BIM data acquisition method and system applied to highway engineering

Abstract

The invention relates to the technical field of electronic digital data processing, in particular to a BIM data collection method and system applied to highway engineering, and the method comprises the steps: obtaining collection data and flight data; calculating an acquisition influence degree according to the flight data; performing region segmentation and grouping on the point cloud data to obtain point cloud data of regions; calculating the surface form complexity of the region according to the point cloud data of the region; according to the surface form complexity degree and the acquisition influence degree of the region, calculating to obtain an acquisition data interference degree; performing perspective projection transformation processing on the point cloud data and the image of the region to obtain a high-dimensional image; and performing weighted fusion on the high-dimensional image according to the acquired data interference degree of the region to obtain a three-dimensional space coordinate set corresponding to the region. The method evaluates the collection time influence, divides the coverage area, and evaluates the earth surface complexity. The regional interference degree is calculated based on an evaluation result, point cloud data are fused, wind power influence is considered, and the data reliability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to a BIM data acquisition method and system applied to highway engineering. Background Art

[0002] With the rapid development of my country's infrastructure construction, highway projects, as a key component of the transportation network, continue to grow in scale and complexity. In order to improve the design accuracy, construction quality and operation and maintenance efficiency of highway projects, more and more projects are beginning to adopt building information modeling (BIM) technology. The application of BIM technology can realize the digitization, visualization and management integration of engineering information, thereby providing strong support for the full life cycle management of highway projects.

[0003] However, in the process of BIM data collection, due to the complex terrain or construction section restrictions, it is impossible to use vehicle-mounted laser radar for three-dimensional scanning, so drone scanning becomes an alternative. However, when collecting road surface information in the air according to a predetermined flight trajectory, drones are easily affected by wind, resulting in deviations in the collected road data, which in turn affects the accuracy of highway BIM data. At present, there is no effective technology to repair these data deviations. Therefore, there is an urgent need for a BIM data optimization collection method suitable for highway engineering. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a BIM data collection method and system for highway engineering. In order to achieve the above purpose, the technical solution adopted by the present invention is as follows: In the first aspect, the present application provides a BIM data collection method for highway engineering, the method comprising: obtaining collection data and flight data of a drone within a preset time period, the preset time period being a time period in which the drone flies at a constant speed along a target highway section at a preset speed, the collection data comprising point cloud data and images; calculating the collection influence degree at each moment based on the flight data and the images within the preset time period; performing regional segmentation based on the point cloud data corresponding to each moment, and performing grouping based on the segmentation results to obtain point cloud data when each region is collected multiple times; calculating the surface morphology complexity of each region based on the point cloud data when each region is collected multiple times, the surface morphology complexity being determined by the number of points in each region. The point cloud density distribution and the point cloud curvature difference during the single acquisition are calculated; the degree of interference of the acquisition data of each area in a single acquisition is calculated according to the complexity of the surface morphology of each area and the acquisition influence of the area in a single acquisition; the point cloud data and image of each area in a single acquisition are processed by perspective projection transformation, and linear interpolation is performed based on the processing result to obtain a high-dimensional image corresponding to each acquisition, and each pixel in the high-dimensional image corresponds to a mapped three-dimensional space coordinate; based on the degree of interference of the acquisition data of each area in a single acquisition and the high-dimensional image, weighted fusion is performed to obtain a set of three-dimensional space coordinates corresponding to each area, and the three-dimensional space coordinate set is used to generate a BIM model of the highway project.

[0005] In combination with the first aspect, in a possible implementation method, the acquisition influence degree of each moment is calculated based on the flight data and the image within the preset time period, including: calculating the image deviation degree corresponding to each moment based on the difference between adjacent images in time sequence; calculating the drone attitude deviation degree corresponding to each moment based on the flight data; and multiplying the image deviation degree and the drone attitude deviation degree corresponding to each moment to calculate the acquisition influence degree.

[0006] In combination with the first aspect, in a possible implementation method, the image deviation corresponding to each moment is calculated according to the difference between the adjacent images in time sequence, including: processing all the images according to the optical flow algorithm to obtain the optical flow vector of each pixel in each of the images; counting the number of optical flow vectors in each of the images to obtain the number of optical flows corresponding to each of the images; calculating the first optical flow mean vector and the second optical flow mean vector corresponding to each of the images according to the time sequence, the first optical flow mean vector is the mean of the optical flow vectors in a first preset time period, the second optical flow mean vector is the mean of the optical flow vectors in a second preset time period, the end times of the first preset time period and the second preset time period are both the acquisition time of the image, and the time length of the first preset time period is greater than the time length of the second preset time period; the image deviation corresponding to each of the images is calculated according to the first optical flow mean vector, the second optical flow mean vector, the number of optical flows and the optical flow vector of each pixel corresponding to each of the images.

[0007] In combination with the first aspect, in a possible implementation method, the flight data includes acceleration and angular velocity, and the UAV attitude deviation corresponding to each moment is calculated according to the flight data, including: performing mean calculations according to the acceleration and angular velocity at each moment in a preset time period to obtain the acceleration mean and the angular velocity mean; calculating the UAV attitude deviation corresponding to each moment according to the difference between the acceleration at each moment and the acceleration mean and the difference between the angular velocity and the angular velocity mean.

