A multi-unmanned aerial vehicle cooperative surveying method and system for a region along a railway

Abstract

Embodiments of the present application provide a kind of multi-unmanned aerial vehicle cooperative surveying and mapping method and system for the area along railway, belong to railway surveying and mapping technical field.The method comprises: unmanned aerial vehicle acquires monitoring data in flight process;Dynamic adjustment the relative position and attitude between aircraft, build the observation configuration with change baseline parameter;Control the laser ranging module of each unmanned aerial vehicle carried out synchronous ranging operation, generate multiple sets of laser point cloud data covering railway structure area;The coordinate registration and fusion of the laser point cloud data collected are carried out, and the position information and attitude information of each unmanned aerial vehicle are combined;From the three-dimensional model after compensation, the geometric dimension parameter of railway structure is extracted, and the geometric dimension parameter is output as measurement result.The present application scheme realizes the high-precision non-contact measurement of the geometric dimension parameter such as track gauge, height difference, lateral angle of railway track structure.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of railway surveying technology, in particular to a multi-unmanned aerial vehicle cooperative surveying method for a railway line area and a multi-unmanned aerial vehicle cooperative surveying system for a railway line area. BACKGROUND

[0002] The geometric structure parameters of a railway line, including track gauge, height difference, roll angle, etc., are core indicators for ensuring train operation safety and evaluating line conditions. The non-contact measurement methods widely used at present are mostly based on image acquisition and visual reconstruction technology. After image data is collected along the railway line by a track inspection vehicle, a track robot or a low-altitude flight platform equipped with a camera device, a three-dimensional model is reconstructed by algorithms such as stereo vision, structured light or SfM (Structure from Motion), and the structure parameters are extracted.

[0003] However, in practical applications, there are still two key technical problems with such methods. First, the image reconstruction method is extremely sensitive to shooting angles, lighting conditions and feature contrast. In complex environments (such as tunnel sections, bridge sections and curve sections), image blurring, insufficient parallax or feature extraction failure may easily occur, resulting in incomplete reconstruction model structure or large coordinate errors. Second, the observation configuration of multi-view data lacks effective control, and the baseline parameters between different measurement nodes fluctuate greatly, resulting in unstable quality of the obtained parallax information, making it difficult to standardize spatial registration and error compensation in the three-dimensional reconstruction process, and affecting the final geometric measurement accuracy.

[0004] Therefore, the technical problem to be solved is how to stably control the measurement viewing angle, observation configuration and measurement data quality in the complex structure area along the railway line, so as to improve the reliability and accuracy of geometric measurement. SUMMARY

[0005] The purpose of the embodiments of the present application is to provide a multi-unmanned aerial vehicle cooperative surveying method and system for a railway line area, to at least solve the problems of uncontrollable multi-view measurement configuration and unstable three-dimensional reconstruction accuracy in the prior art.

[0006] To achieve the above-mentioned purpose, the first aspect of the present application provides a multi-unmanned aerial vehicle cooperative surveying method for a railway line area, the method comprising: deploying a plurality of unmanned aerial vehicles equipped with laser ranging modules along the railway line area, and controlling each unmanned aerial vehicle to collect monitoring data including laser ranging data, unmanned aerial vehicle position information and unmanned aerial vehicle attitude information during flight; during the flight of each unmanned aerial vehicle, dynamically adjusting the relative positions and attitudes between the vehicles according to the structure characteristics of the target area, to construct an observation configuration with varying baseline parameters, so as to realize the optimized collection of multi-angle laser ranging data.

[0007] After reaching the target observation configuration, the laser ranging modules carried by the unmanned aerial vehicles are controlled to perform synchronous ranging operations to generate multiple sets of laser point cloud data covering the railway structure region; the collected laser point cloud data are subjected to coordinate registration and fusion, and combined with the position information and attitude information of the unmanned aerial vehicles, the three-dimensional reconstruction error caused by the change in the observation configuration is compensated; the geometric size parameters of the railway structure are extracted from the compensated three-dimensional model, and the geometric size parameters are output as the measurement results.

[0008] Optionally, when the unmanned aerial vehicles are controlled to collect monitoring data during flight, the method further comprises: based on a unified time synchronization strategy, the laser ranging information, the unmanned aerial vehicle position information and the unmanned aerial vehicle attitude information are synchronously collected, and a unified format time label is attached to each type of data; the laser ranging information, the unmanned aerial vehicle position information and the unmanned aerial vehicle attitude information with the unified time label are sorted into independent data frames respectively; the data frames are organized in the order of the time label to construct a time series form of measurement data set.

[0009] Optionally, the relative positions and attitudes between the aircrafts are dynamically adjusted according to the structure characteristics of the target region, comprising: identifying the structure type of the target region, and selecting a configuration parameter group according to the structure type; wherein the structure characteristics include geometric shape characteristics and environmental structure characteristics of the track; the geometric shape characteristics include straight line segments and curved line segments, and the environmental structure characteristics include bridge regions and tunnel regions; the configuration parameter group includes multiple preset baseline length and attitude angle combinations; a configuration parameter group that meets the adaptive conditions of the target region is selected according to the current flight state, and the changes of the flight path and the attitude angle are controlled to obtain the measurement data under the configuration.

[0010] Optionally, the observation configuration with variable baseline parameters is constructed, comprising: adjusting the spatial distance between the aircrafts according to the variation of the point cloud density in the current region; when the point cloud density in a certain region is lower than a set threshold, the relative positions or attitude directions between adjacent aircrafts are adjusted.

