A method for positioning a terrestrial laser scanner network for pre-assembled structures

Abstract

The application provides a positioning network determination method for a ground laser scanner of a pre-assembled structure. A plane model of a target structure and a surrounding area is generated in a NetLogo platform, a restricted area and a blocked area are marked in the surrounding area, and a multi-agent model is obtained; peripheries of assembling interfaces and non-assembling interfaces of the target structure are discretized according to different densities, a visible area of each discrete point is obtained according to a TLS constraint parameter, and a global visible area of the periphery, i.e., a global TLS site area, is obtained by superimposing the visible areas of all the discrete points on the periphery; an initial site position set is calculated by using a genetic algorithm according to the global site area, and then a TLS site network is established, and the site network is optimized by using a registration evaluation method to obtain a TLS positioning network. In the establishment of the global TLS site area, the assembling interfaces are considered, the quality of point cloud data obtained by the TLS positioning network is improved, and the accuracy of structure assembling is ensured, which is fast and efficient.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of scanning planning of terrestrial laser scanner, and particularly relates to a method for determining a positioning network of a terrestrial laser scanner for a pre-assembled structure. BACKGROUND

[0002] In recent years, the application of fabricated structures is becoming more and more widespread in China. Physical pre-assembly is an effective measure to ensure that spatially related components of fabricated structures can be accurately installed on site, but it has the disadvantages of high jig and labor costs, large site occupation, and low efficiency. With the development and application of computer technology and three-dimensional measurement technology, physical pre-assembly is gradually replaced by virtual pre-assembly. Virtual pre-assembly is a method of simulating assembly in a special software by using the assembly control points of components obtained by three-dimensional measurement, so as to achieve assembly accuracy. Terrestrial laser scanner (TLS) can quickly obtain three-dimensional point cloud data of the structure, has the advantages of high data accuracy, less influence from the outside world, and strong operability, and has become the preferred three-dimensional measurement device for virtual pre-assembly.

[0003] At present, the decision of TLS station arrangement mainly depends on the experience of the operator, that is, the selection of TLS station position is subjective, and it is difficult to ensure the integrity of the final point cloud. Due to the lack of a systematic and reasonable decision-making method, it is still not possible to intelligently select the best layout position of the TLS station according to the site environment and the target structure. At the same time, the acquisition of point cloud of the pre-assembled structure needs to meet the requirements of virtual pre-assembly work for the quality of point cloud data. Therefore, a systematic method is urgently needed to solve this problem.

[0004] Existing engineering technicians have transformed the scanning planning problem into a problem of finding the minimum number of predefined viewpoints, which provide complete coverage of the scanning target while meeting the data quality requirements. However, when measuring the outside of the structure, many schemes do not consider that there are obstacles affecting the line of sight of the TLS when placing the TLS station, so that the final scheme cannot achieve the expected results. Traditional station measurement also pays less attention to special areas, for example, in the acquisition of point cloud data for virtual pre-assembly of pre-assembled structures, more accurate point cloud data is needed for the assembly interface area to complete the extraction of feature assembly points. Lack of key measurement of the assembly interface will result in point cloud quality not meeting the requirements of virtual pre-assembly. In addition, a certain degree of overlap is required between TLS stations for point cloud registration to ensure the registration effect. If only the minimum number of predefined stations is considered, the overlap between some adjacent stations is insufficient, resulting in automatic registration between stations, low structure assembly accuracy, and repeated calculation, which increases the workload of station calculation and is low in efficiency. However, too many TLS stations will reduce the scanning efficiency and greatly increase the measurement cost. SUMMARY

[0005] In view of the technical problems existing in the prior art, the application provides a positioning network determination method for a terrestrial laser scanner applicable to a pre-assembly structure, which has stronger application promotion, higher scanning efficiency in actual work, better data quality and controllable measurement cost.

[0006] To solve the above technical problems, the application adopts the following technical solutions.

