Continuous sampling measurement path optimization method for three-dimensional scanner and related equipment

Abstract

The invention provides a continuous sampling measurement path optimization method for a three-dimensional scanner and related equipment, and relates to the technical field of automatic measurement. Comprising the steps of collecting surface contour data and viewpoint data of a scanned object; constructing a weighted graph by taking each viewpoint as a vertex and taking a path between every two viewpoints as an edge; inputting the weighted graph into an improved ant colony algorithm to optimize a connection sequence of vertexes on the weighted graph to obtain a measurement path optimization result of the scanned object, and executing a scanning task on the scanned object by the three-dimensional scanner based on the measurement path optimization result; compared with the prior art, the practicability and reliability of the measurement path are improved.

Description

Technical Field

[0001] The present invention relates to the field of automated measurement technology, and in particular to a continuous sampling measurement path optimization method for a three-dimensional scanner and related equipment. Background Art

[0002] Automated 3D scanner measurement involves a multi-degree-of-freedom robot (generally 6 degrees of freedom) carrying a laser 3D scanner, moving along a planned path, and scanning and measuring the object from multiple viewpoints to obtain complete 3D data. However, the 3D data obtained during the measurement process is largely affected by the viewpoint, as the viewpoint directly determines the measurement position and posture of the 3D scanner. At the same time, the robot drives the scanner along a measurement path that passes through all viewpoints, so the measurement path determines the feasibility and efficiency of the measurement task. Measurement path planning is usually divided into two steps: first, planning a set of discrete viewpoints that can cover the surface of the object being measured; second, connecting these viewpoints to form a continuous measurement path.

[0003] Many objects being measured have complex structures. Due to visual occlusion, numerous viewpoints are required for measurement, and the measurement path is also very complex, which increases measurement time. Traditionally, experienced humans have relied on teaching the robot based on their own experience to determine the order in which the viewpoints should be connected. This process is very time-consuming, and humans lack a reference method or basis for teaching during the planning process. Relying solely on experience, it is impossible to accurately select the most appropriate viewpoint connection sequence, making it difficult to obtain the optimal measurement path. This results in poor scanning results, and the viewpoint connection sequence needs to be repeatedly modified and debugged. This adds unnecessary measurement time and prevents the automated measurement equipment from achieving its maximum performance. Therefore, there is an urgent need for a method to automatically plan the viewpoint connection sequence to improve measurement efficiency and accuracy.

[0004] The existing technology provides a method for planning the measurement path. The Robotics module of the scanner control software provided by the scanner manufacturer generates the robot's scanning motion trajectory offline. According to the appearance characteristics of the product to be measured, the walking position of the laser scanner at the end of the robot is controlled and the path point labels are generated. The Robotics module automatically stores the path and generates a list. However, the three-dimensional scanning of complex objects often faces problems such as unreasonable scanning path planning, large amounts of data redundancy, and low real-time processing efficiency. It is difficult to meet the high requirements for real-time performance, accuracy, and robustness in practical applications.

[0005] With the rapid development of artificial intelligence, artificial intelligence technology has been applied to the field of measurement path optimization of robotic 3D scanners. Some researchers have tried to use an ant colony algorithm to optimize the measurement path. However, in the path optimization process of traditional ant colony algorithms, heuristic information and path weights usually only rely on path length, while ignoring the importance of measurement quality and measurement integrity to path optimization. Summary of the Invention

[0006] The present invention provides a continuous sampling measurement path optimization method for a three-dimensional scanner and related equipment, the purpose of which is to improve the practicality and reliability of the measurement path.

[0007] In order to achieve the above object, the present invention provides a continuous sampling measurement path optimization method for a three-dimensional scanner, comprising: Step 1: collecting surface profile data and viewpoint data of the scanned object, where the surface profile data includes multiple surface slices, and the viewpoint data includes multiple viewpoints, and the surface slices of the scanned object correspond to the viewpoints one by one; Step 2: construct a weighted graph by taking each viewpoint as a vertex and the path between each two viewpoints as an edge; Step 3: Input the weighted graph into the improved ant colony algorithm to optimize the connection order of the vertices on the weighted graph to obtain the measurement path optimization result of the scanned object. The 3D scanner performs the scanning task on the scanned object based on the measurement path optimization result.

