CN-122015879-A - Bridge unmanned aerial vehicle inspection positioning system and method based on embedded magnetic coding grid

CN122015879ACN 122015879 ACN122015879 ACN 122015879ACN-122015879-A

Abstract

The invention discloses a bridge unmanned aerial vehicle inspection positioning system and method based on embedded magnetic coding grids, wherein the system comprises a magnetic coding grid layer which is arranged in a bridge GNSS signal blind area, an absolute coordinate beacon is coded through the arrangement of permanent magnet magnetic poles of a passive magnetic coding unit, an airborne magnetic sensing layer is carried with a triaxial magnetometer to collect magnetic field data in real time, interference compensation and signal identification processing are carried out, a magnetic field component is output, a positioning resolving layer receives the magnetic field component, a relative angle is resolved by adopting a magnetic induction vector algorithm irrelevant to the posture, an absolute coordinate beacon is known by a coding unit, an unmanned aerial vehicle absolute positioning coordinate is output, a disease mapping layer takes the absolute coordinate of the unmanned aerial vehicle and a disease image shot by the unmanned aerial vehicle as input, and the absolute coordinate and the disease image are associated and matched, so that the automatic disease marking of a bridge BIM model is completed. The invention realizes centimeter-level absolute positioning of the unmanned aerial vehicle under the GNSS blind area at the bottom of the bridge and effectively solves the difficulty of inspection and positioning at the bottom of the bridge.

Inventors

OuYang Tianshui
Mei Zhanqiu
XIA PENGFEI
QIU BIN
HUANG ZHENHUA
LIU MINGMING
HUANG YUAN
ZHU HAIBO
ZHOU JIAFENG
ZHENG PING
SONG KAI
YU PENGFEI
HU SHA
HU SHAOQING
YAO DONGLIANG
SHI MUGUI
ZHOU TAO

Assignees

江西交信科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260415

Claims (10)