[0007] A positioning network determination method for a terrestrial laser scanner applicable to a pre-assembly structure, comprising the following steps:

[0008] Step S1: According to a target structure design scheme, a structure information model is established in a BIM software.

[0009] Step S2: The structure information model is planarized and then imported into a NetLogo integrated platform to obtain a planar model of the target structure; according to a photograph of a surrounding area of the target structure obtained by shooting, a surrounding area model is generated outside the planar model, regions in which a laser scanner cannot be placed are marked as restricted regions in the surrounding area model, and regions that will shield the line of sight of the laser scanner for measuring the target structure are marked as shielding regions to obtain a multi-agent model; the multi-agent includes a target structure agent, a shielding region agent and a restricted region agent.

[0010] Step S3: The perimeter of the target structure agent is discretized into a point set, wherein the density of the discrete points on the perimeter corresponding to the assembly interfaces is greater than the density of the discrete points on the perimeter corresponding to the non-assembly interfaces; taking any discrete point on the perimeter as a starting point, a laser scanner station point that can observe the starting point is searched in the surrounding area model according to the constraint parameters of the laser scanner, and the obtained station point set is taken as the visible region of the starting point; the visible regions of all starting points on the perimeter are superimposed to obtain the global visible region of the perimeter; based on the visual mutual difference principle, the global visible region of the perimeter is taken as the global station point region of the laser scanner.

[0011] Step S4: According to the global station point region of the laser scanner obtained in S3 and the shielding region agent and the restricted region agent obtained in S2, a genetic algorithm is used to calculate an initial station point position set of the laser scanner.

[0012] Step S5: According to the initial station point position set of the laser scanner, a Delaunay triangulation method is used to establish a laser scanner station point network; a registration evaluation method is used to optimize the station point network to obtain a positioning network of the laser scanner.

[0013] It should be noted that in step S2, the photograph of the surrounding area of the target structure is shot by a UAV or a satellite, or is shot by a total station instrument positioning and a camera.

[0014] It should be noted that in step S2, the restricted area includes narrow space in the target structure peripheral area, unstable ground, and natural area where equipment cannot be erected; wherein the natural area where equipment cannot be erected includes rivers and canyons.

[0015] It should be noted that in step S3, the constraint parameters of the laser scanner are working distance, incident angle, and field of view angle.

[0016] It should be noted that in step S4, the fitness function used by the genetic algorithm is expressed as formula (1):

[0017] (1)

[0018] In the formula, F(I i ) is the fitness score of individual I i ; R(I i ) is the fitness score of individual I i when the penalty term is not triggered, which is based on its performance without violating any constraint conditions; P(I i ) is the penalty term, which is used to reduce the fitness score of the individual that violates the constraint condition.

[0019] The individual refers to any set of laser scanner stations in the population; the population refers to any set of laser scanner stations in the genetic algorithm calculation process.

[0020] In the formula, R(I i ) is expressed as formula (2):

[0021] (2)

[0022] In the formula: is the viewpoint entropy of individual I i ; is the positioning network size of individual I i ; is the connectivity degree between stations in individual I i .

[0023] In the formula, the penalty term P(I i ) can be expressed as formula (3):

[0024] (3)

[0025] In the formula, n is the total number of stations j in individual I i ; is the weight related to station j; is an indicator function, if station j in individual I i is placed in the blocked area or the restricted area, then =1, otherwise = 0.

[0026] It should be noted that in step S4, when calculating the initial station position set of the laser scanner using the genetic algorithm, the tournament selection method is selected for the selection operation, and the single-point crossover is selected for the crossover operation.