[0008] Furthermore, the weighted graph is fed into the improved ant colony algorithm to optimize the connection order of the vertices on the weighted graph, and the measurement path optimization results of the scanned object are obtained, including: Initialize the pheromone distribution matrix, heuristic information matrix, number of iterations and historical optimal weights, and start iteration; All ants are numbered and randomly placed on any vertex of the weighted graph. The path record, number of vertices passed, and sequence number set of vertices not passed by each ant are initialized. All ants randomly select the next vertex according to equal probability distribution or probability distribution determined by heuristic information until all vertices in the weighted graph are traversed; Calculate the weight obtained by each ant on the path it passes through, and update the historical optimal weight based on the weight; Calculate the weight ranking and pheromone increment matrix of each ant; The heuristic information matrix is ​​updated based on the weight ranking and the pheromone increment matrix to obtain an updated heuristic information matrix; Iteration is repeated based on the updated heuristic information matrix until the number of iterations is too many or the weight does not improve after a certain exponential iteration, and the path corresponding to the optimal weight is used as the measurement path optimization result of the scanned object.

[0009] Furthermore, the weight of each ant on the path it passes is calculated, including: Define the path between any two adjacent vertices that each ant passes as the edge that each ant passes through, and calculate the edge weight; Calculate the vertex weight of each vertex reached by the ant in the path; The total weight obtained by each ant on the path it passes is calculated based on the edge weight and vertex weight.

[0010] Furthermore, the edge weight calculation expression is: ; in, represents the edge weight, Represents a vertex With vertex The edges between Represents a vertex To the top the movement process, represents the path length, It represents the maximum motion cost that can be generated by the combination of any two vertices.

[0011] To further explain, the vertex weight is calculated as: ; in, Represents a vertex The weight of Indicates entering and leaving a vertex The trajectory, and Represents the scanner execution trajectory The subsequent serial number is Surface fragmentation Measurement accuracy and measurement coverage, Indicates the preset minimum measurement coverage.

[0012] To put it more concretely, the total weight each ant obtains on the path it passes is: ; in, Indicates the path passed The total weight obtained on represents the proportion of exercise cost, Represents the proportion of measured effect.

[0013] Furthermore, the expression for updating the heuristic information matrix is: ; ; ; in, represents the updated two-dimensional heuristic information matrix, represents the updated three-dimensional heuristic information matrix, Indicates that when the scanner is in When, leading to The amount of heuristic information on the path, Indicates when the scanner arrive Then, to The amount of heuristic information on the path, represents the number of vertices, represents the coverage probability, Indicates that from the vertex To the top Back to the top The increment of pheromone concentration along the path.

[0014] The present invention also provides a continuous sampling measurement path optimization device for a three-dimensional scanner, comprising: An acquisition module, configured to acquire surface profile data and viewpoint data of a scanned object, wherein the surface profile data includes a plurality of surface slices, and the viewpoint data includes a plurality of viewpoints, and the surface slices of the scanned object correspond to the viewpoints in a one-to-one manner; A construction module is used to construct a weighted graph by taking each viewpoint as a vertex and the path between each two viewpoints as an edge; The optimization module is used to input the weighted graph into the improved ant colony algorithm to optimize the connection order of the vertices on the weighted graph, thereby obtaining the measurement path optimization result of the scanned object. The three-dimensional scanner performs the scanning task on the scanned object based on the measurement path optimization result.

[0015] The present invention also provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a continuous sampling measurement path optimization method for a three-dimensional scanner is implemented.

[0016] The present invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for optimizing a continuous sampling measurement path for a three-dimensional scanner is implemented.