1. Bridge unmanned aerial vehicle inspection position system based on pre-buried magnetism coding net, its characterized in that includes: the magnetic coding grid layer is used for embedding or laying GNSS signal blind areas on the bottom surface of a bridge, the surface of a bridge pier or the inner wall of a box girder and consists of a plurality of passive magnetic coding units with known and fixed positions, wherein each passive magnetic coding unit codes absolute three-dimensional coordinate information of points laid in the GNSS signal blind areas by virtue of the magnetic pole arrangement of an internal permanent magnet material to form an absolute coordinate beacon with unique magnetic field fingerprint characteristics; the airborne magnetic sensing layer is used for being carried on the inspection unmanned aerial vehicle and comprises a triaxial magnetometer and a signal processing module, wherein the triaxial magnetometer and the signal processing module are used for acquiring magnetic field data of the current position of the unmanned aerial vehicle in real time, and after the interference compensation layer is adopted for carrying out interference processing on the magnetic field data, magnetic field component data corresponding to each passive magnetic coding unit are separated through a signal identification algorithm; The positioning resolving layer is used for receiving magnetic field component data output by the on-board magnetic sensing layer, resolving a relative pitch angle and a relative azimuth angle of the unmanned aerial vehicle relative to each passive magnetic coding unit by adopting a magnetic induction vector positioning algorithm irrelevant to the posture of the sensor, and determining the absolute coordinates of the unmanned aerial vehicle by combining an absolute coordinate beacon known by the passive magnetic coding units; And the disease mapping layer is used for associating the bridge disease image shot by the unmanned aerial vehicle with the absolute coordinate of the unmanned aerial vehicle obtained by resolving, so that the automatic marking of the disease position of the bridge disease image on the bridge BIM model is realized.
2. The bridge unmanned aerial vehicle inspection positioning system based on the embedded magnetic coding grid of claim 1, wherein the positioning resolving layer comprises: an inner product calculation module for separating three magnetic field components with different frequencies from the received magnetic field component data 、、 ; For the separated constant magnetic field component or reference frequency component; for the separated effective signal component or the synchronous frequency component; For the separated orthogonal magnetic field components; Will be And (3) with Vector dot multiplication is carried out to obtain Will (i) be And (3) with Vector dot multiplication is carried out to obtain Will (i) be And (3) with Vector dot multiplication is carried out to obtain ; Is that And Scalar inner product values; Is that And (3) with Scalar inner product values; Is that And (3) with Scalar inner product values; The module is used for calculating the module according to the 、、 Calculating the vector module length of each magnetic field component through a vector module length formula 、、 Wherein, the method comprises the steps of, Is that Is a vector mode length of (2); Is that Is a vector mode length of (2); Is that Is a vector mode length of (2); An angle resolving module for resolving according to 、、 And 、、 Solving the relative pitch angle of a passive magnetic coding unit And the relative azimuth of the passive magnetic coding unit ; Known absolute coordinate beacons with three passive magnetic encoding units I=1, 2,3, the current position of the unmanned aerial vehicle is ; Is the first In absolute three-dimensional coordinates of individual passive magnetic coding units An axis coordinate; is the first In absolute three-dimensional coordinates of individual passive magnetic coding units An axis coordinate; is the first In absolute three-dimensional coordinates of individual passive magnetic coding units An axis coordinate; Is unmanned aerial vehicle current An axis coordinate; Is unmanned aerial vehicle current An axis coordinate; Is unmanned aerial vehicle current An axis coordinate; According to the magnetic dipole model, each passive magnetic coding unit is positioned at the current position of the unmanned plane The resulting magnetic field vector, expressed as the relative distance between the drone and each passive magnetic encoding unit And a function of relative direction angles, including relative pitch angle Relative azimuth angle ; Relative to the first unmanned plane The relative pitch angle of the passive magnetic coding units; relative to the first unmanned plane The relative azimuth angles of the passive magnetic coding units; Is unmanned plane and the first The relative distance between the passive magnetic encoding units; Constructing a relative pitch angle through an inner product equation and a module value equation of three passive magnetic coding units Relative azimuth angle And relative distance Solving the nonlinear equation set by adopting a numerical iteration method to obtain a converged relative pitch angle Relative azimuth angle And relative distance ; Is the first The relative pitch angle after the convergence of the passive magnetic coding units; is the first The relative azimuth angles after the convergence of the passive magnetic coding units; is the first The relative distance after convergence of the passive magnetic coding units; wherein the inner product equation expresses the inner products of magnetic field vectors of different passive magnetic coding units as relative pitch angles And relative azimuth angle For constraining unknown relative pitch angle And relative azimuth angle ; The module value equation expresses the magnetic field vector module length generated by each passive magnetic coding unit at the unmanned plane as the relative distance For constraining unknown relative distances ; Based on 、 And And Calculating absolute coordinates of unmanned aerial vehicle , , ), In absolute three-dimensional coordinates of unmanned aerial vehicle An axis coordinate; in absolute three-dimensional coordinates of unmanned aerial vehicle An axis coordinate; in absolute three-dimensional coordinates of unmanned aerial vehicle And (5) axis coordinates.