[0027] It should be noted that the step S5 further comprises:

[0028] S51, based on the initial station position set of the laser scanner, a station network is established using the Delaunay triangulation method, wherein the nodes represent the stations and the edges represent the adjacency relationship between the stations;

[0029] S52, a weight is assigned to each edge in the station network to obtain an undirected weighted graph; the weight of each edge is the overlap degree of the corresponding visible area between the adjacent stations;

[0030] S53, according to the undirected weighted graph, an initial registration path is generated using the maximum spanning tree algorithm;

[0031] S54, the overlap degree of each edge in the initial registration path is checked, if the overlap degree is lower than a threshold, a new station is added between the adjacent stations until the overlap degree of all adjacent stations meets the threshold requirement to obtain an optimal registration path;

[0032] S55, according to the optimal registration path and the corresponding station, a laser scanner positioning network is obtained.

[0033] The threshold is an overlap degree threshold determined according to an automatic registration algorithm, the automatic registration algorithm refers to a method for automatically registering the point cloud data between adjacent stations, and generally can be 2%~10%.

[0034] The basic principle of the present application is that the present application establishes a multi-agent model containing target structure, shielding area and limiting area in the NetLogo integrated platform, then discretizes the target structure assembly interface perimeter and non-assembly interface perimeter according to different point densities, obtains the corresponding visible area for each discrete point, superimposes to obtain the global visible area of the target structure agent perimeter (i.e. the global candidate area of the station), and then based on the visual exchange principle, takes the global visible area of the perimeter as the global station area of the laser scanner, and then calculates the station position set according to the global station area through the genetic algorithm, and determines the optimal TLS positioning network based on the registration evaluation method; the present application considers the focus on the assembly interface area to meet the requirements of point cloud data quality in virtual pre-assembly, and generates a reasonable TLS positioning network, so that the generated TLS positioning network can obtain a large amount of high-quality point cloud data in the shortest working time during virtual pre-assembly, reduces the time required for measurement work, and further reduces the measurement cost; at the same time, the quality of the obtained point cloud data is improved, the point cloud data redundancy is reduced, the integrity of the point cloud data collection is ensured, and faster and more efficient post-processing is realized, the accuracy of structure assembly is ensured, and the advantages of fast and efficient are achieved, which greatly reduces the workload of station calculation.

[0035] Compared with the prior art, the present application has the following advantages:

[0036] 1) By dividing and identifying the line-of-sight shielding area and the area where the TLS cannot be placed, the obtained TLS positioning network is more suitable for engineering practice;

[0037] 2) By using the NetLogo integrated platform to establish a multi-agent model, the calculation efficiency is improved, and the calculation process is more intuitive and accurate;

[0038] 3) By focusing on the target structure assembly interface, the quality of the point cloud data obtained by the TLS positioning network is improved, which meets the requirements of point cloud data quality during virtual pre-assembly;

[0039] 4) By using the registration evaluation method to optimize the station network, the optimized positioning network is free from the problem that the existing method only pursues the minimum number of stations and ignores the need for a certain degree of overlap between adjacent stations to ensure registration effect. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 The flowchart of the present application for determining the positioning network of the ground laser scanner.

[0041] Figure 2 The schematic diagram of the incidence angle of the ground laser scanner.

[0042] Figure 3The schematic diagram of the target structure plane model in the NetLogo integrated platform.

[0043] Figure 4 The schematic diagram of the target structure perimeter visible area in the NetLogo integrated platform.

[0044] Figure 5 The schematic diagram of the ground laser scanner station position set.

[0045] Figures 3-5 In the figure, black is the background color of the NetLogo integrated platform, the edge line with a small triangle represents the blocking area boundary, the edge line with a small diamond represents the virtual pre-assembly interface boundary, the edge line with a small circle represents the limit area boundary, the ordinary edge line represents the target structure non-pre-assembly interface boundary, the gray shadow area represents the visible area of the discrete points on the perimeter of the target structure, and the white small circle point represents the ground laser scanner station.

[0046] Figure 6 The flowchart of the genetic algorithm.

[0047] Figure 7 The schematic diagram of the Delaunay triangulation station network.

[0048] Figure 8 The schematic diagram of the station network undirected weighted graph.

[0049] Figure 9 The schematic diagram of the initial registration path.