[0008] In combination with the first aspect, in a possible implementation method, the surface morphology complexity of each area is obtained based on the point cloud data of each area when it is collected multiple times, including: obtaining the point cloud density and the point cloud curvature variance at a single collection based on the point cloud data at a single collection, respectively; calculating the mean of the point cloud density and the point cloud curvature variance at multiple collections to obtain the density mean and the curvature variance mean; multiplying the density mean with the curvature variance mean to obtain the surface morphology complexity.

[0009] In combination with the first aspect, in a possible implementation method, the point cloud density and the point cloud curvature variance at a single collection are calculated based on the point cloud data at a single collection, including: performing density-based division processing on the point cloud data at a single collection to obtain the number of point cloud clusters, and using the number of point cloud clusters as the point cloud density; performing local surface fitting on each point at the single collection to obtain the curvature of each point; and calculating the curvature variance based on the variance of the curvatures of all points.

[0010] In combination with the first aspect, in a possible implementation manner, the number of point cloud clusters is obtained by processing a DBSCAN algorithm.

[0011] In the second aspect, the present application also provides a BIM data acquisition system for highway engineering, including: a data acquisition module, used to acquire the acquisition data and flight data of the drone within a preset time period, the preset time period is the time period in which the drone flies at a constant speed along the target highway section at a preset speed, and the acquisition data includes point cloud data and images; an influence factor calculation module, used to calculate the acquisition influence degree at each moment according to the flight data and the image within the preset time period; a grouping module, used to perform regional segmentation according to the point cloud data corresponding to each moment, and to perform grouping based on the segmentation results to obtain the point cloud data of each area when it is collected multiple times; a morphological factor calculation module, used to calculate the surface morphological complexity of each area according to the point cloud data when each area is collected multiple times, the surface morphological complexity The complexity is calculated by the point cloud density distribution and the point cloud curvature difference at each acquisition; the interference factor calculation module is used to calculate the interference degree of the collected data of each area at a single acquisition according to the complexity of the surface morphology of each area and the collection influence degree of the area at a single acquisition; the transformation processing module is used to perform perspective projection transformation on the point cloud data and image of each area at a single acquisition, and perform linear interpolation based on the processing result to obtain a high-dimensional image corresponding to each acquisition, and each pixel point in the high-dimensional image corresponds to a mapped three-dimensional space coordinate; the fusion calculation is based on the weighted fusion of the collected data interference degree of each area at a single acquisition and the high-dimensional image to obtain a three-dimensional space coordinate set corresponding to each area, and the three-dimensional space coordinate set is used to generate a BIM model of the highway project.

[0012] In combination with the second aspect, in a possible implementation method, the impact factor calculation module includes: an image deviation calculation module, which is used to calculate the image deviation degree corresponding to each moment according to the difference between adjacent images in time sequence; an attitude deviation calculation module, which is used to calculate the drone attitude deviation degree corresponding to each moment according to the flight data; a first calculation module, which is used to multiply the image deviation degree corresponding to each moment and the drone attitude deviation degree to obtain the acquisition influence degree.

[0013] In combination with the second aspect, in a possible implementation, the morphological factor calculation module includes: a point cloud computing module, which is used to calculate the point cloud density and point cloud curvature variance at a single collection based on the point cloud data at a single collection; a mean calculation module, which is used to perform mean calculation based on the point cloud density and point cloud curvature variance at multiple collections to obtain the density mean and the curvature variance mean; a second calculation module, which is used to multiply the density mean with the curvature variance mean to obtain the complexity of the surface morphology.

[0014] The present invention has the following beneficial effects: In the present invention, the impact caused by the flight attitude at different times is first estimated to obtain the collection impact degree. Subsequently, the coverage area of ​​the drone when performing the collection task is divided into regions, and the surface morphology complexity of each region is evaluated to obtain the surface morphology complexity. Based on these evaluation results, the degree of data interference in each region during the collection process at each collection moment is comprehensively calculated. Then, the present invention maps the collected point cloud data and image data, and effectively fuses these data according to the degree of interference during each collection. The three-dimensional space coordinate set is obtained through the above processing, which can significantly improve the reliability and accuracy of highway engineering BIM collection data by reducing the weight of the point cloud data when the wind is affected. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A flow chart of a BIM data collection method applied to highway engineering provided by an embodiment 1 of the present invention; Figure 2 A schematic flow chart of step S2 provided in Example 1 of the present invention; Figure 3 A schematic flow chart of step S4 provided in Example 1 of the present invention; Figure 4 This is a schematic diagram of the structure of the BIM data acquisition system applied to highway engineering described in Example 2 of the present invention; Figure 5 This is a schematic diagram of the structure of the impact factor calculation module described in Example 2 of the present invention; Figure 6 This is a schematic diagram of the structure of the morphological factor calculation module described in Example 2 of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a BIM data acquisition method and system for highway engineering proposed by the present invention, its specific implementation method, structure, features and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] Embodiment 1: A specific solution of a BIM data collection method applied to highway engineering provided by the present invention is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a method flow diagram of a BIM data collection method for highway engineering provided by an embodiment of the present invention. In this embodiment, the method includes steps S1 to S7.