3. The bridge unmanned aerial vehicle inspection positioning system based on the embedded magnetic coding grid, as set forth in claim 2, is characterized in that the processing process of the on-board magnetic sensing layer is as follows: Calibrating an unmanned aerial vehicle or handheld equipment by using a carrying triaxial magnetometer, scanning a bridge GNSS signal blind area, recording the magnetic field fingerprint characteristics of each passive magnetic coding unit and corresponding absolute coordinate beacons, and generating a bridge bottom magnetic field fingerprint map; selecting a plurality of calibration points on a bridge bottom magnetic field fingerprint map, measuring magnetic field data at each point, and calculating by adopting an iterative compensation algorithm based on an absolute coordinate beacon to obtain mirror image source parameters; The airborne magnetic sensing layer further comprises an interference compensation layer, and is used for compensating the actually measured magnetic field data based on the mirror image source parameters to obtain compensated magnetic field data, specifically: after the inspection unmanned aerial vehicle flies into a GNSS signal blind area, an on-board triaxial magnetometer acquires actual measurement magnetic field data in real time, and compensates the actual measurement magnetic field data by adopting an iterative compensation algorithm based on the mirror image source parameters to obtain compensated magnetic field data; the signal processing module separates the compensated magnetic field data into magnetic field component data corresponding to each passive magnetic coding unit through a signal identification algorithm.
4. The inspection positioning system of the bridge unmanned aerial vehicle based on the embedded magnetic coding grid of claim 3, wherein the mirror image source parameters are obtained by selecting n more than or equal to 3 calibration points and the coordinates of the calibration points when constructing a bridge bottom magnetic field fingerprint map , , ); Is of the standard point An axis coordinate; Is of the standard point An axis coordinate; Is of the standard point N is the number of selected calibration points; measuring magnetic field vectors at various calibration points by a calibration unmanned aerial vehicle or handheld device carrying a triaxial magnetometer ; The measured magnetic field vector at the jth calibration point; From beacons of known absolute coordinates Calculating theoretical magnetic fields under the condition of no interference at each standard point by using a magnetic dipole magnetic field calculation algorithm The disturbing magnetic field is: ; a theoretical magnetic field vector without interference at the j-th calibration point; to the jth index point Interference magnetic field vectors at the individual mirror sources; Based on An iterative compensation algorithm is adopted to carry out iterative optimization calculation, from the following components Iterative derivation of the distance of the calibration point to the mirror source Based on Obtaining the estimated position of the mirror image source through solving a three-dimensional space distance equation ; For the jth index point to the jth index point The spatial distance between the mirror sources; is the first The mirror image sources being in a three-dimensional rectangular coordinate system An axis coordinate estimation value; is the first The mirror image sources being in a three-dimensional rectangular coordinate system An axis coordinate estimation value; is the first The mirror image sources being in a three-dimensional rectangular coordinate system An axis coordinate estimation value; The mirror source has its own local coordinate system, using Solving a rotation matrix from a mirror image source local coordinate system to a navigation coordinate system in a magnetic positioning navigation mode of the unmanned aerial vehicle; Will mirror the source location And the rotation matrix as mirror source parameters.
5. The inspection and positioning system of the bridge unmanned aerial vehicle based on the embedded magnetic coding grid of claim 4, wherein the passive magnetic coding unit is arranged on the bottom surface, the bridge pier surface or the inner wall of the box girder in the following manner: the passive magnetic coding units are arranged on the bottom surface of the bridge in an embedded or laid arrangement mode and are distributed in an equidistant rectangular grid; the bridge pier surface comprises a cylindrical bridge pier and a square bridge pier, wherein passive magnetic coding units are distributed in a ring-shaped and vertical grid form in a laying mode of the cylindrical bridge pier; And the passive magnetic coding units are distributed in a pre-buried mode on the inner wall of the box girder in a rectangular grid form extending longitudinally or transversely along the inner wall of the box girder, and are attached to the curved surface of the inner wall of the box girder to realize dead-angle-free coverage and completely cover the whole inner wall of the box girder.