[0050] Figure 10 The schematic diagram of the optimized registration path. DETAILED DESCRIPTION

[0051] The present application will be further described in detail below in combination with the drawings of the specification and specific examples.

[0052] The present application provides a ground laser scanner positioning network design method for pre-assembled structures. The structure information model of the construction site is built in the BIM software revit through the design scheme of the known structure and the on-site photos. After the structure information model is planarized, it is imported into the NetLogo integrated platform. According to the specifications of the ground laser scanner used, the parameter constraints are obtained. According to the structure information model and the construction site photos, the limit area where the ground laser scanner cannot be placed and the blocking area that will affect the measurement line of sight are defined. Through the setting of the model structure and the model parameters, a multi-agent model is generated. In the multi-agent model, the TLS station position set is generated according to the emphasis on the assembly interface. The genetic algorithm is used to calculate the station network of the ground laser scanner, and the best position set is obtained from the station position set. Finally, the positioning network is optimized based on the registration evaluation method, and the point cloud data scanning positioning network that meets the registration standard is obtained.

[0053] Embodiment 1; see Figures 1-10 .

[0054] The embodiment of the application discloses a positioning network design method for a ground laser scanner of a pre-assembled structure, comprising the following steps:

[0055] Step S1: According to the design scheme of the target structure, a structure information model of the target structure is built in a BIM software.

[0056] The design scheme (design drawing) of the target structure is generally provided by a construction unit or a design unit, and the BIM software generally adopts revit software.

[0057] Step S2: The structure information model is planarized and imported into a NetLogo integrated platform to obtain a plane model of the target structure; according to the photos of the surrounding area of the target structure obtained by shooting, a surrounding area model is generated outside the plane model of the target structure, the area in which the laser scanner cannot be placed is marked as a restricted area in the surrounding area model, and the area that will shield the line of sight of the laser scanner for measuring the target structure is marked as a shielding area, to obtain a multi-agent model; the multi-agent includes a target structure agent, a shielding area agent and a restricted area agent.

[0058] The photos of the surrounding area of the construction site can be shot by a drone or a satellite, or can be shot by a total station instrument positioning and a traditional photo shooting mode, for example, a high-definition camera, to determine all features of the surrounding area and the positional relationship of the features relative to the target structure, and the features include structural features, facility features and topographic features. The surrounding area generally refers to a ground area outside the perimeter of the target structure and having a radius not less than the working distance of the ground laser scanner.

[0059] The area in which the ground laser scanner cannot be placed in the surrounding area is the restricted area of the ground laser scanner, including a narrow space (the space size is less than the size required for installation of the ground laser scanner), an unstable ground (i.e., a ground that cannot provide a stable measurement environment for the ground laser scanner), and a natural area in which equipment cannot be erected; wherein the natural area in which equipment cannot be erected includes a river and a canyon.

[0060] When the multi-agent model is established in the NetLogo integrated platform, the background and size of the multi-agent model need to be set according to the size of the target structure and the surrounding area, the plane layout of the plane model of the target structure and the surrounding area model thereof is constructed, the target structure agent is obtained from the plane model of the target structure, the shielding area and the restricted area are marked in the surrounding area according to the shooting photos, the shape and position of each agent are adjusted and determined, the shielding area agent and the restricted area agent are respectively created, and the colors and attributes of each agent are selected in the NetLogo integrated platform to realize the visual distinction of each agent, such as Figure 3 .

[0061] Step S3: discretizing the perimeter of the target structure agent into a point set, wherein the density of discrete points on the perimeter corresponding to the target structure assembly interface is greater than the density of discrete points on the perimeter corresponding to the non-assembly interface; taking any discrete point on the perimeter as a starting point, finding the laser scanner station position that can observe the starting point in the peripheral region model according to the constraint parameters of the terrestrial laser scanner, and taking the obtained station set as the visible region of the starting point; superimposing the visible regions of all starting points on the perimeter to obtain the global visible region of the perimeter; based on the visual mutual difference principle, taking the global visible region as the global station region of the terrestrial laser scanner.