[0021] S1. Acquire the collected data and flight data of the drone within a preset time period, wherein the preset time period is a time period during which the drone flies at a constant speed along the target highway section at a preset speed, and the collected data includes point cloud data and images; In this step, the drone will fly along the center line of the target highway section and take vertical downward images. The drone is equipped with a Velodyne laser radar device, which has a scanning accuracy of more than 200 points per square meter and supports multi-echo technology, which can penetrate the vegetation layer and obtain accurate surface data. In addition, the drone is also equipped with a high-definition camera with a resolution of 4K, which collects high-resolution image data to supplement the lack of details in the laser radar scanning point cloud data. At the same time, in this embodiment, the camera has an optical image stabilization function to ensure that a clear road image can be captured during flight. In addition, the drone is also equipped with a positioning system to ensure the spatial consistency of the laser radar point cloud data and the image data, and record the real-time position and flight trajectory of the drone. And the communication module can transmit the collected external data to the ground end in real time for construction and modeling analysis. Further, with regard to the laser radar equipment and camera mentioned in this embodiment, those skilled in the art can also replace them with laser radar equipment and cameras of other specifications according to actual needs, and no specific restrictions are made in this embodiment.

[0022] Secondly, in this embodiment, the target highway section referred to includes those areas where vehicle-mounted laser radar cannot be used for three-dimensional scanning due to terrain complexity or construction restrictions. These areas may involve special terrains such as mountains, tunnels, bridges, or areas where normal scanning cannot be performed due to construction activities. In addition, this embodiment can also be used to collect BIM data for specific highway sections according to actual needs. As for the preset speed described in this step, its value can be 1 meter per second or 0.5 meters per second, or adjusted according to actual needs. This embodiment does not make specific restrictions on the preset speed. The determining factors of the preset time include the preset speed and the length of the target highway section. It can be understood that if the preset speed is low, the camera shooting time and the number of scans of the laser radar device in a certain area of ​​the target highway section will increase accordingly, thereby improving the accuracy of the BIM data.

[0023] At the same time, in this embodiment, the image mentioned can be the image captured by the laser radar device during the scanning process; it can also be the video data captured during the flight of the drone, and then, according to the scanning node of the laser radar device, the corresponding frame is extracted from the video data as the corresponding image. In addition, the scanning speed of the laser radar device and the frame rate of the captured video can be set to be consistent to ensure that the point cloud data obtained from each scan can correspond to the video frame one by one. In other words, in this embodiment, in order to maximize the use of all the collected point cloud data, the moments mentioned in the subsequent steps are the moments of each point cloud scan, and each moment corresponds to an image, or it can be called the moment of each point cloud data collection. The specific implementation method can be selected by technicians in this field according to actual conditions, and no specific restrictions are made in this embodiment.

[0024] In this embodiment, it is considered that the UAV may be affected by external environmental factors such as wind during flight, which may cause the flight attitude to deviate, thereby affecting the accuracy of the collected highway point cloud data. In order to solve the above problems, in this embodiment, the road surface point cloud data and flight data collected by the UAV during flight are deeply analyzed. By evaluating the interference of external environmental changes on data collection and the complexity of the collection area, the error of the collection point data can be evaluated, and the actual collection data can be adjusted and corrected accordingly to ensure the acquisition of accurate highway BIM data. Therefore, the specific implementation steps are detailed in steps S2 to S4.

[0025] S2. Calculating the collection impact degree at each moment according to the flight data and the image within the preset time period; Specifically, in this embodiment, the characteristics of images are taken into account, that is, images are a kind of planar data, which lacks a spatial dimension compared to point cloud data. This characteristic makes it easier for image data to produce large errors during post-processing, especially when performing linear interpolation. Therefore, in order to improve the accuracy of data processing, this embodiment proposes a new method, which involves combining the posture changes of the drone at different flight moments and the changes in the images taken at the corresponding moments to calculate the degree of influence of the images collected at each moment. To explain this process more clearly, please refer to Figure 2 The flowchart shown in Figure 2 In the figure, further refinement of step S2 is shown in detail, including step S21, step S22 and step S23, which together constitute the calculation process of the degree of influence on image acquisition.

[0026] S21, calculating the image deviation corresponding to each moment according to the difference between the adjacent images in time sequence; In this step, the difference between the mentioned images can be evaluated and compared by a variety of methods. These methods include but are not limited to comparing pixel differences, comparing structural similarities, and calculating mean square errors. First, the evaluation of pixel differences can be achieved by directly comparing the values ​​of corresponding pixels in two adjacent images. The specific operation is to calculate the difference of each pixel point, and then find the average value or standard deviation of these differences to quantify the degree of difference between the images. Secondly, the evaluation of structural similarity involves comparing the brightness, contrast and structural information of two adjacent images, and judging the similarity of the images by the similarity of these visual features. Finally, the calculation of the mean square error is to square the pixel difference between the two images, and then find the average of these square differences. The smaller the mean square error, the higher the similarity between the two images. In addition to the above-mentioned methods, those skilled in the art can also use other various algorithms for calculating differences, which are not specifically limited in this embodiment.