6. The bridge unmanned aerial vehicle inspection positioning system based on the embedded magnetic coding grid of claim 5, wherein the disease mapping layer comprises the following processing steps: the disease mapping layer correlates the bridge disease image shot by the unmanned aerial vehicle with the absolute coordinate of the unmanned aerial vehicle obtained by resolving, and maps the disease pixel points on the bridge disease image to the surface of the bridge BIM model through a ray method, so that the automatic labeling of the disease position of the bridge disease image on the bridge BIM model is realized.
7. The bridge unmanned aerial vehicle inspection positioning system based on the embedded magnetic coding grid of claim 6, wherein the processing process for automatically marking the disease position of the bridge disease image on the bridge BIM model is as follows: The unmanned aerial vehicle flies according to a preset route, meanwhile, a camera is adopted to shoot bridge disease images, each time a bridge disease image is shot, and a flight control system records absolute coordinates of the unmanned aerial vehicle , , ) And shooting attitude including relative pitch angle And relative azimuth angle ; After the inspection is completed, the absolute coordinates of the unmanned aerial vehicle are recorded , , ) And shooting the attitude and leading in the bridge BIM management system, realize disease location to shooting the bridge disease image, include: Identifying the crack, exposed rib and peeling off diseases in the bridge disease image by adopting a deep learning algorithm to obtain disease pixel coordinates in the bridge disease image; Mapping according to the absolute coordinate of unmanned plane , , ) Shooting the gesture, constructing rays which pass through the disease pixel points from the camera optical center, and calculating the intersection point coordinates of the rays and the bridge BIM model surface; converting the intersection point coordinates into absolute coordinates in the bridge BIM model, and realizing automatic labeling of diseases on the bridge BIM model; and generating a report, namely generating a patrol report containing disease type, size and position information after automatic labeling.
8. The bridge unmanned aerial vehicle inspection positioning method based on the embedded magnetic coding grid is applied to the bridge unmanned aerial vehicle inspection positioning system based on the embedded magnetic coding grid as claimed in any one of claims 1 to 7, and is characterized by comprising the following steps: Step S1, embedding or laying a plurality of passive magnetic coding units in GNSS signal dead zones of the bottom surface of a bridge, the surface of a pier or the inner wall of a box girder, and forming an absolute coordinate beacon with unique magnetic field fingerprint characteristics by encoding absolute three-dimensional coordinate information of points laid in the GNSS signal dead zones by each passive magnetic coding unit; s2, scanning a bridge GNSS signal blind zone by using a calibration unmanned aerial vehicle or handheld device carrying a triaxial magnetometer, recording the magnetic field fingerprint characteristics of each passive magnetic coding unit and absolute coordinate beacons corresponding to the passive magnetic coding units, and generating a bridge bottom magnetic field fingerprint map; s21, selecting a plurality of calibration points from a bridge bottom magnetic field fingerprint map, measuring magnetic field data at each calibration point, and calculating the magnetic field data by adopting an iterative compensation algorithm based on an absolute coordinate beacon to obtain mirror image source parameters; S3, after the inspection unmanned aerial vehicle flies into a GNSS signal blind area, the airborne triaxial magnetometer collects actual measurement magnetic field data of the current position in real time, and compensates the actual measurement magnetic field data based on mirror image source parameters obtained in advance to obtain compensated magnetic field data; s4, adopting a magnetic induction vector positioning algorithm irrelevant to the sensor posture, calculating the relative pitch angle and the relative azimuth angle of the unmanned aerial vehicle relative to each passive magnetic coding unit based on magnetic field component data, and determining the absolute coordinates of the unmanned aerial vehicle by combining an absolute coordinate beacon; s5, the unmanned aerial vehicle flies according to a preset route, bridge disease images are shot, and an absolute coordinate and shooting gesture of the unmanned aerial vehicle at the moment of shooting each bridge disease image are recorded by a flight control system; And S6, associating the bridge disease image with the absolute coordinates and shooting postures of the unmanned aerial vehicle, and mapping disease pixel points on the bridge disease image to the surface of the bridge BIM model through a ray method to automatically mark the disease positions of the bridge disease image on the bridge BIM model.
9. An electronic device comprising a processor, a memory and a bus, wherein the processor and the memory are connected by the bus, the memory is used for storing a set of program codes, and the processor is used for calling the program codes stored in the memory and executing the bridge unmanned aerial vehicle inspection positioning method based on the embedded magnetic coding grid as claimed in claim 8.
10. A non-volatile computer storage medium having stored thereon computer executable instructions for performing the bridge unmanned aerial vehicle inspection positioning method based on the pre-buried magnetically encoded grid of claim 8.