[0011] Optionally, the collected laser point cloud data are subjected to coordinate registration and fusion, comprising: calculating initial space transformation parameters based on the unmanned aerial vehicle position information and the unmanned aerial vehicle attitude information associated with the ranging information; uniformly transforming the point cloud coordinates corresponding to each laser ranging information to the same space reference system; performing iterative error optimization on the basis of the preliminary registration to adjust the space transformation parameters and update the point cloud coordinate values, and completing the data fusion.

[0012] Optionally, the position information and the attitude information of each unmanned aerial vehicle are combined to compensate for the three-dimensional reconstruction error caused by the change of the observation configuration, including: for each set of laser ranging information, obtaining the corresponding unmanned aerial vehicle position information and unmanned aerial vehicle attitude information; calculating the change of the ranging direction in the three-dimensional space based on the unmanned aerial vehicle attitude information; calculating the elastic deformation of the rack based on the force state of the aircraft body during flight; correcting the actual spatial position and direction of the sensor based on the elastic deformation; performing attitude vector inverse transformation and body deformation compensation on the point cloud coordinate value, updating the three-dimensional coordinate value corresponding to the ranging information, and forming the error-compensated point cloud data.

[0013] Optionally, the geometric size parameters of the railway structure are extracted from the compensated three-dimensional model, including: extracting a set of spatial points representing the track area from the point cloud data in the compensated three-dimensional model; determining a plurality of cross-sectional profiles in the set of spatial points; performing curve fitting operation on the profile point set, and calculating the distance between the left and right boundary points, the height difference and the inclination value based on the fitting result.

[0014] Optionally, the method further comprises: performing regional block processing on a plurality of measurement data frames corresponding to the laser ranging information, dividing each measurement data frame into a plurality of spatial sub-regions; performing density estimation on the corresponding laser points in each spatial sub-region, and marking the position of the low-density or hollow region as the region with sparse data after comparison with each preset standard; based on the region with sparse data, adjusting the relative flight path between the unmanned aerial vehicles, so that the subsequent laser ranging information collection covers the region with sparse data in space.

[0015] The second aspect of the application provides a multi-unmanned aerial vehicle cooperative surveying and mapping system for a railway along area, the system comprising: an acquisition unit for deploying a plurality of unmanned aerial vehicles carrying laser ranging modules along the railway area, and controlling each unmanned aerial vehicle to collect monitoring data including laser ranging data, unmanned aerial vehicle position information and unmanned aerial vehicle attitude information during flight; an observation configuration construction unit for dynamically adjusting the relative position and attitude between the aircrafts according to the structure characteristics of the target area during the flight of each unmanned aerial vehicle, constructing an observation configuration with variable baseline parameters, to realize the optimized collection of multi-angle laser ranging data; a processing unit for controlling the laser ranging modules carried by each unmanned aerial vehicle to perform synchronous ranging operation after reaching the target observation configuration, to generate a plurality of sets of laser point cloud data covering the railway structure area; a three-dimensional reconstruction unit for performing coordinate registration and fusion on the collected laser point cloud data, and combining the position information and the attitude information of each unmanned aerial vehicle to compensate for the three-dimensional reconstruction error caused by the change of the observation configuration; a reading unit for extracting the geometric size parameters of the railway structure from the compensated three-dimensional model, and outputting the geometric size parameters as the measurement results.

[0016] In another aspect, the present application provides a computer readable storage medium, which stores instructions that, when executed on a computer, cause the computer to perform the above-mentioned multi-unmanned aerial vehicle cooperative mapping method for a railway line area.

[0017] Through the above technical solution, the present application scheme realizes multi-angle and high-density laser ranging data collection of the structure along the railway line through multi-unmanned aerial vehicle cooperative deployment and flight control. By dynamically adjusting the relative positions and attitudes between the aircrafts, an observation configuration with varying baseline parameters is constructed, effectively improving the measurement parallax and spatial coverage. Based on the multiple sets of laser point cloud data generated by the synchronous ranging operation, combined with the unmanned aerial vehicle position information and attitude information, coordinate registration and error compensation are performed, which can reduce the reconstruction deviation caused by the change of the configuration. Finally, the structure parameters such as track gauge and high-low difference can be extracted from the high-precision point cloud model, and the stability and precision of the railway geometric measurement are improved.

[0018] Other features and advantages of the present application will be described in detail in the following specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0019] The accompanying drawings are included to provide a further understanding of the present application and constitute a part of the specification, which together with the specific embodiments below, serve to explain the present application, but do not constitute a limitation on the present application. In the drawings:

[0020] Figure 1 is a step flowchart of the multi-unmanned aerial vehicle cooperative mapping method for a railway line area provided by an embodiment of the present application;

[0021] Figure 2 is a system structure diagram of the multi-unmanned aerial vehicle cooperative mapping system for a railway line area provided by an embodiment of the present application. DETAILED DESCRIPTION

[0022] The specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present application, and do not limit the present application.

[0023] Figure 1 is a method flowchart of the multi-unmanned aerial vehicle cooperative mapping method for a railway line area provided by an embodiment of the present application. As shown in Figure 1 the present application embodiment provides a multi-unmanned aerial vehicle cooperative mapping method for a railway line area, which comprises:

[0024] Step S10: deploying multiple unmanned aerial vehicles carrying laser ranging modules in the railway line area, and controlling each unmanned aerial vehicle to collect monitoring data including laser ranging data, unmanned aerial vehicle position information and unmanned aerial vehicle attitude information during flight.

[0025] Specifically, when controlling each unmanned aerial vehicle to collect monitoring data during flight, the method further comprises: based on a unified time synchronization strategy, synchronously collecting laser ranging information, unmanned aerial vehicle position information and unmanned aerial vehicle attitude information, and adding a time label in a unified format to each type of data; arranging the laser ranging information, the unmanned aerial vehicle position information and the unmanned aerial vehicle attitude information with the unified time label into independent data frames respectively; and organizing the data frames in the order of the time label to construct a measurement data set in the form of a time sequence.