[0017] The above solution of the present invention has the following beneficial effects: The present invention collects surface contour data and viewpoint data of the scanned object; uses each viewpoint as a vertex and the path between each two viewpoints as an edge to construct a weighted graph; inputs the weighted graph into an improved ant colony algorithm to optimize the connection order of the vertices on the weighted graph, and obtains an optimized measurement path result of the scanned object. The three-dimensional scanner performs a scanning task on the scanned object based on the measurement path optimization result. Compared with the existing technology, the present invention improves the practicality and reliability of the measurement path.

[0018] Other beneficial effects of the present invention will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Schematic diagram of a flow chart of an embodiment of the present invention; Figure 2 Flowchart of an improved ant colony algorithm according to an embodiment of the present invention; Figure 3 Schematic diagram of the structure of a continuous sampling measurement path optimization device in an embodiment of the present invention; Figure 4 Schematic diagram of the structure of the terminal device in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] To make the technical problems, technical solutions, and advantages to be solved by the present invention more clear, the following is a detailed description with reference to the accompanying drawings and specific embodiments. It is obvious that the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0021] In the description of the present invention, it should be noted that the terms "first", "second" and "third" are only used for descriptive purposes and should not be understood as indicating or implying relative importance.

[0022] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0023] In response to the existing problems, the present invention provides a continuous sampling measurement path optimization method for a three-dimensional scanner and related equipment.

[0024] like Figure 1 As shown, an embodiment of the present invention provides a continuous sampling measurement path optimization method for a three-dimensional scanner, comprising: Step 1: collecting surface profile data and viewpoint data of the scanned object, where the surface profile data includes multiple surface slices, and the viewpoint data includes multiple viewpoints, and the surface slices of the scanned object correspond to the viewpoints one by one; Step 2: construct a weighted graph by taking each viewpoint as a vertex and the path between each two viewpoints as an edge; Step 3: Input the weighted graph into the improved ant colony algorithm to optimize the connection order of the vertices on the weighted graph to obtain the measurement path optimization result of the scanned object. The 3D scanner performs the scanning task on the scanned object based on the measurement path optimization result.

[0025] In the embodiment of the present invention, the surface contour data of the scanned object is represented by a surface point set. express, represents a point in space, It is a surface point The coordinate value of Represents the sequence number of the vertex; each surface fragment All are surface point sets A subset of all surface slices The union of covers all surfaces that need to be measured on the scan target, subscript Represents the serial number of the surface slice, each surface slice Corresponding to a viewpoint , viewpoint Indicates surface sharding The scanner position where the measurement is taken, are the position coordinates of the scanner, is the Euler angle of the scanner posture, all viewpoints Composition viewpoint data .

[0026] Specifically, weighted graph Depend on and constitute, is the vertex set of the graph, vertex represent Viewpoint in , and One-to-one correspondence; Represents the edge set of a weighted graph, edge Representative from the viewpoint To Viewpoint movement process.

[0027] Specifically, the weighted graph is input into the improved ant colony algorithm to optimize the connection order of the vertices on the weighted graph, and the measurement path optimization results of the scanned object are obtained, including: Initialize the pheromone distribution matrix, heuristic information matrix, number of iterations and historical optimal weights, and start iteration; All ants are numbered and randomly placed on any vertex of the weighted graph. The path record, number of vertices passed, and sequence number set of vertices not passed by each ant are initialized. All ants randomly select the next vertex according to equal probability distribution or probability distribution determined by heuristic information until all vertices in the weighted graph are traversed; Calculate the weight obtained by each ant on the path it passes through, and update the historical optimal weight based on the weight; Calculate the weight ranking and pheromone increment matrix of each ant; The heuristic information matrix is ​​updated based on the weight ranking and the pheromone increment matrix to obtain an updated heuristic information matrix; The above iterative process is repeated based on the updated heuristic information matrix until the number of iterations is too many or the weight does not improve after a certain exponential iteration. The path corresponding to the optimal weight is taken as the measurement path optimization result of the scanned object.