[0027] In addition, in this embodiment, a method for matching adjacent images and calculating the difference between them by using an optical flow algorithm is also provided. Specifically, steps S211-S214 are included. These sub-steps describe how to use the optical flow algorithm to track motion information in an image sequence, thereby achieving accurate calculation of image differences.

[0028] S211, processing all the images according to an optical flow algorithm to obtain an optical flow vector for each pixel in each of the images; In this embodiment, dense optical flow is used to calculate the motion vector of each pixel in the image. Optical flow algorithm is a method for analyzing the motion of objects in an image sequence. This method can detect and describe the movement of objects between consecutive frames by estimating the motion field of pixels in the image. It can be specifically implemented by the Lucas-Kanade algorithm, the Horn-Schunck algorithm, or the Farneback algorithm, which are all existing technologies, and the specific implementation process will not be repeated in this embodiment.

[0029] S212, counting the number of optical flow vectors in each of the images to obtain the number of optical flows corresponding to each of the images; S213, respectively calculating a first optical flow mean vector and a second optical flow mean vector corresponding to each of the images according to a time sequence, wherein the first optical flow mean vector is a mean value of the optical flow vectors in a first preset time period, and the second optical flow mean vector is a mean value of the optical flow vectors in a second preset time period, and the end time of the first preset time period and the end time of the second preset time period are both the acquisition time of the image, and the time length of the first preset time period is greater than the time length of the second preset time period; It should be noted that in this embodiment, the long time span that the drone may experience during the flight is fully considered, for example, it can be up to 30 minutes. However, the duration of the fluctuation phenomenon that occurs during the flight of the drone is often significantly affected by instantaneous factors such as wind. Therefore, in this embodiment, a method is adopted, that is, taking the specific moment to be calculated or the image acquisition moment corresponding to the moment as the end point, and intercepting the image within a period of time along the time series forward, as the basis for analyzing the fluctuation phenomenon. Specifically, for example, a shorter time period is set, referred to as the first preset time period, and its time length can be 1 minute; at the same time, a shorter time period is set, referred to as the second preset time period, and its time length can be 10 seconds. It is worth noting that for the specific duration of the first preset time period and the second preset time period, technicians in this field can flexibly choose according to the actual flight situation and analysis requirements. In this embodiment, we do not impose strict restrictions on these durations to ensure the applicability and flexibility of the method.

[0030] S214. Calculate the image deviation corresponding to each of the images according to the first optical flow mean vector, the second optical flow mean vector, the number of optical flows, and the optical flow vector of each pixel point corresponding to each of the images.

[0031] Specifically, in this embodiment, the calculation function of the image deviation is as follows: ; in, Indicates The image deviation corresponding to the image taken at the moment; represents the maximum-minimum normalized function; Represents a second optical flow mean vector within a second preset time period; Represents a first optical flow mean vector within a first preset time period; Indicates The number of optical flows corresponding to the images taken at each moment; Indicates The image captured at that moment The optical flow vector of each pixel; Represents a function that computes the modulus of a vector.

[0032] In the above calculation function, Indicates The image captured at the moment is analyzed for its corresponding optical flow vector. If the acquisition angle at the current moment deviates, the size and direction of the optical flow vector collected at the same moment will be different. Indicates one by one The larger the value is, the higher the degree of deviation is at the current moment. The above calculation function can better describe the difference between adjacent images.

[0033] S22, calculating the attitude deviation of the drone corresponding to each moment according to the flight data; In this embodiment, the acquisition of flight data is carried out in real time by the inertial measurement unit IMU carried by the drone itself. These flight data mainly include two aspects: acceleration and angular velocity. Among them, in this embodiment, it is considered that when the drone encounters a large attitude change during flight, the flight data collection at this time may have a large deviation. In view of this, it is particularly important to calculate the attitude deviation of the drone for the analysis and processing of drone flight data. In order to achieve this goal, two key steps are also specifically included in this embodiment, namely step S221 and step S222.

[0034] S221, respectively calculating the mean of the acceleration and the angular velocity at each moment in a preset time period to obtain the mean of the acceleration and the mean of the angular velocity; S222. Calculate the attitude deviation of the drone corresponding to each moment according to the difference between the acceleration and the acceleration mean at each moment and the difference between the angular velocity and the angular velocity mean.

[0035] Specifically, the absolute value of the difference between the average acceleration of the UAV during flight and the acceleration of the UAV at each moment is taken as the acceleration difference degree at each moment; the absolute value of the difference between the average angular velocity of the UAV during flight and the angular velocity of the UAV at each moment is taken as the angular velocity difference degree at each moment; at each moment, normalization is performed according to the product of the corresponding acceleration difference degree and the corresponding angular velocity difference degree to obtain the corresponding UAV attitude deviation at each moment; the normalization method adopts the maximum-minimum normalization function; the attitude performance of the UAV during flight can be better reflected by measuring the angular velocity dimension and the acceleration dimension respectively.