[0026] In the implementation of the present application, a plurality of unmanned aerial vehicles with laser ranging capability are deployed in the area along the railway line, which can realize non-contact spatial measurement of large-scale track structure. In the present application, by controlling a plurality of unmanned aerial vehicles to collect monitoring data including laser ranging information, unmanned aerial vehicle position information and unmanned aerial vehicle attitude information during flight, raw data support is provided for subsequent construction of a three-dimensional model and extraction of geometric size parameters. In order to ensure the consistency of multi-source monitoring data in time and space, a unified time synchronization strategy is introduced in the present application to coordinate the collection and unified identification of various types of measurement data.

[0027] Specifically, during the flight of the unmanned aerial vehicle, the laser ranging information records the distance information returned after the laser pulse emitted by the current ranging device towards the ground or the structure, which has high time sensitivity. In order to ensure that this information corresponds to the position and attitude of the unmanned aerial vehicle at the moment of measurement, it is necessary to synchronously record the spatial position information (such as three-dimensional coordinates or longitude and latitude data) and attitude information (including pitch angle, roll angle and yaw angle) at the moment of flight. Based on a unified time reference source, such as GNSS timing or a high-stability internal clock, the present application synchronously labels all types of data involved in the measurement.

[0028] During the synchronous collection process, a time label in a unified format, i.e. a time stamp, is added to each set of laser ranging information, unmanned aerial vehicle position information and unmanned aerial vehicle attitude information. This time label has uniqueness and global consistency, and can correctly correspond different sources of data to the same sampling time in the subsequent processing link. In order to ensure data processing efficiency and clear structure logic, all collected data are encapsulated into independent data frames, each frame containing a set of laser ranging values, unmanned aerial vehicle position coordinates and attitude angle values at the corresponding time.

[0029] Further, all time-labeled data frames are organized in time sequence and constructed into a measurement data set in the form of time series. The time series not only preserves the spatial continuity of the laser point cloud data, but also provides a basis for subsequent point cloud splicing, attitude compensation and coordinate registration. In this way, not only can each laser point be completely matched with its flight state at the time of collection, but also the generation process of each measurement point in space can be accurately tracked when constructing a three-dimensional point cloud model, reducing geometric errors caused by attitude drift or measurement delay.

[0030] Based on the scheme of the present application, the scheme of the present application effectively solves the problems of data misalignment, time drift, and attitude and ranging decoupling commonly encountered in multi-unmanned aerial vehicle cooperative measurement, ensures the consistency of the collected data in the time domain and the spatial domain, and thus improves the accuracy and stability of subsequent three-dimensional model construction. Through unified time control and data frame construction, data fusion under different flight paths and different attitudes can be supported, so that the geometric continuity and spatial integrity of the measurement data can still be maintained in complex railway structure areas (such as curve sections, tunnel sections or bridge sections), providing a reliable data basis for high-precision extraction of key geometric parameters such as track gauge, height difference and roll angle.

[0031] Step S20: During the flight of each unmanned aerial vehicle, the relative positions and attitudes between the aircrafts are dynamically adjusted according to the structural characteristics of the target area, and an observation configuration with varying baseline parameters is constructed to realize the optimized collection of multi-angle laser ranging data.

[0032] Specifically, dynamically adjusting the relative positions and attitudes between the aircrafts according to the structural characteristics of the target area includes: identifying the structure type of the target area, and selecting a configuration parameter group according to the structure type; wherein the structural characteristics include geometric morphological characteristics of the track and environmental structural characteristics; the geometric morphological characteristics include straight sections and curved sections, and the environmental structural characteristics include bridge regions and tunnel regions; the configuration parameter group includes a plurality of preset baseline length and attitude angle combinations; a configuration parameter group that meets the target area adaptation condition is selected according to the current flight state, and the changes of the flight path and the attitude angle are controlled to obtain the measurement data under the configuration.

[0033] Further, constructing an observation configuration with varying baseline parameters includes: adjusting the spatial distance between the aircrafts according to the variation of the point cloud density in the current area; when the point cloud density in a certain area is lower than a set threshold, the relative positions or attitude directions between adjacent aircrafts are adjusted.

[0034] In the embodiment of the application, in the process of performing the mapping task of the area along the railway line, in order to obtain laser ranging data with high spatial coverage and measurement accuracy, the flight configuration between multiple unmanned aerial vehicles needs to be dynamically adjusted according to the structural characteristics of different terrain environments. The scheme of the application constructs an observation configuration with variable baseline parameters, so that in the whole flight process, each unmanned aerial vehicle can flexibly change its relative position and attitude according to the changes of the actual task area, to realize multi-angle and multi-parallax laser ranging data acquisition, thereby improving the spatial solving capability of the measurement area and the integrity of the point cloud data.

[0035] The scheme of the application identifies the structure type of the target area during flight. The structure identification is based on the spatial form characteristics of the railway line along the line, mainly including two types of information: one is the geometric form characteristics of the track, such as straight line segment, horizontal curve segment, vertical curve segment, etc.; the other is the environmental structure characteristics, such as open scene, tunnel section, bridge structure, station area, etc. By combining navigation data, pre-stored track geographic information or rapid pre-scanning performed before the task starts, the type of the measurement area to be entered can be classified.

[0036] For different area types, the method predefines multiple configuration parameter groups. The configuration parameter group includes multiple control quantities for describing the spatial arrangement between unmanned aerial vehicles, mainly including the following types:

[0037] 1) Baseline length range, i.e. the relative distance between two unmanned aerial vehicles in the horizontal plane, used to control the observation parallax.