[0028] In the embodiment of the present invention, Figure 2 As shown in Figure 2, the specific process of the improved ant colony algorithm is as follows: Step 3.1 Initialize the pheromone distribution matrix For shape The matrix of all 1s, represents the number of viewpoints, Indicates from arrive Afterwards, leading to Pheromone concentration on the path, heuristic information matrix 、 , number of iterations , historical optimal weight ; Step 3.2 Then iterate: Step 3.2.1 An ant is randomly placed at any vertex ,Record Indicates the vertex number where the ant is currently located. All ants are numbered. The ants are represented as , create a path record for each ant ,in ~ Is the ant's first The vertices passed by Assigned to To record the first randomly placed vertex, ~ Initialized to 0, the number of vertices initialized is 1, the set of vertex numbers that each ant has not passed , Include The sequence number of the vertex that has not been passed, due to Placed , so from Delete ; Step 3.2.2 Repeat the following steps, and after each cycle Increase by 1, Increased from 2 to Then the loop ends, and each loop makes all ants go to the next vertex; Loop through the following steps, after each loop Increase by 1, Increase from 1 to Then the loop ends, and each loop makes one ant go to the next vertex; The system randomly takes a Boolean value or ; if for ,Do: Randomly go to A vertex that has not been visited ; if for ,Do: if , that is, when the ant is at the first vertex and is about to go to the second vertex, Go Vertices not visited The probability of , at this time the ant does not have a "previous vertex", so it is specially processed and uses an independent heuristic information matrix ; if , that is, when the ant goes to the third to the last vertex, Go Vertices not visited The probability of ; The above two probabilities mean: The probability of going to a certain unpassed vertex is equal to the heuristic information of the vertex among all unpassed vertices (i.e. The ratio of the sum of the heuristic information of the vertices in .

[0029] Perform the following steps to record Go to the top :Will Assign to in , then from Delete , which means when Choose to go , you need to change the vertex number from from The set of vertices not passed through Move out, move in Path record In the , move-out is implemented as deletion, and move-in is implemented as assignment overwriting; It should be noted that in step 3.2.2, for The current vertex number, for The sequence number of the previous vertex. and from In the query, that is Middle elements and elements; for The sequence number of the next vertex that may be visited is undecided and needs to be traversed The possible vertices to which you can go are recorded in .

[0030] Step 3.2.3 Count each ant Record the path it takes The total weight obtained : Each path from arrive , representing the ants passing through the edge , get the edge weight ; Each of , representing the ant reaching the top , get the vertex weight ; Calculate the total weight based on edge weight and vertex weight ; Step 3.2.4 If the weight of the optimal path generated in this iteration is Less than , then Assign to ; Step 3.2.5 If In recent There is no decrease in the number of iterations, or , then terminate the iteration, otherwise continue the iteration; Step 3.2.6 Calculate the weight ranking of each ant ; Step 3.2.7 Calculate the shape Pheromone increment matrix , It is from arrive Afterwards, leading to The increment of pheromone concentration along the path is: If an ant The first path is arrive ,but Increase , since the first segment of the path does not have a previous vertex, Take all values ​​(i.e. )of Both increase pheromones; If an ant The second to last section of the path is from arrive Afterwards, move towards ,but Increase ; Step 3.2.8 Assign to , ; Step 3.2.9 Update the heuristic information matrix and set the number of iterations Updated to ; Step 3.3 Generate of The corresponding vertex order is recorded as , This is the optimal scan path for output.

[0031] Specifically, the total weight obtained by each ant on the path it passes through is calculated, including: Define the path that each ant passes from vertex to vertex as the edge passed by each ant, and calculate the edge weight ; Calculate the vertex weight of each ant reaching a vertex along the path ; Based on edge weights and vertex weights Calculate the total weight of each ant on the path it passes .

