; underground pipe gallery scenario: v0 = 0.5 - 0.8 m / s; [[ID=四十二]]Suggested range: 2 - 5 seconds.

[0091] S302: The processing unit performs data verification on the dynamically calibrated pose data, preprocessed environmental data, and underground pipeline data. If the dynamically calibrated pose data, preprocessed environmental data, and underground pipeline data match, the verification passes; otherwise, the verification fails.

[0092] For example, if the environmental data corresponds to building image data, while the pose data and underground pipeline data are clearly not data belonging to the building scenario, the verification fails.

[0093] S4: The processing unit obtains the 3D model of the pipeline in the corresponding area according to the verified pose data and performs multi-level LOD optimization and adaptive deformation compensation with multi-factor coupling on the 3D model of the pipeline, including:

[0094] S401: Extract the 3D model of the pipeline in the corresponding area from the underground pipeline database based on the spatial coordinates in the verified pose data; and improve the efficiency of data query and loading through spatial index optimization, progressive transfer and BIM model lightweighting.

[0095] When the LiDAR detects a pipeline location that deviates from the pipeline location recorded in the BIM by more than a threshold Δ, it triggers an incremental update of the underground pipeline database.

[0096] S402: Perform multi-level LOD (Level of Detail) optimization on the pipeline 3D model, including: dynamically adjusting the model accuracy based on the view distance, such as adjusting the model size or number of faces according to the view distance, loading the complete BIM model or the simplified wireframe model; it also includes pipeline data processing: parsing and adjusting the connection semantics of the BIM model, such as dynamically adjusting the connection relationship between multiple models according to the connection semantics when adjusting the model size to achieve topology reconstruction; triggering incremental database updates when the pipeline position deviation exceeds the threshold Δ.

[0097] S403: Adaptive deformation compensation based on multi-factor coupling of environmental depth information for pipeline 3D models to correct environmental interference. Specifically, it improves the accuracy of model deformation by using a B-spline-based deformation model and environmental factor compensation (geomagnetic anomaly, temperature drift, multipath effect, etc.). The multi-factor coupled adaptive deformation compensation model is as follows:

[0098]

[0099] in: The basis functions are cubic B-spline functions. An adaptive environmental weighting factor that is coupled with multiple factors (related to temperature, magnetic field strength, etc.). represents the original 3D coordinates of the i-th control point in the pipeline 3D model; n represents the number of control points. Calculations were performed using a multi-factor coupling model:

[0100]

[0101] in T ref , B ref , ρ ref An adaptive threshold for scene temperature, magnetic field strength, and humidity; Δ T Δ B Δ ρ These represent the deviations of temperature, magnetic field strength, and humidity from the reference values, respectively; the coefficient α+β+γ=1.

[0102] By employing multi-factor coupled modeling (simultaneously integrating three key interference sources—temperature, magnetic field, and humidity—to better reflect the actual interference characteristics of complex underground environments), dynamic weight adaptation (mapping the intensity of multi-factor interference to the [0,1] interval to achieve a smooth transition of weight factors), and scenario adaptability design (the sensitivity coefficients α, β, and γ can be dynamically adjusted according to specific scenarios), the system achieves improved compensation accuracy, optimized computational efficiency, and strong engineering feasibility.

[0103] S5: The AR unit performs AR rendering on the pipeline 3D model after multi-level LOD optimization and multi-factor coupling adaptive deformation compensation, generates AR images, and outputs them to the user terminal for display.

[0104] The AR unit employs an AR rendering engine to perform virtual-real fusion rendering based on ambient lighting, achieving sub-pixel-level alignment between the pipeline's 3D model and the surrounding environment. Specifically, it utilizes the PnP algorithm to achieve sub-pixel-level registration and geometric alignment.