[0038] 2) Observation angle range, i.e. the angle formed between the laser ranging directions of each unmanned aerial vehicle, used to adjust the ranging angle coverage range.

[0039] 3) Attitude angle setting, i.e. the pitch angle, roll angle and yaw angle parameters of each unmanned aerial vehicle, used to construct different ranging view.

[0040] 4) Relative height difference, used for height adjustment when there is a risk of obstruction or when it is desired to increase the vertical parallax distribution.

[0041] Each configuration parameter group corresponds to a typical structure area, for example, a linear array configuration is used in a straight line segment, a fan-shaped arrangement is used in a curve segment, and a near-parallel flight mode is used in a tunnel to avoid reflection interference.

[0042] During flight, the method selects a matching configuration parameter set according to the current flight state (including position, attitude, speed, etc.) and the prediction result of the type of the region ahead. Once the target configuration is confirmed, the control logic starts the corresponding adjustment strategy to instruct the UAV to adjust its relative position or attitude angle. For example, if entering a curve segment, the original parallel formation is adjusted to an observation formation with a certain included angle; if entering a tunnel region, the baseline length is reduced and the laser ranging direction is made as parallel as possible to avoid ranging interference caused by curved structures.

[0043] After completing the selection and preliminary adjustment of the configuration parameter set, to further improve the effectiveness of the ranging data, the scheme of the present application also dynamically corrects the configuration according to the density distribution of the real-time point cloud data. The point cloud density can be evaluated by counting the number of valid laser reflection points in each unit voxel (such as 0.5m³). When the point cloud density of a certain region is continuously below a set threshold (for example, less than N points per cubic meter), it is determined that there is a ranging dead angle or insufficient spatial coverage problem in that region.

[0044] At this time, the method will be corrected in the following two ways:

[0045] 1) Adjust the relative distance between adjacent UAVs, such as appropriately shortening the baseline to increase the overlap rate of parallax on the same structural details.

[0046] 2) Adjust the attitude direction, such as changing the ranging angle to bypass obstructions or enhance the ability to measure obliquely. This configuration adaptive adjustment based on point cloud data feedback improves the scene adaptability of the observation configuration, enabling the UAV group to maintain high-quality coverage of the target structure even when facing variable terrain.

[0047] In addition, to ensure data continuity during configuration transformation, the scheme of the present application adopts a frame-by-frame configuration transition mechanism. That is, the target configuration parameters are used as the target state of the next frame configuration, and the flight path and attitude angle are adjusted gradually by a certain step size to avoid interruptions in data collection or reconstruction errors caused by large configuration jumps. This mechanism allows the UAV to continue stable laser ranging data collection during configuration adjustment. The implementation of the scheme of the present application also includes a configuration rationality verification step. By calculating the global spatial coverage, laser ranging direction intersection angle distribution, and parallax distribution indicators of the current observation configuration, it is determined whether the configuration meets the three-dimensional reconstruction input conditions, such as minimum baseline length, maximum parallax coverage rate, etc. If the verification fails, it automatically reverts to the previous stable configuration or selects other backup schemes from the configuration template library to perform replacement.

[0048] Based on the scheme, the scheme introduces structural adaptability through dynamic configuration control in the laser ranging data acquisition stage, significantly improves the point cloud coverage and measurement angle diversity in complex areas (such as bridge connection sections, curved ramps, and tunnel exits). Compared with fixed flight paths or uniform configuration methods, this configuration adaptive strategy can optimize the ranging direction and configuration matching according to the target structure characteristics, effectively alleviating measurement obstacles such as ranging blind area, insufficient reconstruction disparity, and severe occlusion. In the subsequent coordinate registration, point cloud merging, and error compensation steps, the configuration controllability is enhanced, and the three-dimensional model stability and geometric measurement reliability are higher.

[0049] Step S30: After reaching the target observation configuration, the laser ranging modules carried by each unmanned aerial vehicle are controlled to perform synchronous ranging operation, generating multiple sets of laser point cloud data covering the railway structure area.

[0050] Specifically, after reaching the target observation configuration, synchronous ranging operation between multiple aircrafts needs to be performed to ensure the consistency of laser ranging data in space and time dimensions. In the preparation stage of synchronous ranging, a unified ranging trigger timing is preset for each aircraft, which is constructed based on a shared time reference and can be initialized before flight starts through a high-precision time service mechanism. Each aircraft continuously corrects its own clock and the unified time reference during flight to ensure that the ranging command is responded synchronously at the trigger time.

[0051] Specifically, when performing synchronous ranging, all aircrafts complete laser beam emission and echo reception within a predetermined time window, and record the corresponding distance information, acquisition time, three-dimensional position, and attitude angle in the same coordinate reference system. To improve spatial resolution, multiple laser emissions can be performed within each synchronous acquisition period, and each set of laser data corresponds to a point cloud subset. Multiple aircrafts measure the same structure area at different attitudes and relative positions simultaneously, and can obtain laser point cloud data observed from multiple angles, enhancing the spatial sampling density and feature expression ability of the structure surface.

[0052] Based on the scheme, the use of the synchronous ranging method not only improves the timing consistency between different point cloud data, but also ensures the spatial correlation and reconstruction consistency between point clouds, effectively improves the geometric reconstruction accuracy of railway structures under multi-angle measurement, and establishes a reliable data foundation for subsequent point cloud fusion and error compensation.

[0053] Step S40: Coordinate registration and fusion of the collected laser point cloud data, and compensation of three-dimensional reconstruction errors caused by changes in observation configuration combined with the position information and attitude information of each unmanned aerial vehicle.

