Ground Laser Scanner Scanning Planning Method for Construction Sites Based on Oblique Photogrammetry

Abstract

The present invention provides a scanning planning method for a ground laser scanner at a construction site based on oblique photography, including the steps of: S1: using a drone to photograph the site and importing it into modeling software for three-dimensional reconstruction and point cloud data acquisition; S2: preliminarily processing the point cloud data; S3: classifying the point cloud data; S4: setting the maximum scanning distance and maximum scanning angle according to the scanner parameters and scanning accuracy requirements; S5: visibility judgment and scanning range statistics; S6: using a genetic algorithm to perform scanning planning for the construction site; S7: setting the scanning resolution. The scanning planning method for a ground laser scanner at a construction site based on oblique photography of the present invention uses a genetic algorithm to perform scanning planning for the construction site. By performing binary coding on the preselected measurement sites, setting relevant screening criteria, fitness criteria, iteration methods, and termination criteria, the optimal combination of scanning measurement sites is finally obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of scanning planning methods for terrestrial laser scanners, and particularly to a scanning planning method for a terrestrial laser scanner at a construction site based on oblique photography. Background Art

[0002] With the rapid development of computer vision technology, three-dimensional laser scanning technology has been increasingly widely applied in the field of civil engineering due to its advantages of high precision and the ability to quickly obtain a large amount of point cloud information in a short time. However, currently, manual pre-planning is still required before the terrestrial laser scanner scans. Traditional scanning planning for terrestrial laser scanners is usually based on existing BIM models or CAD models, or relies on professional on-site personnel for manual planning. However, in most cases, it is difficult to have a relatively complete model during the construction stage, and it is difficult to consider the complex environment of the construction site based on BIM or CAD models. Planning on-site relying on the scanning experience of professional personnel is also likely to take longer due to the influence of personal judgment, and it is impossible to complete the scanning with the fewest measurement stations while ensuring the scanning integrity.

[0003] With the popularization of consumer-grade drones with good mobility and simple operation, the advantages of oblique photogrammetry technology in three-dimensional reconstruction by quickly taking a large number of pictures have also developed rapidly in many fields. However, the current oblique photography modeling accuracy is still limited to the centimeter level and is difficult to reach the millimeter-level accuracy of terrestrial laser scanners. Just in this situation, this technology combines the advantages of rapid modeling of consumer-grade drone oblique photography and can achieve rapid planning of terrestrial laser scanners at a construction site without a construction model. Summary of the Invention

[0004] Aiming at the above deficiencies in the prior art, the present invention provides a scanning planning method for a terrestrial laser scanner at a construction site based on oblique photography, in order to complete high-integrity three-dimensional laser scanning that meets the LOD standard in the shortest time.

[0005] To achieve the above object, the present invention provides a scanning planning method for a terrestrial laser scanner at a construction site based on oblique photography, including the steps of:

[0006] S1: Using a drone to photograph the site and import it into modeling software for three-dimensional reconstruction and point cloud data acquisition;

[0007] S2: Performing preliminary processing on the point cloud data;

[0008] S3: Classifying the point cloud data, and the categories of the point cloud data include scanned target point cloud, occluded point cloud, and preselected scanned point cloud;

[0009] S4: Setting the maximum scanning distance and maximum scanning angle according to the scanner parameters and scanning accuracy requirements;

[0010] S5: Visibility judgment and scanning range statistics;

[0011] S6: Use genetic algorithm to conduct scanning planning for the construction site;

[0012] S7: Set the scanning resolution.

[0013] Preferably, in the step S2, first confirm the range of the scanning plan, delete the point cloud data that does not belong to the range of the scanning plan, and secondly, delete the movable obstacles photographed on the site, and the movable obstacles include the point cloud of the crawler crane on the site and the point cloud of the aerial work platform on the site.

[0014] Preferably, in the step S3, the scanning target point cloud is determined by manual selection; the preselected scanning point cloud is obtained by using methods such as statistics, point cloud filtering, and point cloud downsampling; the occluded point cloud is the remaining point cloud on the site except the scanning target point cloud and the preselected scanning point cloud.