[0105] Embodiment 2 of the present invention provides an augmented reality dynamic mapping system for underground pipelines, which adopts a progressive architecture of perception layer, data layer, processing layer, rendering layer and application layer, including a dynamic mapping device, which includes a pose data acquisition unit, an environmental data acquisition unit, an underground pipeline database, a processing unit and an AR unit;

[0106] The pose data acquisition unit, environmental data acquisition unit, and underground pipeline database are respectively used to acquire pose data, environmental data, and underground pipeline data of the dynamic mapping device;

[0107] More preferably, the multi-source localization module (pose data acquisition unit) of the perception layer includes:

[0108] a. GPS receiver unit: Deploys a dual-frequency RTK-GNSS antenna array;

[0109] b. Inertial Measurement Unit: Includes a three-axis gyroscope and an accelerometer;

[0110] c. LiDAR unit: Equipped with a 16-line rotating scanning head;

[0111] The environmental sensing module (environmental data acquisition unit) includes:

[0112] a. RGBD camera: 1080p depth resolution @ 30fps; directly connected to the LiDAR unit via a time synchronization cable;

[0113] b. Magnetic field sensor: range ±8 Gauss.

[0114] The pipeline database of the data layer includes:

[0115] a. BIM Model Library: Stores IFC format pipeline data;

[0116] b. Point cloud database: contains underground facility scan data;

[0117] c. Attribute database: Records attributes such as pipe diameter and material.

[0118] The processing unit is used to preprocess the pose data, environmental data, and underground pipeline data respectively to obtain preprocessed equipment pose data, environmental data, and underground pipeline data; to dynamically calibrate the preprocessed pose data to obtain dynamically calibrated pose data, and to perform data verification together with the preprocessed environmental data and underground pipeline data. If the verification fails, an anomaly is marked. If the verification passes, the corresponding pipeline 3D model is obtained based on the verified pose data, and multi-level LOD optimization and multi-factor coupled adaptive deformation compensation are performed on the obtained pipeline 3D model.

[0119] More preferably, the data fusion center (processing unit) of the processing layer includes:

[0120] a. Spatiotemporal alignment unit: Enables coordinate system transformation;

[0121] b. Error Compensation Unit: Running the improved EKF algorithm, the error compensation unit can establish a two-way data verification channel with the attribute database. The attribute database can ensure the rationality of the location calculation and the environmental adaptability by providing the geometric and physical properties of the pipeline.

[0122] c. Feature matching unit: Performs SIFT feature point comparison;

[0123] d. Multi-factor coupled adaptive deformation compensation module: Based on the B-spline deformation algorithm, the multi-factor coupled adaptive deformation compensation module can receive real-time interference data from the magnetic field sensor.

[0124] The AR unit is used to perform AR rendering on the pipeline 3D model after multi-level LOD optimization and multi-factor coupling adaptive deformation compensation, generate underground pipeline augmented reality dynamic mapping images and output them to the user terminal for display.

[0125] More preferably, the AR rendering engine of the rendering layer includes:

[0126] a. Geometric Alignment Module: Employs the PnP solver;

[0127] b. Lighting blending module: Supports HDR environment mapping.

[0128] In practice, the adaptive deformation compensation module with multi-factor coupling can also be deployed in the rendering layer.

[0129] Application layer user terminals include:

[0130] a. AR head-mounted display: 52° FOV binocular display;

[0131] b. Mobile control terminal: Equipped with a touch-screen interface;

[0132] c. Collaboration Server: Supports data synchronization across multiple terminals.

[0133] The following are examples of applications in urban road scenarios:

[0134] Deploy differential GNSS base stations (210) to establish a centimeter-level positioning reference network;

[0135] The mobile terminal (100) includes: a Trimble R12 GNSS receiver, an Xsens MTi-680G inertial unit, and a Velodyne VLP-16 lidar;

[0136] The dynamic calibration engine uses an update frequency of 15 frames per second, and the sliding window length is set to 5 seconds;

[0137] The AR rendering engine supports multi-user collaborative annotation, and the annotation data is uploaded to the cloud database in real time.

[0138] The following are examples of applications in underground utility tunnel scenarios:

[0139] Pre-configured UWB positioning base stations as auxiliary positioning sources;

[0140] Activate the closed-loop correction submodule and set the marker recognition threshold to 80% similarity;

[0141] The pipeline model loading adopts a progressive transmission strategy, prioritizing the loading of data in the current field of view.