[0062] The constraint parameters of the terrestrial laser scanner are the working distance, the incident angle, and the field of view angle of the terrestrial laser scanner.

[0063] Working distance: the distance that can be scanned by the terrestrial laser scanner equipment;

[0064] Incident angle: the angle between the laser beam emitted by the terrestrial laser scanner and the normal line corresponding to a surface of the scanned target structure; for example, Figure 2 ;

[0065] Field of view angle (FOV): the range of the scanning angle of the terrestrial laser scanner;

[0066] In this embodiment, the Trimble X7 type terrestrial laser scanner is taken as an example, the working distance of which is 0.6m-80m, the incident angle is 40°, and the field of view angle is 360°x282°.

[0067] The visible region of any starting point on the perimeter of the target structure is marked with the same reference color, and color coding is used to represent the overlap degree of the visible regions corresponding to all starting points on the perimeter of the target structure, the lightness of the color corresponds to the height of the overlap degree, the lighter the color, the higher the overlap degree of the visible region, the darker the color, the lower the overlap degree of the visible region; the area with lighter color represents the superposition of the visible regions of multiple perimeter points, which means that the visibility of the station position in these areas is higher (more points on the perimeter are observed), and the area with darker color represents the superposition of the visible regions of fewer perimeter points, which means that the visibility of these areas is lower (fewer points on the perimeter are observed), for example, Figure 4By this color coding method, it can be intuitively identified which site position can observe a larger target structure range. Finally, this process helps us to quantify the amount of observation information of each potential terrestrial laser scanner site position. Because the emphasis on the assembly interface area needs to be met, when looking for visible areas outside the perimeter corresponding to the assembly interface, by encrypting the observed points (i.e. discrete points) on the perimeter, increasing the information amount of the observed points contained in the site position, the weight of the information of the observed points contained in the site in the visible area outside the perimeter corresponding to the assembly interface can be improved relative to the visible area outside the perimeter of the non-assembly interface.

[0068] Because the density of discrete points on the perimeter corresponding to the target structure assembly interface is greater than the density of discrete points on the perimeter corresponding to the non-assembly interface, that is, after encrypting (densifying) the discrete points on the perimeter of the assembly interface, generating a visible area on the discrete points on the perimeter corresponding to the assembly interface, the information amount contained in the set of terrestrial laser scanner site positions composed of this part of the area is greater than the information amount contained in the set of corresponding site positions before encryption or non-assembly interface corresponding perimeter. Then, when calculating the terrestrial laser scanner site at the position adjacent to the assembly interface corresponding perimeter and the non-assembly interface corresponding perimeter, based on the above encryption work, when calculating the terrestrial laser scanner site position in the subsequent calculation, it will naturally be closer to the assembly interface corresponding perimeter. In this way, the ground laser scanner can obtain more and more accurate point cloud data about the assembly interface when measuring, so as to complete the extraction of the feature assembly point. The feature assembly point is the virtual pre-assembly splicing control point, which is a key factor to ensure the engineering assembly precision, improve the construction efficiency and protect the structure safety. The more accurate the feature assembly point is, the higher the assembly precision is.

[0069] Step S4: According to the global site area of the terrestrial laser scanner obtained in S3 and the intelligent agent of the occlusion area and the intelligent agent of the restricted area obtained in S2, the initial site position set of the terrestrial laser scanner is calculated by using a genetic algorithm.

[0070] Referring to Figure 6 , the step of calculating the site position set by the genetic algorithm can be refined as follows:

[0071] 1) Initialization: randomly generate a set of terrestrial laser scanner site sets as an initial population;

[0072] The initial population includes a plurality of laser scanner site sets randomly generated; the site set I i is an individual I i in the population; the individual I i contains a number of sites, which is determined according to the size of the target structure. The number of sites contained in different individuals I i is the same, and the number of initial sites in this embodiment is 12.