[0032] Specifically, the calculation expression of edge weight is: ; in, represents the edge weight, Represents a vertex With vertex The edges between Represents a vertex To the top The movement process, represents the path length, It represents the maximum motion cost that can be generated by the combination of any two vertices.

[0033] Specifically, the calculation expression of vertex weight is: ; in, Represents a vertex The weight of Indicates the measurement accuracy, and Represents the scanner execution trajectory The subsequent serial number is Surface fragmentation Measurement accuracy and measurement coverage, defining measurement accuracy To characterize execution back The accuracy of the measurement results, represents the measurement coverage, Indicates entering and leaving a vertex The trajectory of The starting point of Only ants leave Trajectory The end point in Contains only ants entering The trajectory, Indicates the preset minimum measurement coverage, defining the measurement coverage To execute back The proportion of the area measured in .

[0034] In this embodiment of the present invention, vertex weights are subject to measurement accuracy. and measuring coverage Joint influence. Measurement accuracy and measuring coverage It is a function determined by the performance and characteristics of the 3D scanner actually used, provided by the manufacturer, and the measurement accuracy It should reflect the accuracy of the scanner's measurement results of a certain surface after performing a certain scanning action. The higher the accuracy, the better. The higher the value, the better. Measuring coverage It should reflect the completeness of the measurement results of a surface slice after the 3D scanner performs a certain scanning action. The more complete The higher the value.

[0035] Specifically, the total weight obtained by each ant on the path it passes is: ; in, Indicates the path passed The total weight obtained on represents the proportion of exercise cost, represents the proportion of measurement effect, .

[0036] Specifically, the heuristic information matrix is ​​expressed as and ,in Used to guide the selection of the second vertex, Used to guide the selection of subsequent vertices, the value of the heuristic information matrix and the measurement accuracy and measuring coverage Positively correlated with pheromone concentration and path length It is negatively correlated, and the expression for updating the heuristic information matrix is: ; ; ; in, represents the updated two-dimensional heuristic information matrix, represents the updated three-dimensional heuristic information matrix, Indicates that when the scanner is in When, leading to The amount of heuristic information on the path, Indicates when the scanner arrive Then, to The amount of heuristic information on the path, represents the number of vertices, represents the coverage probability, Represents a vertex To the top The movement process, Indicates that from the vertex To the top Back to the top The increment of pheromone concentration along the path.

[0037] In the measurement path planning of an example scanning object, the motion cost and measurement coverage of the measurement path obtained using the method provided by the embodiment of the present invention and the existing method are shown in Table 1 below: Table 1 Movement cost and measurement coverage of measurement paths ; As can be seen from Table 1 above, compared with existing methods, the embodiment of the present invention can increase the measurement coverage from 70% to 90% to nearly complete coverage. However, since the embodiment of the present invention adds constraints on the measurement coverage and excludes some shorter measurement paths that are detrimental to the measurement coverage, the motion cost incurred by the embodiment of the present invention increases.

[0038] The embodiment of the present invention collects surface contour data and viewpoint data of the scanned object; constructs a weighted graph using each viewpoint as a vertex and the path between each two viewpoints as an edge; inputs the weighted graph into an improved ant colony algorithm to optimize the connection order of the vertices on the weighted graph, thereby obtaining an optimized measurement path result for the scanned object. The 3D scanner then performs a scanning task on the scanned object based on the optimized measurement path result. Compared with the prior art, the embodiment of the present invention adds vertex weights reflecting measurement coverage and measurement quality to the total weight, encouraging the ant colony to find a viewpoint connection sequence that satisfies measurement coverage constraints and has both good motion cost and measurement quality, thus avoiding the damage to measurement coverage and quality caused by the prior art's pursuit of the shortest path. The embodiment also defines a three-dimensional pheromone matrix and a heuristic information matrix. Compared with the two-dimensional matrix of the prior art, the three-dimensional pheromone matrix and the heuristic information matrix can reflect the characteristics of measurement coverage and measurement quality related to historical paths, providing the ant colony with path selection guidance information related to historical paths, accelerating the ant colony's convergence to the optimal viewpoint connection sequence, improving the coverage and measurement quality of the measurement path, ensuring that all parts of the object to be measured are measured without omission and with high quality, and guaranteeing the practicality and reliability of the measurement path.