[0142] Tests showed that the positioning accuracy of the present invention reached 0.2m in open areas, and the model loading delay was <200ms, which is significantly better than the existing technology.

[0143] Embodiment 3 of the present invention provides a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to perform operations according to the instructions to execute the steps of the method.

[0144] Embodiment 4 of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.

[0145] Compared with the prior art, the beneficial effects of the present invention include at least the following:

[0146] 1. This invention performs spatiotemporal alignment preprocessing on pose data and dynamic calibration on the preprocessed pose data. The spatiotemporal alignment preprocessing includes coordinate system transformation, hardware-level synchronization based on PPS pulse signals, software-level compensation prediction of pose data based on linear interpolation compensation, and timestamp synchronization using a timestamp synchronization model, so that the spatiotemporal fusion error of multi-source positioning data is <0.3m. The dynamic calibration adopts an improved extended Kalman filter algorithm. When the lidar identifies a pre-stored marker, pose calibration is triggered. By introducing a magnetic field compensation factor, magnetic field interference is suppressed, and the window length of data processing is adaptively adjusted according to the device's movement speed to improve stability.

[0147] 2. This invention performs topology analysis preprocessing on underground pipeline data, checks and corrects unreasonable data, and through data lightweighting and index optimization, performs real-time incremental updates, so that the dynamic loading delay of the pipeline 3D model is <200ms.

[0148] 3. This invention performs LOD optimization on the pipeline 3D model and adaptive deformation compensation based on multi-factor coupling using environmental weight factors. It also performs virtual-real fusion rendering based on ambient lighting, achieving sub-pixel-level alignment between the pipeline 3D model and the environmental scene, resulting in a virtual-real registration angle error of <1°. By calculating environmental weight factors through multi-factor coupling modeling, and simultaneously integrating three key interference sources—temperature, magnetic field, and humidity—it better reflects the actual interference characteristics of complex underground environments. The multi-factor interference intensity is mapped to the [0,1] interval, achieving a smooth transition of the weight factors. Furthermore, the sensitivity coefficient can be dynamically adjusted according to the specific scene, improving compensation accuracy, optimizing computational efficiency, and demonstrating strong engineering feasibility.

[0149] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0150] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0151] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0152] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for augmented reality dynamic mapping of underground pipelines, characterized in that, The method comprises the following steps: S1: acquiring dynamic mapping device pose data, environment data and underground pipeline data respectively; S2: preprocessing the pose data, environment data and underground pipeline data respectively to obtain preprocessed device pose data, environment data and underground pipeline data; S3: dynamically calibrating the preprocessed pose data to obtain dynamically calibrated pose data, and performing data verification together with the preprocessed environment data and underground pipeline data, if the verification fails, performing abnormal marking, if the verification passes, entering S4; S4: obtaining the corresponding pipeline three-dimensional model according to the verified pose data and performing LOD optimization and multi-factor coupled adaptive deformation compensation on the obtained pipeline three-dimensional model; wherein the model for performing multi-factor coupled adaptive deformation compensation on the pipeline three-dimensional model is: wherein: is the pipeline coordinate after deformation compensation; is a cubic B-spline basis function; is a deformation parameter; is a multi-factor coupled adaptive environmental weight factor; is the original three-dimensional coordinate of the i-th control point in the pipeline three-dimensional model; n is the number of control points; Adaptive environmental weight factor coupled with multiple factors The computational model is: wherein, T ref , B ref , S5: performing AR rendering on the pipeline three-dimensional model after the LOD optimization and multi-factor coupled adaptive deformation compensation, generating an underground pipeline augmented reality dynamic mapping image and outputting to a user terminal for display. ref is an adaptive threshold value for the scene temperature, magnetic field intensity, humidity; Δ T , Δ B , Δ 2. The underground pipeline augmented reality dynamic mapping method according to claim 1, wherein: is the deviation of the temperature, magnetic field intensity, humidity from the reference value; the sensitivity coefficients α + β + γ = 1; In S1, the pose data includes device positioning information, pose information, three-dimensional space, environmental features and auxiliary positioning data; The environment data includes environment image data and magnetic field data; The underground pipeline data includes pipeline three-dimensional model, underground facility scanning data and pipeline attribute information.