[0036] S23, multiplying the image deviation degree and the drone attitude deviation degree corresponding to each moment to calculate the acquisition influence degree.

[0037] S3, performing region segmentation according to the point cloud data corresponding to each moment, and performing grouping based on the segmentation results to obtain point cloud data collected multiple times in each region; As described above, in this embodiment, the drone flies at a constant speed along the target highway section. Therefore, each point in the target highway section will be collected multiple times within a continuous time period. If the drone's posture changes during this time period, the collected area will have data deviations that change with time. Therefore, in this embodiment, by performing regional segmentation processing on the point cloud data corresponding to each moment in accordance with the preset area, multiple regions can be segmented at a single moment and each region can be further assigned a unique code. In order to classify the point cloud data corresponding to the same area together, point cloud data collected multiple times in a single area can be obtained. At the same time, it should be noted that in this embodiment, the preset area can be a square cell of 1*1 square meters, and a strip cell of 1*the width of the target highway section. The specific area and shape can be selected by those skilled in the art according to actual conditions. No specific restrictions are made in this embodiment. At the same time, for those skilled in the art, in order to further accurately perform regional segmentation, in this embodiment, feature matching algorithm SIFT (scale-invariant feature transform) can be used to extract feature points in point cloud data and images, and matching can be performed to achieve alignment between the image and point cloud data, so as to accurately divide the preset area.

[0038] In this embodiment, the drone performs a uniform flight mission along the target highway section. Therefore, each specific position of the target highway section will be collected multiple times in a continuous time period. If the posture of the drone changes during the collection, the collected data will have a deviation that changes with time. In order to solve this problem, this embodiment adopts a method, that is, to perform regional segmentation processing on the point cloud data at each time point, according to a predetermined area. In this way, the point cloud data can be divided into multiple regions at a single time point, and a unique code is assigned to each region. The purpose of this is to classify the point cloud data of the same area, so as to obtain a point cloud data set collected multiple times in a single area. In addition, it should be clear that in this embodiment, the predetermined area can be a square cell of 1*1 square meter, or a strip cell of 1*the width of the target highway section. As for the specific area size and shape, those skilled in the art can choose according to actual conditions. This embodiment does not make specific restrictions on this. At the same time, for those skilled in the art, in order to further improve the accuracy of regional segmentation, in this embodiment, a feature matching algorithm SIFT (scale invariant feature transform) can be used to extract feature points in point cloud data and images, and to achieve alignment between images and point cloud data by matching these feature points. This allows for a more accurate division of the predetermined area, thereby improving the accuracy of data collection.

[0039] S4. Calculate the surface morphology complexity of each area according to the point cloud data collected multiple times, wherein the surface morphology complexity is calculated by the point cloud density distribution and the point cloud curvature difference during each collection; In this embodiment, the influence of the change of the flight attitude of the drone on the collected point cloud data when detecting any specific area is further considered. In particular, when the surface morphology of the detection area is more complex, the influence of attitude change on the data is also increased accordingly. In order to accurately evaluate this influence, this embodiment analyzes the density distribution and curvature difference of the point cloud data of a single area at multiple collection times. For specific operation steps, please refer to Figure 3 , the figure shows step S4 in detail, including steps S41 to S43, which together constitute an exemplary process for calculating the complexity of the surface morphology of a specific area.

[0040] S41, calculating the point cloud density and the point cloud curvature variance at the time of single collection according to the point cloud data at the time of single collection; Among them, the point cloud density calculation method mentioned in this step can be calculated based on the number of point clouds in the area, but in this embodiment, it is considered that the change in the number of point clouds per unit area caused by the attitude of the drone at adjacent moments may not be obvious. Therefore, in this embodiment, the point cloud density calculation method is: the point cloud data at a single acquisition is divided based on density to obtain the number of point cloud clusters, and the number of point cloud clusters is used as the point cloud density. Among them, the DBSCAN algorithm in the clustering algorithm can be used for density-based division. The point cloud clusters obtained by this method may better describe the complexity of the area. The larger the value, the more chaotic the point cloud distribution in the current area, and the higher the complexity of its surface morphology. At the same time, the point cloud clusters are also more sensitive to the shooting angle, which can better reflect a change in the attitude of the drone. As for the point cloud curvature variance, each point at a single acquisition can be fitted locally to obtain the curvature of each point; then the variance is calculated based on the curvature of all points to obtain the curvature variance. Among them, as for local surface fitting and curvature calculation, it is a prior art, and its specific process is not repeated in this embodiment.

[0041] S42, performing mean calculation according to the point cloud density and the point cloud curvature variance during multiple acquisitions to obtain a density mean and a curvature variance mean; S43, multiplying the density mean and the curvature variance mean to obtain the complexity of the surface morphology.