[0039] Corresponding to the continuous sampling measurement path optimization method for a three-dimensional scanner described in the above embodiment, Figure 3 As shown, an embodiment of the present invention further provides a continuous sampling measurement path optimization device 100 for a three-dimensional scanner, and the continuous sampling measurement path optimization device 100 includes: An acquisition module 101 is configured to acquire surface profile data and viewpoint data of a scanned object, wherein the surface profile data includes a plurality of surface slices, and the viewpoint data includes a plurality of viewpoints, and the surface slices of the scanned object correspond to the viewpoints in a one-to-one manner; A construction module 102 is configured to construct a weighted graph by taking each viewpoint as a vertex and the path between each two viewpoints as an edge; The optimization module 103 is used to input the weighted graph into the improved ant colony algorithm to optimize the connection order of the vertices on the weighted graph to obtain the measurement path optimization result of the scanned object. The 3D scanner performs the scanning task on the scanned object based on the measurement path optimization result.

[0040] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0041] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0042] The embodiment of the present invention further provides a terminal device, such as Figure 4 As shown, the terminal device D10 of this embodiment includes: at least one processor D100 ( Figure 4 Only one processor is shown in the figure), a memory D101, and a computer program D102 stored in the memory D101 and executable on the at least one processor D100. When the processor D100 executes the computer program D102, the above-mentioned continuous sampling measurement path optimization method for a three-dimensional scanner is implemented.

[0043] The terminal device D10 can be a computing device such as a desktop computer, a notebook, a PDA, a server, a server cluster, a cloud server, etc. The terminal device may include, but is not limited to, a processor D100 and a memory D101. It will be understood by those skilled in the art that Figure 4 This is merely an example of the terminal device D10 and does not constitute a limitation on the terminal device D10 . The terminal device D10 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device D10 may also include input and output devices, network access devices, etc.

[0044] The processor D100 may be a central processing unit (CPU), or may be another general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.

[0045] In some embodiments, the memory D101 may be an internal storage unit of the terminal device D10, such as a hard disk or memory of the terminal device D10. In other embodiments, the memory D101 may also be an external storage device of the terminal device D10, such as a plug-in hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device D10. Furthermore, the memory D101 may include both an internal storage unit of the terminal device D10 and an external storage device. The memory D101 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program. The memory D101 may also be used to temporarily store data that has been output or is about to be output.

[0046] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0047] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0048] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, a continuous sampling measurement path optimization method for a three-dimensional scanner is implemented.

[0049] If the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process steps in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a construction device / terminal device, a recording medium, computer memory, read-only memory (ROM), random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Examples include a USB flash drive, a removable hard drive, a magnetic disk, or an optical disk.

[0050] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A continuous sampling measurement path optimization method for a three-dimensional scanner, characterized in that: include: Step 1: collecting surface contour data and viewpoint data of a scanned object, wherein the surface contour data includes a plurality of surface slices, and the viewpoint data includes a plurality of viewpoints, and the surface slices of the scanned object correspond to the viewpoints one to one; Step 2: construct a weighted graph by taking each viewpoint as a vertex and the path between each two viewpoints as an edge; Step 3: Input the weighted graph into the improved ant colony algorithm to optimize the connection order of the vertices on the weighted graph to obtain the measurement path optimization result of the scanned object, and the three-dimensional scanner performs the scanning task on the scanned object based on the measurement path optimization result.