[0015] Preferably, in the step S5, for the visibility judgment, connect the scanning target point cloud and the preselected scanning point cloud, first calculate the distance between the two points and the vertical scanning angle of the measuring station. If the distance requirement or the vertical angle requirement is not met, it is judged that the scanning target point cloud is not visible to the current preselected measuring station without further calculation; if the accuracy requirement is met, it is necessary to judge whether it meets the visibility standard. To enhance the redundancy of the visibility judgment, whether there is the occluded point cloud within 0.1 m range with the axis of the line connecting the two points is used as the judgment standard. If there is the occluded point cloud, it is determined that there is occlusion, and the scanning target point cloud is not visible to the current preselected measuring station; based on the above process, calculate the scanning range of each preselected measuring station for the scanning target point cloud.

[0016] Preferably, the step S6 further includes the steps:

[0017] S61: Set the fitness index, and the fitness index satisfies the formula:

[0018]

[0019] where F represents the fitness, m represents the number of the preselected measuring stations, and f i represents the fitness occupancy ratio;

[0020] S62: Conduct binary coding for the preselected measuring stations;

[0021] Place the preselected measurement stations on the chromosome in sequence. For the chromosome encoding at the corresponding positions of the preselected measurement stations adopted, it should be set to 1, and for those not adopted, it should be set to 0. And so on to complete the chromosome encoding of the preselected measurement stations.

[0022] S63: Set the screening conditions, iteration rules, and termination rules of the genetic algorithm.

[0023] Preferably, in step S7, according to the expected scanning LOD standard, calculate the average distance from each measurement station to the visible target scanning points, and set the corresponding resolution according to the correspondence between the resolution and the distance.

[0024] Due to the adoption of the above technical solutions, the present invention has the following beneficial effects:

[0025] 1. The present invention does not require providing a BIM model or a CAD model, but is based on the actual modeling data at the construction site.

[0026] 2. In the present invention, the acquisition of construction site information is based on the on-site photo shooting of consumer-grade drones, which has the characteristics of fast speed and low cost compared with airborne lidar.

[0027] 3. The present invention is obtained relying on the actual on-site scanning platform, taking into account the influence of the sundries stacking at the construction site and the occlusion of the on-site buildings.

[0028] 4. The present invention takes into account the scanning accuracy and the degree of scanning details of the laser scanner.

[0029] 5. The present invention uses a heuristic algorithm for iterative calculation, and the calculated result is the optimal result compared with the traditional weighted greedy algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a flowchart of the method for scanning and planning the ground laser scanner at the construction site based on oblique photography according to the embodiment of the present invention;

[0031] Figure 2 is a flowchart of using the genetic algorithm to perform scanning and planning on the construction site according to the embodiment of the present invention;

[0032] Figure 3 is a schematic diagram of the genetic algorithm encoding method according to the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] Next, according to the drawings Figures 1 to 3 , a preferred embodiment of the present invention is given and described in detail to better understand the functions and features of the present invention.

[0034] Please refer to Figures 1 to 3, A method for scanning and planning a ground laser scanner at a construction site based on oblique photography according to an embodiment of the present invention includes the steps:

[0035] S1: Use a drone to photograph the site and import it into modeling software for three-dimensional reconstruction and point cloud data acquisition;

[0036] Consumer drones with RTK functions on the market can be used for flight path planning and oblique photography. The captured photos are imported into relevant processing software to automatically perform three-dimensional modeling and point cloud output.

[0037] S2: Perform preliminary processing on the point cloud data;

[0038] First, confirm the scanning and planning scope, and delete the point cloud data that does not belong to the scanning and planning scope. Secondly, delete the movable obstacles captured on site, including the point cloud of the crawler crane on site and the point cloud of the aerial work platform on site, etc.

[0039] S3: Classify the point cloud data. The categories of the point cloud data include scanned target point cloud, occluded point cloud, and preselected scanning point cloud;

[0040] The scanned target point cloud is determined by manual selection; the preselected scanning point cloud is obtained by methods such as statistics, point cloud filtering, and point cloud downsampling; the occluded point cloud is the remaining point cloud on site except for the scanned target point cloud and the preselected scanning point cloud.