[0042] Therefore, when the point cloud curvature of a specific area shows significant changes, and at the same time, the distribution of point cloud clusters in the area also shows large differences, this usually indicates that the area has rich texture details. On the contrary, if data deviations occur in the area during the data collection process, then due to the richness of the texture details, these deviations may be amplified during the subsequent linear interpolation processing. In view of this, when the present embodiment uses the complexity of the surface morphology to correct the data in the subsequent steps, specifically, for those data collected under stable conditions, this embodiment will give a greater weight, and for those data collected under unstable conditions, it will be given a smaller weight, so as to optimize the accuracy and reliability of the data.

[0043] S5. Calculate the interference degree of the collected data in each area during a single collection according to the complexity of the surface morphology of each area and the collection influence degree of the area during a single collection; Specifically, in this embodiment, it is considered that the change of the attitude of the drone during flight may cause the change of the shooting angle and the shooting height, and the change of the shooting angle and the shooting height will directly affect the image distortion. Therefore, in this embodiment, it is also considered to obtain the interference degree of the collected data according to the correction of the shooting angle and the shooting height.

[0044] That is, in this embodiment, the flight data also includes the shooting height and the shooting angle of the camera, which can be obtained by the built-in sensors of the drone respectively.

[0045] Specifically, the sampling distance between the drone and the target highway section in each area at each moment is calculated; the product of the sampling distance corresponding to each area at each moment, the collection influence degree of the drone at each moment and the surface morphology complexity of each area is used as the reference influence degree corresponding to each area at each moment; the shooting angle of each area at each moment is obtained; the ratio between the reference influence degree and the shooting angle is normalized to obtain the interference degree of the collected data of each area at each moment; the normalization method adopts the maximum-minimum normalization function; and when the shooting angle is 0, it is replaced by 0.001 to avoid the situation where the denominator is 0; It should be noted that the sampling distance is calculated by the sensor height and width, image height and width, focal length and flight altitude on the drone, which is the prior art and will not be repeated in this embodiment; in the above-mentioned process of obtaining the interference degree of collected data, when the drone collects data of an area, the greater the degree of interference to the drone itself at the corresponding moment, the greater the degree of collection impact. At the same time, the higher the complexity of the surface morphology under the corresponding collection area, the longer the sampling distance at the corresponding moment, and the smaller the shooting angle, the greater the degree of impact on the collected data points in the area at the current moment.

[0046] Meanwhile, for those skilled in the art, instead of using the two parameters of shooting height and shooting angle, the interference degree of collected data can be obtained by directly multiplying and normalizing the influence degree of collection by the complexity degree of surface morphology.

[0047] S6, performing perspective projection transformation processing on the point cloud data and image of each area at a single acquisition, and performing linear interpolation based on the processing result to obtain a high-dimensional image corresponding to each acquisition, wherein each pixel point in the high-dimensional image corresponds to a mapped three-dimensional space coordinate; Regarding perspective projection transformation processing, its core is to convert the three-dimensional coordinates of the point cloud data from the coordinate system of the laser radar device to the corresponding pixel points in the camera coordinate system through the external parameter matrix and the internal parameter matrix. The specific implementation process is the existing technology and will not be repeated in this embodiment.

[0048] S7. Perform weighted fusion based on the interference degree of the collected data of each area during a single collection and the high-dimensional image to obtain a three-dimensional space coordinate set corresponding to each area, and the three-dimensional space coordinate set is used to generate a BIM model of the highway project.

[0049] Through the processing of step S6, each area will have multiple multi-dimensional images, that is, the same pixel point may have different mapped three-dimensional space coordinates during the acquisition process, and this step is to fuse the different mapped three-dimensional space coordinates to obtain a three-dimensional space coordinate. Specifically, the fusion calculation function of the three-dimensional space coordinate is as follows: ; in, Indicates Region The three-dimensional space coordinates of the pixel points after adjustment; Indicates The number of times an area is sampled; Indicates The moment The interference degree of collected data corresponding to each area; Indicates The mean value of interference degree of collected data corresponding to each area; Indicates the total number of regions; Indicates Moment The region corresponds to the first The mapped three-dimensional space coordinates of the pixel area.

[0050] In the above calculation function, By The interference level of all collected data in the area and The number of times the area was collected Calculate the mean.

[0051] In the above calculation function, It means that by fusion calculation of the weights corresponding to each acquisition, the higher the external interference of a certain acquisition, the lower the data credibility of the high-dimensional image corresponding to this acquisition. Therefore, based on this, the three-dimensional space coordinates in each high-dimensional image are fused and calculated, and finally the three-dimensional space coordinates of the current pixel are obtained.

[0052] In summary, in this embodiment, it is considered that in the process of drone collecting highway BIM data, due to the susceptibility to interference from the external environment, the collected data may deviate, thus affecting the accuracy of the BIM data. In view of this phenomenon, the single collection range of the drone is divided into regions, and the degree of interference from the external environment at different collection times and the complexity of the surface morphology in the collection area are evaluated to determine the degree of interference in a single area in a single collection of data. And further, by mapping the collected point cloud data and images, and adjusting the weight of the data according to the degree of interference at each collection, combined with multiple collection data, the adjusted single area data is obtained, which further ensures the reliability and accuracy of the highway engineering BIM collection data.