[0073] 2) Individual evaluation: Calculate the fitness of each individual in the population;

[0074] The fitness function used by the genetic algorithm is expressed as follows according to formula (1):

[0075] (1)

[0076] Where: F(I i ) is individual I i Fitness score of R(I i ) is individual I i The fitness score of the non-penalty-term is based on its performance without violating any constraints; P(I i ) is a penalty term used to reduce the fitness score of individuals that violate the constraints;

[0077] Among them, R(I i ) is defined as the weighted sum of three parameters, which can be expressed as follows according to formula (2):

[0078] (2)

[0079] Where: For individual I i The entropy of opinions; For individual I i The size of the positioning network; For individual I i The degree of connectivity between sites in the network; the opinion entropy Γ quantifies a possible solution (i.e., individual I i ) provides information; the positioning network size Φ quantifies a possible solution (i.e., individual I i ) of the station locations; the connectivity Δ between the station locations measures a possible solution (i.e., individual I i ) between the site locations; 、 、 The parameters are 、 、 The weight of .

[0080] Determine weights 、 、 The principle is to ensure a balance between the amount of information obtained and the number of sites and to ensure that the visual area has a certain degree of overlap during subsequent registration. 、 、 They are 0.5, 0.4 and 0.1 respectively.

[0081] Individual Ii The proportion of the target structure perimeter that is scanned by the site set is determined by the ratio of the number of discrete points on the target structure perimeter scanned by the site set to the total number of discrete points on the perimeter; Determined based on the ratio of the number of inactivated sites to the total number of sites in the site set; It is determined by the connectivity of the site set, which is 1 if connected and 0 if not connected.

[0082] During calculation, an individual is a coding string (including sites and their order). During the calculation process, an explicit or implicit activation mechanism is usually added to the coding string. When the site is in the entire site area but not in the occluded area agent and the restricted area agent, it is dominant. In other cases, it is recessive. Explicit means activated, and recessive means inactivated. The purpose of setting recessive sites is to increase the genetic diversity of individuals and facilitate crossover and mutation operations.

[0083] Among them, the penalty term P(I i ) can be expressed as follows according to formula (3):

[0084] (3)

[0085] Where: n is individual I i The total number of sites j in ; is the weight associated with site j, which can be set according to the importance of the blocked area or restricted area. The larger the weight, the more severe the penalty for violating the constraint; is the indicator function, if individual I i If the middle station j is placed in the blocked area or restricted area, then =1, otherwise =0; In this embodiment, when a site (individual I i ) is in a blocked area or restricted area, take =∞, thereby eliminating such sites.

[0086] The fitness function is used to quantify the quality of individuals (site sets). The penalty term is proposed because sites in restricted or blocked areas are eliminated, thereby completing the screening of individuals (site sets).

[0087] 3) Selection operation: select individuals based on fitness values ​​to reproduce and form a new generation, using the tournament selection method;

[0088] 4) Crossover operation: A new individual is generated through a crossover operation. A single-point crossover is selected, that is, a crossover point is randomly selected in the encoding strings of two individuals (position sets), and then the sequence after the crossover point is exchanged to generate a new individual.

[0089] 5) Mutation operation: mutation operation is applied to increase the diversity of the population, mutation can randomly change some genes of individuals, which means changing the position of a certain site in the position set, the structure of the position set, etc.; after selection, crossover and mutation operation, the fitness is calculated again, and part of the individuals are eliminated to generate a new generation of population;

[0090] 6) Repeat the steps of selection, crossover, mutation, evaluation, elimination of part of the individuals and generation of new population, etc. until the preset iteration termination condition is reached.

[0091] The iteration termination condition is that when the fitness of the optimal individual and the fitness of the population no longer increase, the iteration is terminated. In this embodiment, when the number of iterations is less than 3000 times, the fitness no longer increases. After the algorithm stops, the most adaptive individual is output as the site position set, as shown in Figure 5 .