[0054] Specifically, the collected laser point cloud data is subjected to coordinate registration and fusion, including: calculating initial space transformation parameters based on the unmanned aerial vehicle position information and the unmanned aerial vehicle attitude information associated with the ranging information; uniformly transforming the point cloud coordinates corresponding to each laser ranging information to the same space reference system; performing iterative error optimization on the basis of preliminary registration to adjust the space transformation parameters and update the point cloud coordinate values, and completing data fusion.

[0055] Further, combined with the position information and attitude information of each unmanned aerial vehicle, the three-dimensional reconstruction error caused by the change of the observation configuration is compensated, including: for each set of laser ranging information, obtaining the corresponding unmanned aerial vehicle position information and unmanned aerial vehicle attitude information; calculating the change of the ranging direction in the three-dimensional space based on the unmanned aerial vehicle attitude information; calculating the elastic deformation variable of the aircraft body according to the force state of the aircraft body in the flight process; correcting the actual space position and direction of the sensor based on the elastic deformation variable; performing attitude vector inverse transformation and body deformation compensation on the point cloud coordinate values, updating the three-dimensional coordinate values corresponding to the ranging information, and forming error-compensated point cloud data.

[0056] In the embodiment of the application, the collected laser point cloud data is subjected to coordinate registration and fusion, which is a key step to realize high-precision three-dimensional reconstruction of railway structures. Since multiple aircrafts collect laser ranging information at different positions and different attitudes, the generated point cloud data original coordinates are distributed in their respective local coordinate systems, and there is a certain space deviation caused by attitude error, position drift and configuration change. Therefore, it is necessary to unify each local point cloud to the same global coordinate reference system, and through a multi-level registration and error compensation mechanism, the consistency and geometric precision of the fused point cloud are improved.

[0057] In the initial registration stage, the space transformation parameters of each set of laser ranging information associated with the unmanned aerial vehicle position information and the unmanned aerial vehicle attitude information at the time of collection are calculated. The position information provides a three-dimensional translation vector, and the attitude information includes a pitch angle, a roll angle and a yaw angle three-dimensional rotation amount. By converting the attitude angle into a rotation matrix, a homogeneous transformation matrix of each data frame from the local coordinate system to the target reference system can be constructed. Multiply the three-dimensional coordinate vector in each point cloud by the transformation matrix to obtain the point cloud data unified to the world coordinate system. This step decouples the multi-source point cloud from the respective unmanned aerial vehicle reference system, forming an initial alignment of the point cloud with global spatial significance.

[0058] After the initial transformation, to improve the fusion quality, an iterative error optimization is performed in the unified coordinate system. This optimization process is based on the point pairs in the spatial overlap area, and the residual vectors between adjacent point clouds are calculated by the Iterative Closest Point (ICP) algorithm or its improved variants (such as weighted ICP, color ICP, normal ICP). By minimizing these residuals, the rotation and translation parameters in the spatial transformation matrix are optimized, thus correcting the cumulative errors in the initial transformation. This process can be performed locally to correct the point cloud overlap errors of adjacent aircraft, or globally optimized to improve the overall model consistency. During the registration optimization process, multiple registration accuracy thresholds can be set to gradually refine the error convergence range, achieving fine alignment of the fused point cloud in spatial continuity and local geometric structure.

[0059] After completing the coordinate registration and point cloud fusion, there may still be residual three-dimensional reconstruction errors caused by changes in the observation configuration. To further improve the spatial accuracy of the point cloud, the drone position information and attitude information for each set of laser ranging information need to be combined to compensate for the configuration error. For each set of laser ranging information, the corresponding drone position information and attitude information need to be obtained simultaneously. The position information is generally represented as a spatial three-dimensional position calculated by GNSS coordinates or an inertial navigation system (INS), and the attitude information typically includes pitch angle, roll angle, and yaw angle to describe the orientation of the drone in space. At this time, it is necessary to ensure that the position and attitude data have a unified time label to ensure the spatio-temporal correspondence of the data.

[0060] Based on the obtained drone attitude information, the change of the laser ranging direction in three-dimensional space can be calculated. Specifically, the attitude angle can be converted into a rotation matrix, and the fixed emission direction vector of the laser in the body coordinate system can be transformed to the ground reference frame through the rotation matrix, thus obtaining the real emission direction of each ranging beam in three-dimensional space. This step is a basic process for correctly projecting local ranging data into the global coordinate system.

[0061] Further, the elastic deformation effect of the aircraft body due to changes in aerodynamic force, load, or environmental temperature during flight is considered. To calculate the elastic deformation of the rack, acceleration data, attitude change rate, wind speed information, and external temperature data during flight can be referenced to establish a simplified rack elastic model. For example, the rack can be considered as a simplified beam structure model, and the micro displacement of each node can be calculated according to the conventional mechanics formula. With small-angle approximation, the deflection change of the rack can be calculated to obtain the elastic deformation of the order of microns to millimeters based on the external load and material stiffness parameters (such as Young's modulus).

[0062] After obtaining the elastic deformation amount of the rack, the actual spatial position and direction of the sensor need to be corrected based on the deformation amount. Specifically, on the basis of the sensor installation matrix obtained in the initial static calibration, a small pose change (such as a small translation amount and a small rotation angle) calculated in real time can be superimposed to obtain a dynamically updated sensor pose description. The emission origin position and the ranging direction vector of the sensor are thus finely adjusted to match the spatial displacement of the aircraft caused by the deformation of the rack during dynamic flight.