3. The underground pipeline augmented reality dynamic mapping method according to claim 1, wherein: In S2, the pose data is time and space aligned, including: coordinate system conversion is performed on the data; PPS pulse signal is used to align the clocks of multiple source sensors to realize hardware level synchronization; linear interpolation compensation is used to predict the software level compensation of the pose information; a timestamp synchronization model is used to synchronize the timestamps of multiple source sensors; The environment data is denoised; The underground pipeline data is topologically analyzed, and abnormal data is checked and corrected.

4. The underground pipeline augmented reality dynamic mapping method according to claim 1, wherein: In S3, dynamic calibration is triggered when a pre-stored positioning marker is detected, and an improved extended Kalman filter algorithm is used to dynamically calibrate the pose data to generate calibrated pose data; the improved extended Kalman filter algorithm for dynamically calibrating the pose data is as follows:

5. The underground pipeline augmented reality dynamic mapping method according to claim 4, wherein: The improved extended Kalman filter algorithm adaptively adjusts the window length of data processing according to the device motion speed, and the formula is: wherein, , are the posterior state estimate and the prior state estimate at time k, respectively; is the measurement result of the pose data sensor at time k; is the Kalman gain; is the observation model of the GNSS receiving unit; is the observation model of the IMU sensor; x is the state vector; is the observation matrix; is the magnetic field compensation factor; is the original geomagnetic field strength; is the geomagnetic field reference strength; is the geomagnetic field strength unit; is the 3rd order identity matrix; denotes the norm.

6. The underground pipeline augmented reality dynamic mapping method according to claim 1, wherein: In S4, the pipeline three-dimensional model of the corresponding area is extracted from the underground pipeline database according to the verified pose data; and the data query and loading efficiency is improved through spatial index optimization, progressive transmission and BIM model lightweight; the pipeline database triggers incremental update when the laser radar detects that the pipeline position deviates from the pipeline position recorded in the database by more than a threshold value. wherein, is a real-time motion speed; is a reference speed threshold value, set according to a work scene type; is an initial window length; is a ceiling function. ​ ​ 7. The underground pipeline enhanced reality dynamic mapping method of claim 1, wherein: In S4, the LOD optimization of the pipeline three-dimensional model comprises: dynamically adjusting the model precision according to the sight distance, and analyzing and optimizing the connection semantics of the model.

8. The underground pipeline enhanced reality dynamic mapping method of claim 1, wherein: In S5, the AR rendering engine is used to perform virtual-real fusion rendering based on the ambient light, so as to realize the sub-pixel level alignment display of the pipeline three-dimensional model and the environment scene image.

9. An underground pipeline augmented reality dynamic mapping system for implementing the method of any one of claims 1 to 8, characterized in that, The system comprises a dynamic mapping device, which comprises a pose data acquisition unit, an environment data acquisition unit, an underground pipeline database, a processing unit and an AR unit. The pose data acquisition unit, the environment data acquisition unit and the underground pipeline database are respectively used to acquire the dynamic mapping device pose data, the environment data and the underground pipeline data. The processing unit is used to pre-process the pose data, the environment data and the underground pipeline data respectively, so as to obtain the pre-processed device pose data, the environment data and the underground pipeline data. The pre-processed pose data is dynamically calibrated to obtain the dynamically calibrated pose data, which is verified with the pre-processed environment data and underground pipeline data. If the verification fails, an exception is marked. If the verification passes, the corresponding pipeline three-dimensional model is obtained according to the verified pose data, and the obtained pipeline three-dimensional model is subjected to LOD optimization and multi-factor coupled adaptive deformation compensation. The AR unit is used to perform AR rendering on the pipeline three-dimensional model subjected to LOD optimization and multi-factor coupled adaptive deformation compensation, to generate an underground pipeline enhanced reality dynamic mapping image and output it to a user terminal for display.

10. A terminal comprising a processor and a storage medium, characterized in that: The storage medium is used to store instructions; The processor is used to operate according to the instructions to perform the steps of the method according to any one of claims 1-8.

11. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the steps of the method according to any one of claims 1-8.

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