[0053] Embodiment 2: like Figure 4 As shown, this embodiment provides a BIM data acquisition system applied to highway engineering, and the system includes: The data acquisition module is used to acquire the collected data and flight data of the drone within a preset time period. The preset time period is the time period in which the drone flies at a constant speed along the target highway section at a preset speed. The collected data includes point cloud data and images.

[0054] The impact factor calculation module is used to calculate the collection impact degree at each moment based on the flight data and the image within the preset time period.

[0055] The grouping module is used to perform region segmentation according to the point cloud data corresponding to each moment, and to perform grouping division based on the segmentation result to obtain the point cloud data of each area when it is collected multiple times.

[0056] The morphological factor calculation module is used to calculate the surface morphological complexity of each area based on the point cloud data collected multiple times in each area, and the surface morphological complexity is calculated by the point cloud density distribution and the point cloud curvature difference during each collection.

[0057] The interference factor calculation module is used to calculate the interference degree of the collected data of each area during a single collection according to the complexity of the surface morphology of each area and the collection influence degree of the area during a single collection.

[0058] The transformation processing module is used to perform perspective projection transformation processing on the point cloud data and images of each area during a single acquisition, and to perform linear interpolation based on the processing results to obtain a high-dimensional image corresponding to each acquisition, wherein each pixel point in the high-dimensional image corresponds to a mapped three-dimensional space coordinate.

[0059] A fusion calculation module is used to perform weighted fusion based on the interference degree of the collected data of each area during a single collection and the high-dimensional image, so as to obtain a three-dimensional space coordinate set corresponding to each area, and the three-dimensional space coordinate set is used to generate a BIM model of the highway project.

[0060] Furthermore, if Figure 5 The impact factor calculation module shown includes: The image deviation calculation module is used to calculate the image deviation corresponding to each moment according to the difference between the adjacent images in time sequence.

[0061] The attitude deviation calculation module is used to calculate the attitude deviation of the UAV corresponding to each moment according to the flight data.

[0062] The first calculation module is used to multiply the image deviation degree and the drone posture deviation degree corresponding to each moment to calculate the acquisition influence degree.

[0063] Furthermore, if Figure 6 The morphological factor calculation module comprises: The point cloud computing module is used to calculate the point cloud density and the point cloud curvature variance at a single acquisition based on the point cloud data at a single acquisition.

[0064] The mean calculation module is used to calculate the mean value of the density and the mean value of the curvature variance according to the point cloud density and the point cloud curvature variance during multiple acquisitions.

[0065] The second calculation module is used to multiply the density mean and the curvature variance mean to obtain the complexity of the surface morphology.

[0066] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0067] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0068] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A BIM data collection method applied to highway engineering, characterized in that: The method comprises: Acquire the collected data and flight data of the drone within a preset time period, wherein the preset time period is a time period during which the drone flies at a constant speed along the target highway section at a preset speed, and the collected data includes point cloud data and images; Calculating the collection impact degree at each moment according to the flight data and the image within the preset time period; Performing regional segmentation according to the point cloud data corresponding to each moment, and grouping and dividing based on the segmentation results to obtain point cloud data when each area is collected multiple times; The surface morphology complexity of each area is calculated based on the point cloud data collected multiple times, and the surface morphology complexity is calculated based on the point cloud density distribution and the point cloud curvature difference during each collection; The interference degree of the collected data in each area during a single collection is calculated according to the complexity of the surface morphology of each area and the collection influence degree of the area during a single collection; Performing perspective projection transformation on the point cloud data and image of each area during a single acquisition, and performing linear interpolation based on the processing result to obtain a high-dimensional image corresponding to each acquisition, wherein each pixel point in the high-dimensional image corresponds to a mapped three-dimensional space coordinate; Based on the interference degree of the collected data of each area during a single collection and the high-dimensional image, a weighted fusion is performed to obtain a three-dimensional space coordinate set corresponding to each area, and the three-dimensional space coordinate set is used to generate a BIM model of the highway project; The acquisition process of the acquisition influence degree includes: calculating the image deviation degree corresponding to each moment according to the difference between the adjacent images in time sequence; calculating the UAV attitude deviation degree corresponding to each moment according to the flight data; and multiplying the image deviation degree corresponding to each moment with the UAV attitude deviation degree to calculate the acquisition influence degree.