[0063] Subsequently, the original coordinate values of the laser point cloud are subjected to inverse pose transformation and body deformation compensation. First, the inverse pose transformation is applied to convert the ranging points from the local aircraft reference frame to the ground absolute reference frame. Then, the three-dimensional coordinates of each ranging point are further adjusted according to the sensor pose corrected by the elastic deformation. The adjustment method can use the vector superposition method, that is, the spatial displacement of the sensor caused by the deformation is added to the point cloud coordinates after the preliminary inverse transformation. Through the above process, the final error-compensated point cloud dataset can be formed. This dataset not only corrects the measurement error caused by the change of the aircraft attitude, but also further compensates for the measurement displacement caused by the elastic deformation of the aircraft body, which can effectively improve the spatial accuracy and consistency of the three-dimensional reconstruction model in the cooperative mapping of multiple unmanned aerial vehicles.

[0064] In one possible implementation, by using the above double compensation mechanism, under the flight conditions of wind speed higher than 5 m / s and unmanned aerial vehicle load change greater than 10%, the local spatial error of the point cloud data can be reduced by more than 30% on average, greatly improving the stability and reliability in extracting geometric parameters of track structures (such as track gauge, high-low difference, and roll angle). Especially in long-distance and multi-batch mapping tasks, elastic deformation compensation can effectively suppress the cumulative drift of point clouds, improve the consistency of the overall measurement results, and has important engineering practical value.

[0065] Based on the scheme of the present application, by using the above coordinate registration and error compensation method, the problem of inconsistent point cloud geometry caused by changes in observation configuration, attitude disturbance and position information error among multiple unmanned aerial vehicles can be effectively solved. Compared with the method of directly splicing point clouds without processing, this method significantly improves the overall structure alignment, detail fidelity and spatial geometric stability of the fused point cloud. Especially in curved sections, tunnel sections or areas with occluded structures, the point clouds in each direction can be effectively connected to improve the density and integrity of the overall model, providing precision assurance for subsequent geometric size parameter extraction (such as track gauge, high-low difference, and roll angle), and meeting the core demand of high-reliability three-dimensional data for non-contact railway measurement.

[0066] Step S50: Extracting geometric size parameters of the railway structure from the compensated three-dimensional model and outputting the geometric size parameters as measurement results.

[0067] Specifically, the geometric dimension parameters of the railway structure are extracted from the compensated three-dimensional model, including: extracting a set of spatial points representing the track area from the point cloud data in the compensated three-dimensional model; determining multiple cross-sectional profiles in the set of spatial points; performing a curve fitting operation on the profile point set, and calculating the distance, height difference and inclination value between the left and right boundary points based on the fitting results.

[0068] In this embodiment of the present invention, extracting the geometric dimensions of railway structures from the compensated 3D model is a key step in the entire surveying and mapping process, directly related to the quantitative analysis and assessment of the railway line's structural condition. After completing point cloud coordinate registration and attitude error compensation, a set of laser point cloud data with a unified coordinate reference, high geometric accuracy, and spatial integrity is obtained. This dataset represents the spatial morphology of the railway structures along the line in three dimensions. Based on this, geometric analysis methods are used to extract and quantify key structural areas, resulting in multiple geometric indicators such as track gauge, height difference, and roll angle, which serve as the final output of the railway survey.

[0069] Specifically, the first step is to identify a set of spatial points representing the track structure in the three-dimensional point cloud data. This step can be accomplished by combining methods such as setting a ground height threshold, filtering point density features, and geometric morphology recognition. Since the track structure appears in the point cloud as a high-density, parallel, strip-like region extending longitudinally, the consistency of the surface normal distribution and the spacing between structural lines can be used to extract the point cloud segments of the two rails and construct the spatial boundary of the track area. To eliminate the influence of non-target structures (such as trackside facilities and ground interference objects) on subsequent calculations, the extracted results must be spatially filtered and outliers removed.

[0070] After extracting the track area point cloud, multiple cross-sectional point sets are captured at regular intervals along the track's longitudinal direction. Each cross-sectional point set contains the left and right rail structural points at the current cross-sectional location. By projecting the point cloud into the cross-sectional coordinate system, an approximate two-dimensional cross-sectional structure of the track can be obtained. Curve fitting is performed on each set of cross-sectional point clouds, using methods such as quadratic polynomials, spline interpolation, and circular arc fitting to describe the rail contour boundary shape. During the fitting process, the rail head or tread edge points are preferentially identified as structural references to prevent stray points on the outside of the curve from interfering with the fitting results.

[0071] After curve fitting is complete, structural parameter calculations are performed based on the geometric relationships between the fitted curves. Track gauge is calculated as the shortest horizontal distance between the rail curves on either side of the profile, typically taking the Euclidean distance between representative points (such as the inner edge of the rail top). Height difference is determined by comparing the vertical elevation difference between the fitted curves on both sides of the rail surface within the same section, reflecting the unevenness of the track cross section. The roll angle is calculated based on the angle between the normal to the rail surface tangent and the horizontal plane, revealing the lateral tilt of the track structure.

[0072] Preferably, the method further comprises: performing regional block processing on the plurality of measurement data frames corresponding to the laser ranging information, dividing each measurement data frame into a plurality of spatial sub-regions; performing density estimation on the corresponding laser points in each spatial sub-region, and marking the positions of low-density or hollow regions as regions with sparse data after comparison with each preset standard; and adjusting the relative flight paths between the unmanned aerial vehicles based on the regions with sparse data, so that subsequent laser ranging information collection covers the regions with sparse data in space.

[0073] In the embodiments of the present application, after the preliminary laser ranging information collection is completed, in order to improve the integrity and structural coverage of the laser point cloud data in space, the collected data can be further subjected to regional block processing and spatial sparsity detection. This processing link is mainly used to find regions with insufficient coverage or low density in the point cloud data, and through optimization and adjustment of the flight path, the subsequent ranging task can focus on compensating these data sparse regions, thereby improving the spatial continuity and geometric accuracy of the overall three-dimensional model.