Description

Bridge unmanned aerial vehicle inspection positioning system and method based on embedded magnetic coding grid Technical Field The invention relates to the technical field of inspection of bridge structure diseases, in particular to a bridge unmanned aerial vehicle inspection positioning system and method based on embedded magnetic coding grids. Background The bridge is used as a key node of a traffic infrastructure, and the structural safety of the bridge is directly related to the smoothness of traffic and transportation and the safety of lives and properties of people. Along with the continuous increase of the total quantity and the continuous increase of the service life of the bridge, the requirements for detecting and maintaining the bridge structural diseases are increasingly urgent. The apparent bridge diseases mainly comprise cracks, exposed ribs, flaking, honeycomb pitting, expansion joint damage and the like, and timely discovery and accurate positioning of the diseases are the basis of bridge safety assessment and maintenance decision. In recent years, unmanned aerial vehicle inspection technology is widely applied to the field of bridge disease detection by virtue of the advantages of flexibility, comprehensive visual angle, high operation efficiency and the like. Through carrying sensors such as high definition camera, thermal infrared imager, the unmanned aerial vehicle can gather bridge structure apparent image fast, combines image recognition algorithm automated inspection disease, has promoted inspection efficiency greatly. However, unmanned aerial vehicles face a key technical bottleneck in the process of bridge inspection, namely the bottom of a bridge, the periphery of a pier and the inside of a box girder are typical global navigation satellite system (Global Navigation SATELLITE SYSTEM, GNSS) signal rejection environments. When the unmanned aerial vehicle flies into these areas, satellite positioning signals are completely lost, resulting in the following technical problems: Positioning drift and accumulated error problem The current mainstream solution is to use visual SLAM or laser SLAM for relative positioning. The unmanned aerial vehicle perceives the environmental characteristics through the carried camera or laser radar, builds an environmental map in real time and estimates the position of the unmanned aerial vehicle. However, the SLAM method has the inherent problem of accumulated errors that the positioning errors are gradually amplified along with the increase of the flight distance, and the requirements of disease centimeter-level accurate positioning cannot be met. When the unmanned aerial vehicle flies inside the box girder which is as long as tens of meters, the terminal positioning error can reach the meter level, so that the disease position cannot be accurately traced. Poor environmental adaptability The bottom of the bridge is often insufficient in illumination and serious in shadow shielding, and the surface of the bridge pier may be stained, moss and the like, and the factors can cause visual recognition failure. The internal structure of the box girder is repeated, the texture is deficient, and the visual SLAM is extremely easy to lose lock. Although the laser SLAM is not affected by illumination, the performance of the laser SLAM in smoke and water mist environments is reduced, the equipment cost is high, the power consumption is high, and the cruising ability of the unmanned aerial vehicle is limited. Active device dependency problem In order to solve the problem of GNSS blind area positioning, a scheme for arranging pseudolites or UWB base stations is proposed. The drone achieves positioning by receiving the signals emitted by these active devices. However, the scheme has obvious defects that the pseudolites and the UWB base stations need to be powered and periodically maintained, the practicability is poor in an environment which is difficult to maintain frequently, such as a bridge, the equipment layout cost is high, the equipment is difficult to popularize and apply in large-scale bridge inspection, and the active equipment also has the problems of signal interference, electromagnetic compatibility and the like. Interference problem of bridge structure to magnetic field Ferromagnetic materials such as steel bars are used in large quantities in bridge structures. According to the magnetic field mirror theory, the ferromagnetic substances can generate equivalent mirror sources in space, and generate a distortion effect on the original magnetic field of the embedded magnetic beacon. The interference magnetic field and the beacon magnetic field have the same frequency, are difficult to separate by a conventional filtering method, and seriously affect the magnetic positioning precision. An effective compensation scheme for bridge reinforcement interference is not available at present. Problem of correlation of disease location with BIM model Disease images collec