2. The BIM data acquisition method for highway engineering according to claim 1 is characterized in that: The image deviation corresponding to each moment is calculated based on the difference between the adjacent images in time sequence, including: Processing all the images according to the optical flow algorithm to obtain an optical flow vector for each pixel in each of the images; Counting the number of optical flow vectors in each of the images to obtain the number of optical flows corresponding to each of the images; Calculating respectively according to the time sequence to obtain a first optical flow mean vector and a second optical flow mean vector corresponding to each of the images, wherein the first optical flow mean vector is the mean of the optical flow vectors in a first preset time period, and the second optical flow mean vector is the mean of the optical flow vectors in a second preset time period, the end time of the first preset time period and the end time of the second preset time period are both the acquisition time of the image, and the time length of the first preset time period is greater than the time length of the second preset time period; The image deviation degree corresponding to each of the images is calculated based on the first optical flow mean vector, the second optical flow mean vector, the number of optical flows, and the optical flow vector of each pixel point corresponding to each of the images.

3. The BIM data acquisition method for highway engineering according to claim 1 is characterized in that: The flight data includes acceleration and angular velocity. The attitude deviation of the drone corresponding to each moment is calculated based on the flight data, including: Calculate the average value of the acceleration and angular velocity at each moment in the preset time period to obtain the average value of the acceleration and the average value of the angular velocity; The attitude deviation of the drone corresponding to each moment is calculated according to the difference between the acceleration and the acceleration mean at each moment and the difference between the angular velocity and the angular velocity mean.

4. The BIM data acquisition method for highway engineering according to claim 1 is characterized in that: The surface morphology complexity of each area is obtained by calculating the point cloud data collected multiple times in each area, including: The point cloud density and the point cloud curvature variance at the time of single acquisition are calculated based on the point cloud data at the time of single acquisition respectively; The mean value of density and the mean value of curvature variance are obtained by performing mean calculation based on the point cloud density and point cloud curvature variance during multiple acquisitions; The complexity of the surface morphology is obtained by multiplying the density mean and the curvature variance mean.

5. The BIM data acquisition method for highway engineering according to claim 4 is characterized in that: The point cloud density and point cloud curvature variance at a single acquisition are calculated based on the point cloud data at a single acquisition, including: Performing density-based division processing on the point cloud data collected in a single time to obtain the number of point cloud clusters, and using the number of point cloud clusters as the point cloud density; A local surface fitting will be performed on each point during a single acquisition to obtain the curvature of each point; The curvature variance is obtained by calculating the variance of the curvature of all points.

6. The BIM data acquisition method for highway engineering according to claim 5 is characterized in that: The number of point cloud clusters is obtained by processing the DBSCAN algorithm.

7. A BIM data acquisition system applied to highway engineering, characterized in that: include: A data acquisition module, used to acquire the collected data and flight data of the UAV within a preset time period, wherein the preset time period is a time period during which the UAV flies at a constant speed along the target highway section at a preset speed, and the collected data includes point cloud data and images; An influence factor calculation module, used to calculate the collection influence degree at each moment according to the flight data and the image within the preset time period; The acquisition process of the acquisition influence degree includes: calculating the image deviation degree corresponding to each moment according to the difference between the adjacent images in time sequence; calculating the UAV attitude deviation degree corresponding to each moment according to the flight data; multiplying the image deviation degree corresponding to each moment and the UAV attitude deviation degree to calculate the acquisition influence degree; A grouping module, used to perform region segmentation according to the point cloud data corresponding to each moment, and to perform grouping based on the segmentation result to obtain point cloud data when each region is collected multiple times; A morphological factor calculation module is used to calculate the surface morphological complexity of each area according to the point cloud data collected multiple times in each area, and the surface morphological complexity is calculated by the point cloud density distribution and the point cloud curvature difference during each collection; An interference factor calculation module, used for calculating the interference degree of the collected data of each area in a single collection according to the complexity of the surface morphology of each area and the collection influence degree of the area in a single collection; A transformation processing module, used to perform perspective projection transformation processing on the point cloud data and image of each area at a single acquisition, and to perform linear interpolation based on the processing result to obtain a high-dimensional image corresponding to each acquisition, wherein each pixel point in the high-dimensional image corresponds to a mapped three-dimensional space coordinate; A fusion calculation module is used for fusion calculation based on the interference degree of the collected data of each area during a single collection and the weighted fusion of the high-dimensional image to obtain a three-dimensional space coordinate set corresponding to each area, and the three-dimensional space coordinate set is used to generate a BIM model of the highway project.

8. The BIM data acquisition system for highway engineering according to claim 7 is characterized in that: The impact factor calculation module includes: An image deviation calculation module, used for calculating the image deviation corresponding to each moment according to the difference between the adjacent images in time sequence; An attitude deviation calculation module is used to calculate the attitude deviation of the UAV corresponding to each moment according to the flight data; The first calculation module is used to multiply the image deviation degree and the drone posture deviation degree corresponding to each moment to calculate the acquisition influence degree.

9. The BIM data acquisition system for highway engineering according to claim 7 is characterized in that: The morphological factor calculation module comprises: A point cloud computing module, used to calculate the point cloud density and the point cloud curvature variance at a single acquisition according to the point cloud data at a single acquisition; The mean calculation module is used to calculate the mean value of density and the mean value of curvature variance according to the point cloud density and the point cloud curvature variance during multiple acquisitions; The second calculation module is used to multiply the density mean and the curvature variance mean to obtain the complexity of the surface morphology.

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