[0074] In specific operations, first, the plurality of measurement data frames corresponding to the laser ranging information are subjected to three-dimensional grid division according to a uniform spatial resolution. The point cloud data in each measurement data frame is divided into a plurality of spatial sub-regions with a fixed voxel size, for example, the voxel edge length is set to 0.5 meters or 1 meter. The number of laser points contained in each sub-region is counted and used as a density value to reflect the spatial sampling situation of the sub-region. To improve the statistical accuracy, the data of multiple frames can be superimposed and calculated to form a time-fused density map.

[0075] Subsequently, the point cloud density of each spatial sub-region is compared with a preset standard threshold. The threshold is preset according to the measurement scene and structural complexity, for example, it is set that at least 100 valid points per cubic meter are required in the track structure region, and if the number of points in a region is insufficient, it is marked as a "low-density region" or a "data hollow region". In addition, for a spatial segment with a plurality of low-density sub-regions in succession, it can be further identified as a region with serious structural obstruction or ranging dead angle, which needs to be compensated by configuration or path adjustment.

[0076] After the marking is completed, according to the spatial distribution result of the sparse area, the path optimization adjustment of the next stage of the ranging task is carried out. The adjustment strategy can include: appropriately reducing the lateral spacing between the unmanned aerial vehicles to improve the coverage of the overlapping observation area; adjusting the attitude angle or the overhead angle of part of the aircraft to change the laser beam irradiation direction, to compensate for the area that was originally blocked or the area with insufficient measurement angle; additional scanning paths can also be inserted above the sparse area to form a local coverage enhancement section. The data acquisition adjustment mechanism based on the feedback of the sparse area has high adaptability and local compensation ability, and is especially suitable for measurement scenes with complex structures and frequent blockages (such as bridge connection sections, track intersection sections or side wall blocked sections). Compared with the traditional uniform scanning path, this strategy can significantly improve the density and completeness of the point cloud data in the key parts of the structure, and reduce the reconstruction error caused by data loss.

[0077] Based on the scheme of the present application, by carrying out regional block and sparsity analysis on laser ranging data, not only the automatic identification of the measurement blind area is realized, but also a closed-loop mechanism for driving the flight path adjustment from the measurement results is constructed. This method improves the data closure degree of the ranging task and the boundary integrity of the point cloud model, and further enhances the stability and reliability of the subsequent geometric parameter extraction, providing a solid data guarantee for high-precision modeling and structural analysis of the structures along the railway.

[0078] Figure 2 is the system structure diagram of a multi-unmanned aerial vehicle cooperative mapping system for a railway area provided by an embodiment of the present application. As shown in Figure 2 The present application provides a multi-unmanned aerial vehicle cooperative mapping system for a railway area, which comprises: an acquisition unit for deploying a plurality of unmanned aerial vehicles carrying laser ranging modules along the railway area, and controlling each unmanned aerial vehicle to acquire monitoring data including laser ranging data, unmanned aerial vehicle position information and unmanned aerial vehicle attitude information during flight; an observation configuration construction unit for dynamically adjusting the relative positions and attitudes between the aircrafts according to the structural characteristics of the target area during the flight of each unmanned aerial vehicle, constructing an observation configuration with varying baseline parameters, to realize the optimized acquisition of multi-angle laser ranging data; a processing unit for controlling the laser ranging modules carried by each unmanned aerial vehicle to perform synchronous ranging operation after reaching the target observation configuration, to generate a plurality of sets of laser point cloud data covering the railway structure area; a three-dimensional reconstruction unit for performing coordinate registration and fusion on the acquired laser point cloud data, and combining the position information and attitude information of each unmanned aerial vehicle to compensate for the three-dimensional reconstruction error caused by the change of the observation configuration; and a reading unit for extracting the geometric size parameters of the railway structure from the compensated three-dimensional model, and outputting the geometric size parameters as the measurement results.

[0079] The embodiment of the present application further provides a computer readable storage medium, which stores instructions, and the instructions make the computer execute the above-mentioned method for cooperative surveying of a railway area by multiple unmanned aerial vehicles when the instructions are run on the computer.

[0080] Those skilled in the art can understand that all or part of the steps of the method for implementing the above-mentioned embodiments can be completed by programs instructing relevant hardware, the programs are stored in a storage medium, and the programs include a plurality of instructions for making a single-chip microcomputer, a chip or a processor execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various storage media capable of storing program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0081] The optional embodiments of the present application are described in detail above in combination with the drawings, but the embodiments of the present application are not limited to the specific details in the above-mentioned embodiments, and various simple modifications can be made to the technical solutions of the embodiments of the present application within the technical concept range of the embodiments of the present application, and the simple modifications all belong to the protection range of the embodiments of the present application. In addition, it should be noted that each specific technical feature described in the above-mentioned specific embodiments can be combined in any appropriate manner without contradiction. In order to avoid unnecessary repetition, the embodiments of the present application will not be described again for various possible combination manners.

[0082] In addition, various different embodiments of the present application can also be combined in any manner, as long as the combination does not deviate from the technical concept of the embodiments of the present application, and the combination should also be regarded as the disclosed content of the embodiments of the present application.