[0092] The global area of the ground laser scanner site is the definition domain when the genetic algorithm is calculated, and the shelter area agent and the restricted area agent are the constraint conditions when the calculation is performed.

[0093] Step S5: According to the initial site position set of the laser scanner, a laser scanner site network is established by using the Delaunay triangulation method; the site network is optimized by using a registration evaluation method to obtain a ground laser scanner positioning network;

[0094] In this embodiment, the positioning network is optimized based on the registration evaluation method, which can ensure that the overlap degree between adjacent site scanning areas meets the basic requirements of the automatic registration algorithm. The automatic registration algorithm refers to the automatic registration between adjacent site point cloud data. Generally, the ground laser scanner measurement has a supporting field software to realize automatic registration.

[0095] Step S5 can be further refined as:

[0096] 1) Based on the initial site position set of the ground laser scanner, a site network is established by using the Delaunay triangulation method, wherein the nodes represent the sites, and the edges represent the adjacent relationship between the sites, as shown in Figure 7 .

[0097] 2) Assign weights to the edges in the site network, and the weight of each edge is the overlap degree of the corresponding visible area between adjacent sites, to obtain an undirected weighted graph; in this way, the total weight of the registration path can be maximized, thereby optimizing the registration process;

[0098] 3) According to the undirected weighted graph, an initial registration path is generated by using the maximum spanning tree algorithm, as shown in Figure 9 ; the obtained registration path will connect all the sites, and ensure that the point cloud data of the entire site network can be effectively registered through the obtained registration path;

[0099] 4) Check the overlap of each edge in the registration path. If the overlap is lower than the preset threshold, add a new site between adjacent sites. Since the automatic registration algorithm will automatically realize the automatic registration of the visible areas of adjacent sites to ensure the overlap of the site point cloud data, the preset overlap threshold of the non-assembly interface site can be >0% (generally 2%~10%, 5% in this embodiment); then for the assembly interface, since the present invention increases the weight of the observed point information contained in the site outside the perimeter corresponding to the assembly interface, the preset overlap threshold of the assembly interface can be the same as the overlap threshold of the non-assembly interface site. The larger the overlap threshold, the more ground laser scanner sites there are, so it needs to be selected according to the actual project situation. If the overlap between adjacent scanning positions is lower than the preset threshold, successful registration may not be possible. In this case, new scanning positions need to be added between these adjacent scanning positions to increase their overlap. Until the overlap of all adjacent scanning positions meets the threshold requirement, the optimal registration path is finally obtained, such as Figure 10 .

[0100] 5) Based on the preferred registration path and the obtained sites, a laser scanner positioning network is obtained; the nodes in the preferred registration path are the terrestrial laser scanner sites, and the edges are the connections between adjacent terrestrial laser scanner sites, thus obtaining the terrestrial laser scanner positioning network.

[0101] When adding a new site between adjacent sites with insufficient overlap, the system first attempts to select the midpoint of the two adjacent sites as the new site. The system then checks whether the new site is feasible and safe (whether it is within restricted and blocked areas) and whether the overlap with the adjacent site's visible area meets the requirements. If the conditions are met, the new site is adopted. If not, the system searches for new sites in the adjacent ground until the requirements are met.

[0102] In this example, three new sites were added, for a total of 15 sites. This small number of new sites generally meets expectations. If the number of new sites exceeds expectations, a small number of sites can be added when selecting the initial number of sites (generating the initial population) to ensure that the recalculated total number of sites meets expectations, thereby reducing measurement costs.