[0041] The scanned target area needs to be manually selected and screened out. Secondly, the preselected scanning point cloud needs to be screened by statistics. Since the preselected measurement stations need to be flat for better instrument erection, statistics are performed through the Z-direction coordinates of the point cloud. Considering that there may be obstacles such as sundries piled on the ground and it is impossible to set up stations, the point cloud quantity is statistically analyzed with an elevation interval of 0.2 m to separate the ground and obstacles. According to the statistical results, there will be peaks in several obvious elevation data ranges, and the peak data is the point cloud where the station can be placed. However, some ground points or platform points only have very small planes and are not suitable as measurement stations. At this time, the extracted preselected measurement stations need to be screened and processed by point cloud radius filtering. By setting a reasonable filtering radius, the independent small-range plane points can be excluded. At the same time, the remaining plane points are too dense, and in a large range, the perspective change of the measurement stations with an interval of 0.5 m is not so obvious. Therefore, the remaining plane point data still needs to be downsampled. The uniform downsampling method is adopted to create a three-dimensional voxel grid with a suitable size for the plane point cloud data, and the point closest to the voxel center in each voxel is taken as the final center point of the downsampling. Finally, the plane points are lifted 1.5 m along the positive Z-axis as the coordinates of the final preselected measurement stations; the occluded point cloud includes the remaining points in the entire concerned range.

[0042] S4: Set the maximum scanning distance and maximum scanning angle according to the scanner parameters and scanning accuracy requirements;

[0043] Taking the leica P40 scanner as an example, the distance accuracy is 1.2mm + 10ppm. The distance can be set to 120m and the vertical angle can be set to 75° to ensure better accuracy.

[0044] S5: Visibility judgment and scanning range statistics;

[0045] Visibility judgment is to connect the scanned target point cloud and the preselected scanning point cloud. First, calculate the distance between the two points and the vertical scanning angle of the station. If the distance requirement or vertical angle requirement is not met, it is judged that the scanned target point cloud is invisible to the current preselected station without further calculation; if the accuracy requirement is met, it is necessary to judge whether it meets the visibility standard. To enhance the redundancy of visibility judgment, whether there is an occluding point cloud within 0.1m of the axis connecting the two points is used as the judgment standard. If there is an occluding point cloud, it is determined that there is occlusion, and the scanned target point cloud is invisible to the current preselected station; based on the above process, calculate the scanning range of each preselected station for the scanned target point cloud, briefly recorded as l i .

[0046] S6: Use the genetic algorithm to perform scanning planning on the construction site;

[0047] Specifically, it is necessary to perform binary encoding on the preselected stations, and obtain the best combination of scanning stations by setting relevant screening criteria, fitness criteria, iteration methods, and termination criteria.

[0048] Step S6 further includes the steps:

[0049] S61: Set the fitness index;

[0050] Set the fitness index to the total number of stations, but the proportion of each station in the fitness index is not 1, but determined by its scanning angle. From step S5, the scanning range of each point can be obtained, and the horizontal angle scanning range of the scanning points can also be obtained. Take the scanning angle α / 180 as the fitness occupancy ratio f of this station i , that is When α > 180°, the fitness occupancy ratio of this station is 1. Assuming the number of selected stations is m, the fitness F formula is as follows:

[0051]

[0052] Among them, F represents fitness, m represents the number of preselected stations, and f i represents the fitness occupancy ratio;

[0053] S62: Binary-encode the preselected measurement sites;

[0054] When using a genetic algorithm for scan planning, chromosome encoding needs to be set. The chromosome encoding uses binary encoding, and the encoding method is as Figure 3 shown. Place the preselected measurement sites on the chromosome in sequence. For the chromosome encoding at the corresponding positions of the preselected measurement sites used, it should be set to 1, and for the preselected measurement sites not used, it should be set to 0. And so on to complete the chromosome encoding of the preselected measurement sites;

[0055] S63: Set the genetic algorithm screening conditions, iteration rules, and termination rules;

[0056] First, an initial population should be generated and screened. In the present invention, the integrity is used as the screening condition. The integrity is the ratio of the sum of the scanned target points of the measurement sites selected by the chromosome to all the target point clouds. Assuming the total number of target point clouds is n, the formula is as shown below:

[0057]

[0058] The integrity (such as 99%) can be set as the screening criterion for preliminary screening. Then, cross and mutate the remaining chromosomes. The crossover rate and mutation rate can be set by oneself. For example, the crossover rate is set to 0.6 and the mutation rate is set to 0.01. Combine the population after crossover and mutation with the original population to form a new population. Next, use the integrity as the screening condition. First, eliminate the chromosomes that do not meet the integrity standard. Secondly, sort the remaining chromosomes of the new population according to the fitness criterion. Set a maximum population size, and further eliminate the chromosomes with a ranking greater than this population size. Then, perform the second round of crossover and mutation, and iterate in this way. Finally, when the highest fitness chromosome no longer changes during iteration or the iteration has exceeded 1000 times as the termination condition, complete the scan planning of the genetic algorithm and output the scan points;

[0059] S7: Set the scan resolution.