2. The continuous sampling measurement path optimization method for a three-dimensional scanner according to claim 1, characterized in that: Inputting the weighted graph into the improved ant colony algorithm to optimize the connection order of vertices on the weighted graph to obtain the measurement path optimization result of the scanned object, including: Initialize the pheromone distribution matrix, heuristic information matrix, number of iterations and historical optimal weights, and start iteration; All ants are numbered and randomly placed on any vertex of the weighted graph, and the path record, the number of vertices passed, and the sequence number set of vertices not passed by each ant are initialized; All ants randomly select the next vertex according to an equal probability distribution or a probability distribution determined by heuristic information until all vertices in the weighted graph are traversed; Calculate the weight obtained by each ant on the path it passes through, and update the historical optimal weight based on the weight; Calculate the weight ranking and pheromone increment matrix of each ant; updating the heuristic information matrix based on the weight ranking and the pheromone increment matrix to obtain an updated heuristic information matrix; Iteration is repeated based on the updated heuristic information matrix until the number of iterations is too many or the weight does not improve after a certain exponential iteration, and the path corresponding to the optimal weight is used as the measurement path optimization result of the scanned object.

3. The continuous sampling measurement path optimization method for a three-dimensional scanner according to claim 2, characterized in that: Calculate the weight of each ant on the path it passes through, including: Define the path between any two adjacent vertices that each ant passes as the edge that each ant passes through, and calculate the edge weight; Calculate the vertex weight of each vertex reached by the ant in the path; The total weight obtained by each ant on the path it passes through is calculated based on the edge weights and the vertex weights.

4. The continuous sampling measurement path optimization method for a three-dimensional scanner according to claim 3, characterized in that: The calculation expression of the edge weight is: ； in, represents the edge weight, Represents a vertex With vertex The edges between Represents a vertex To the top the movement process, represents the path length, It represents the maximum motion cost that can be generated by the combination of any two vertices.

5. The continuous sampling measurement path optimization method for a three-dimensional scanner according to claim 4, characterized in that: The calculation expression of the vertex weight is: ； in, Represents a vertex The weight of Indicates entering and leaving a vertex The trajectory, and Represents the scanner execution trajectory The subsequent serial number is Surface fragmentation Measurement accuracy and measurement coverage, Indicates the preset minimum measurement coverage.

6. The continuous sampling measurement path optimization method for a three-dimensional scanner according to claim 5, characterized in that: The total weight obtained by each ant on the path it passes is: ； in, Indicates the path passed The total weight obtained on represents the proportion of exercise cost, Represents the proportion of measured effect.

7. The continuous sampling measurement path optimization method for a three-dimensional scanner according to claim 6, characterized in that: The expression for updating the heuristic information matrix is: ； ； ； in, represents the updated two-dimensional heuristic information matrix, represents the updated three-dimensional heuristic information matrix, Indicates that when the scanner is in When, leading to The amount of heuristic information on the path, Indicates when the scanner arrive Then, to The amount of heuristic information on the path, represents the number of vertices, represents the coverage probability, Indicates that from the vertex To the top Back to the top The increment of pheromone concentration along the path.

8. A continuous sampling measurement path optimization device for a three-dimensional scanner, characterized in that: include: An acquisition module, configured to acquire surface profile data and viewpoint data of a scanned object, wherein the surface profile data includes a plurality of surface slices, and the viewpoint data includes a plurality of viewpoints, and the surface slices of the scanned object correspond to the viewpoints in a one-to-one manner; A construction module is used to construct a weighted graph by taking each viewpoint as a vertex and the path between each two viewpoints as an edge; The optimization module is used to input the weighted graph into the improved ant colony algorithm to optimize the connection order of the vertices on the weighted graph, thereby obtaining the measurement path optimization result of the scanned object, and the three-dimensional scanner performs the scanning task on the scanned object based on the measurement path optimization result.

9. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the continuous sampling measurement path optimization method for a three-dimensional scanner is implemented as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the continuous sampling measurement path optimization method for a three-dimensional scanner is implemented.

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