[0103] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method for positioning determination of a terrestrial laser scanner for pre-assembly structures, characterized by: The method comprises the following steps: Step S1: according to the design scheme of the target structure, a structure information model is established in a BIM software; Step S2: the structure information model is planarized and then imported into a NetLogo integrated platform to obtain a planar model of the target structure; according to a photo of a surrounding area of the target structure obtained by shooting, a surrounding area model is generated outside the planar model, regions in the surrounding area model where a laser scanner cannot be placed are marked as restricted regions, and regions in the surrounding area model that will shield the line of sight of the laser scanner for measuring the target structure are marked as shielding regions, to obtain a multi-agent model; the multi-agent comprises a target structure agent, a shielding region agent and a restricted region agent; Step S3: a perimeter of the target structure agent is discretized into a point set, wherein the density of the discrete points on the perimeter corresponding to a target structure assembly interface is greater than the density of the discrete points on the perimeter corresponding to a non-assembly interface; taking any discrete point on the perimeter as a starting point, a laser scanner station point that can observe the starting point is searched in the surrounding area model according to constraint parameters of the laser scanner, and a set of obtained station points is taken as a visible region of the starting point; The visible regions of all starting points on the perimeter are superimposed to obtain a global visible region of the perimeter; Based on the visual mutual difference principle, the global visible region of the perimeter is taken as a station point region global domain of the ground laser scanner; Step S4: according to the station point region global domain of the laser scanner obtained in S3 and the shielding region agent and the restricted region agent obtained in S2, an initial station point position set of the laser scanner is calculated by using a genetic algorithm; Step S5: a laser scanner station point network is established by using a Delaunay triangulation method according to the initial station point position set of the laser scanner; and the station point network is optimized by using a registration evaluation method to obtain a laser scanner positioning network.

2. The method of claim 1, wherein: In step S2, the photo of the surrounding area of the target structure is shot by using a UAV or a satellite, or is shot by using a total station instrument positioning and a camera.

3. The method of claim 1, wherein: In step S2, the restricted regions comprise narrow spaces in the surrounding area of the target structure, unstable ground and natural regions where equipment cannot be erected; wherein the natural regions where equipment cannot be erected comprise rivers and canyons.

4. The method of claim 1, wherein: The constraint parameters of the laser scanner are a working distance, an incident angle and a field of view angle.

5. The method of claim 1, wherein: In step S4, the fitness function used by the genetic algorithm is expressed by formula (1): （1） Where: F(I i ) is individual I i Fitness score of R(I i ) is individual I i The fitness score when the penalty term is not triggered; P(I i ) is a penalty term; The individual refers to any laser scanner station point set in a population; and the population refers to any set of laser scanner station points in a calculation process of the genetic algorithm. wherein R(I i ) is represented by formula (2): （2） wherein: is the perspective entropy of individual I i ; is the positioning network size of individual I i ; is the connectivity between sites in individual I i ; , , are the weights of , , respectively; Among them, P(I i ) is expressed as follows according to formula (3): （3） where n is the individual I i the total number of sites j in the network; is the weight associated with site j; is an indicator function that is equal to 1 if the individual I i is placed in a blocked or restricted area, and = 1, otherwise = 0.

6. The method of claim 1, wherein: In step S4, when the initial station point position set of the laser scanner is calculated by using the genetic algorithm, tournament selection is used for selection operation, and single-point crossover is used for crossover operation.

7. The method of claim 1, wherein: The step S5 further comprises: S51: a station point network is established by using a Delaunay triangulation method based on the initial station point position set of the laser scanner, wherein a node represents a station point, and an edge represents an adjacency relationship between station points; S52: a weight is assigned to each edge in the station point network to obtain an undirected weighted graph; and the weight of each edge is the overlapping degree of the corresponding visible regions between adjacent station points; S53: an initial registration path is generated by using a maximum spanning tree algorithm according to the undirected weighted graph; S54, checking the overlap degree of each edge in the initial registration path, if the overlap degree is lower than a threshold, adding a new station between adjacent stations until the overlap degree of all adjacent stations meets the threshold requirement, and obtaining a preferred registration path; S55, obtaining a laser scanner positioning network according to the preferred registration path and the corresponding stations.

Images