[0060] According to the expected scan LOD standard, calculate the average distance from each measurement site to the visible target scan points, and set the corresponding resolution according to the correspondence between the resolution and the distance. Since the scan range is large, it is recommended to set the scan resolutions of all measurement stations to be the same.

[0061] The present invention has been described in detail above in conjunction with the embodiments in the drawings. Those of ordinary skill in the art can make various variations of the present invention according to the above description. Therefore, some details in the embodiments should not constitute a limitation to the present invention, and the protection scope of the present invention will be defined by the scope of the appended claims.

Claims

1. A construction site ground laser scanner scanning planning method based on oblique photography, comprising the steps of: S1: Use a drone to photograph the scene and import it into the modeling software for 3D reconstruction and point cloud data acquisition; S2: Preliminary processing of the point cloud data; S3: classifying the point cloud data, wherein the categories of the point cloud data include scanning target point cloud, occluded point cloud and pre-selected scanning point point cloud; S4: Set the maximum scanning distance and maximum scanning angle according to the scanner parameters and scanning accuracy requirements; S5: Visibility judgment and scanning range statistics; S6: Using genetic algorithm to scan and plan the construction site; S7: Set the scanning resolution.

2. The method for scanning and planning a ground laser scanner at a construction site based on oblique photography according to claim 1, wherein In the step S2, firstly, the scope of the scan plan is confirmed, and the point cloud data that does not belong to the scope of the scan plan is deleted. Secondly, the movable obstacles photographed on site are deleted. The movable obstacles include the on-site crawler crane point cloud and the on-site aerial platform point cloud.

3. The method for scanning and planning a ground laser scanner at a construction site based on oblique photography according to claim 2, wherein In the step S3, the scanning target point cloud is determined by manual selection; the pre-selected scanning point point cloud is acquired by using statistics, point cloud filtering and point cloud downsampling methods; the occluded point cloud is the remaining point cloud on site except the scanning target point cloud and the pre-selected scanning point point cloud.

4. The method for scanning and planning a ground laser scanner at a construction site based on oblique photography according to claim 3, wherein In the step S5, the visibility judgment is to connect the scanning target point cloud and the pre-selected scanning point cloud, first calculate the distance between the two points and the vertical scanning angle of the measuring station. If the distance requirement or the vertical angle requirement is not met, no subsequent calculation is required, and it is judged that the scanning target point cloud is not visible to the current pre-selected measuring station; if the accuracy requirement is met, it is necessary to judge whether it meets the visibility standard. In order to enhance the redundancy of the visibility judgment, whether there is an obstruction point cloud within 0.1m of the axis line connecting the two points is used as a judgment standard. If there is an obstruction point cloud, it is determined that there is obstruction, and the scanning target point cloud is not visible to the current pre-selected measuring station; based on the above process, the scanning range of each pre-selected measuring station for the scanning target point cloud is calculated.

5. The method for scanning and planning a ground laser scanner at a construction site based on oblique photography according to claim 4, wherein The step S6 further comprises the steps of: S61: Setting a fitness index, the fitness index satisfies the formula: Among them, F represents fitness, m represents the number of the preselected sites, and f i represents the fitness occupancy ratio; S62: Binary encoding the pre-selected measurement sites; The pre-selected test sites are placed on the chromosome in order, and the chromosome codes of the corresponding positions of the adopted pre-selected test sites should be set to 1, and the pre-selected test sites that are not adopted should be set to 0, and so on to complete the chromosome codes of the pre-selected test sites; S63: Setting genetic algorithm screening conditions, iteration rules and termination rules.

6. The method for scanning and planning of a ground laser scanner at a construction site based on oblique photography according to claim 5, characterized in that, In the step S7, the average distance from each measuring station to the visible target scanning point is calculated according to the expected scanning LOD standard, and the corresponding resolution is set according to the corresponding relationship between resolution and distance.

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