Claims

1. A multi-UAV collaborative mapping method for areas along railways, characterized in that: The method comprises: Deploy multiple drones equipped with laser ranging modules along the railway line and control each drone to collect monitoring data including laser ranging data, drone position information, and drone attitude information during flight; During the flight of each UAV, the relative position and attitude between the aircraft are dynamically adjusted according to the structural characteristics of the target area, and an observation configuration with variable baseline parameters is constructed to achieve optimized collection of multi-angle laser ranging data; After reaching the target observation configuration, the laser ranging modules carried by each UAV are controlled to perform synchronous ranging operations to generate multiple sets of laser point cloud data covering the railway structure area; The collected laser point cloud data is coordinate registered and fused, and the position and attitude information of each UAV is combined to compensate for the 3D reconstruction error caused by the change of observation configuration. Combining the position and attitude information of each drone, the 3D reconstruction error caused by the change in the observed configuration is compensated. This includes: obtaining the corresponding drone position and attitude information for each set of laser ranging information; calculating the change in the ranging direction in 3D space based on the drone attitude information; inferring the elastic deformation of the frame based on the force state of the aircraft body during flight; correcting the actual spatial position and direction of the sensor based on the elastic deformation; performing an inverse attitude vector transformation and body deformation compensation on the point cloud coordinate values, updating the 3D coordinate values ​​corresponding to the ranging information, and forming error-compensated point cloud data; The geometrical dimension parameters of the railway structure are extracted from the compensated three-dimensional model and output as measurement results.

2. The multi-UAV collaborative mapping method for railway areas according to claim 1 is characterized in that: When controlling each UAV to collect monitoring data during flight, the method further includes: Based on a unified time synchronization strategy, laser ranging information, drone position information, and drone attitude information are collected synchronously, and time tags in a unified format are added to each type of data; The laser ranging information, UAV position information and UAV attitude information with unified time tags are organized into independent data frames; The data frames are organized in the order of time labels to construct a measurement dataset in the form of a time series.

3. The multi-UAV collaborative mapping method for railway areas according to claim 1 is characterized in that: Dynamically adjust the relative position and attitude of aircraft based on the structural characteristics of the target area, including: Identify the structural type of the target area and select a configuration parameter group according to the structural type; The structural features include the geometric features of the track and the environmental structural features; the geometric features include straight segments and curved segments, and the environmental structural features include bridge areas and tunnel areas; The configuration parameter group includes a plurality of preset baseline length and posture angle combinations; According to the current flight state, a configuration parameter group that meets the adaptation conditions of the target area is selected, and the changes in the flight path and attitude angle are controlled to obtain the measurement data under this configuration.

4. The multi-UAV collaborative mapping method for railway areas according to claim 1 is characterized in that: Construct observation configurations with varying baseline parameters, including: Adjust the spatial distance between aircraft based on the changes in point cloud density in the current area; When the point cloud density in a certain area is lower than the set threshold, the relative position or attitude direction between adjacent aircraft is adjusted.

5. The multi-UAV collaborative mapping method for railway areas according to claim 1 is characterized in that: Coordinate registration and fusion of collected laser point cloud data, including: Calculating initial spatial transformation parameters based on the UAV position information and the UAV attitude information associated with the ranging information; The point cloud coordinates corresponding to each laser ranging information are uniformly transformed into the same spatial reference system; Based on the preliminary registration, iterative error optimization is performed to adjust the spatial transformation parameters and update the point cloud coordinate values ​​to complete data fusion.

6. The multi-UAV collaborative mapping method for railway areas according to claim 1, characterized in that: Extract the geometric parameters of the railway structure from the compensated 3D model, including: Extracting a set of spatial points representing the track area from the point cloud data of the compensated three-dimensional model; determining a plurality of cross-sectional profiles in a set of spatial points; A curve fitting operation is performed on the profile point set, and the distance, height difference, and inclination angle between the left and right boundary points are calculated based on the fitting results.

7. The multi-UAV collaborative mapping method for railway areas according to claim 1, characterized in that: The method further comprises: Performing regional block processing on multiple measurement data frames corresponding to the laser ranging information, dividing each measurement data frame into multiple spatial sub-regions; Perform density estimation on the corresponding laser points in each spatial sub-region and mark the locations of low-density or empty areas after comparing with various preset standards as areas with sparse data; Based on the areas with sparse data, the relative flight paths between drones are adjusted so that the subsequent laser ranging information collection spatially covers the areas with sparse data in the previous round.

8. A multi-UAV collaborative mapping system for areas along railways, characterized by: The system comprises: The acquisition unit is used to deploy multiple drones equipped with laser ranging modules along the railway line and control each drone to collect monitoring data including laser ranging data, drone position information, and drone attitude information during flight; The observation configuration construction unit is used to dynamically adjust the relative position and attitude of each UAV according to the structural characteristics of the target area during the flight of each UAV, and to construct an observation configuration with variable baseline parameters to achieve optimized collection of multi-angle laser ranging data; The processing unit is used to control the laser ranging modules carried by each UAV to perform synchronous ranging operations after reaching the target observation configuration, thereby generating multiple sets of laser point cloud data covering the railway structure area; The 3D reconstruction unit is used to coordinate registration and fusion of the collected laser point cloud data, and to compensate for the 3D reconstruction error caused by the change of the observation configuration by combining the position information and attitude information of each UAV. Combining the position and attitude information of each drone, the 3D reconstruction error caused by the change in the observed configuration is compensated. This includes: obtaining the corresponding drone position and attitude information for each set of laser ranging information; calculating the change in the ranging direction in 3D space based on the drone attitude information; inferring the elastic deformation of the frame based on the force state of the aircraft body during flight; correcting the actual spatial position and direction of the sensor based on the elastic deformation; performing an inverse attitude vector transformation and body deformation compensation on the point cloud coordinate values, updating the 3D coordinate values ​​corresponding to the ranging information, and forming error-compensated point cloud data; The reading unit is used to extract geometrical dimension parameters of the railway structure from the compensated three-dimensional model and output the geometrical dimension parameters as measurement results.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the multi-UAV collaborative mapping method for an area along a railway as described in any one of claims 1 to 7.

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