Point cloud splicing method and point cloud splicing device based on high-precision coordinate measuring machine

Abstract

The invention discloses a point cloud splicing method and a point cloud splicing device based on a high-precision coordinate measuring machine. The point cloud splicing method comprises the following steps: controlling a depth camera to pre-scan a measured object; a path planning algorithm is adopted to plan scanning sites and scanning paths of the laser scanner, and coordinate position parameters of all the scanning sites are determined; controlling the laser scanner to perform multi-view point cloud data acquisition on the measured object according to the set scanning site and scanning path to obtain multi-view point cloud data; calculating a transformation matrix corresponding to each scanning site, and performing position transformation on each point cloud data; and splicing the multi-view point cloud data after position transformation and removing repeated points. According to the invention, the steps are simple, the problems of incomplete point cloud data, large multi-site point cloud splicing error and the like caused by shielding and inaccurate external parameters of the laser scanner are avoided, and the accuracy and efficiency in the point cloud splicing process are greatly improved.

Description

Technical Field

[0001] The invention relates to a point cloud stitching technology, and in particular to a point cloud stitching method and a point cloud stitching device based on a high-precision coordinate measuring machine. Background Art

[0002] In today's era of rapid development of digitalization and informatization, point cloud stitching technology, as a key link in three-dimensional spatial data processing, has shown its indispensable importance in many fields. Point cloud, as a three-dimensional data representation that accurately depicts the surface morphology of objects, is increasingly widely used in high-end technical fields such as unmanned driving, robot navigation, and surveying and mapping geographic information systems. With the continuous advancement of deep learning technology, point cloud stitching technology has also achieved significant development, including point cloud calibration, fusion, automatic classification, visualization and post-processing.

[0003] In the field of academic research, point cloud stitching technology has been regarded as one of the key technologies in the field of three-dimensional imaging. The process involves finding the optimal spatial transformation matrix T to minimize the sum of the distances between the transformed point cloud P and the target point cloud Q, and to achieve optimization of six degrees of freedom. At present, the most commonly used point cloud stitching technology mainly relies on advanced algorithms, such as the stitching method based on the ICP (iterative closest point) algorithm, which achieves high-precision stitching by iteratively optimizing the correspondence between point clouds. Its advantage is that it can process point cloud data with partially overlapping areas and has high stitching accuracy. In addition, feature-based point cloud stitching technology has also attracted much attention. It achieves stitching by extracting significant feature points in the point cloud for matching. Its advantage is that it is more robust to noise and outliers.

[0004] In the current technical field, point cloud data stitching methods can be divided into two categories: manual registration and automatic registration, depending on whether manual intervention is involved. In manual registration technology, the stitching process relies on identifying and establishing correspondences between points of the same name. This technical path requires at least three sets of points of the same name to establish an accurate registration transformation. However, this method has high requirements for point selection accuracy, and the operation process is cumbersome, resulting in low overall registration efficiency. On the other hand, automatic registration technology uses algorithms to automatically extract feature points from point cloud data, and realizes registration and stitching between point clouds based on these feature points. Despite this, there is still a certain gap in stability and accuracy between automatic registration technology and manual registration, and its adaptability and flexibility in different application scenarios are also insufficient.

[0005] At present, the research on the improvement of point cloud stitching technology mainly focuses on using optimization algorithms to improve the accuracy of point cloud stitching, so as to better obtain the spatial transformation matrix required for point cloud stitching. However, when collecting point clouds in complex scenarios, the acquisition of external parameters often encounters inaccuracies, resulting in large errors in the stitching of point cloud data. In this case, traditional methods relying on algorithms for stitching may not provide satisfactory results, so manual stitching is often required to correct these errors. However, due to the subjectivity and complexity of manual operations, using this method is not only inefficient but also prone to large errors. In addition, the errors in manual operations may also come from multiple links such as instrument calibration and data preprocessing, resulting in deviations between the stitched point cloud data and the geometric shape of the actual object. How to efficiently and accurately obtain the external parameters for point cloud stitching is a challenge in current research. These challenges limit the application of point cloud stitching technology in high-precision detection and complex scene reconstruction. Summary of the Invention

[0006] Current point cloud stitching algorithms often fall into local extrema and lead to stitching failures when the initial position of the image is not good, or when there is a lot of background occlusion of the object being measured and the scene is complex. To overcome the above-mentioned shortcomings and deficiencies of the prior art, the purpose of the present invention is to provide a point cloud stitching method based on a high-precision coordinate measuring machine, which can complete the acquisition and stitching of three-dimensional point cloud data of a complete object through only one stitching without going through the steps of rough stitching and fine stitching. The steps are simple and avoid the problems of incomplete point cloud data caused by occlusion and inaccurate external parameters of the laser scanner, large stitching errors of multi-station point clouds, and difficulties in processing complex large-scale point cloud data, realizing the innovation of point cloud stitching technology in complex scenarios and greatly improving the accuracy and efficiency in the point cloud stitching process.

[0007] Compared with the prior art, the present invention has significant technological breakthroughs. Existing stitching methods based on the ICP algorithm can achieve stitching, but they rely on iterative optimization of point cloud correspondence relationships, resulting in huge computational amounts when dealing with large-scale point cloud data, and the stitching efficiency will decrease significantly as the data volume increases. Moreover, when the initial position deviation of the point cloud is large or there is occlusion, it is easy to fall into a local optimal solution, resulting in limited stitching accuracy. Feature-based point cloud stitching technology, although more robust to noise and outliers, the accuracy and stability of feature extraction will be affected in complex scenarios, thus affecting the stitching accuracy.

[0008] From the perspective of the mathematical model, the present invention constructs a more accurate coordinate transformation model by precisely determining three translation parameters (X-axis translation parameter Xi, Y-axis translation parameter Yi, Z-axis translation parameter Zi) and three rotation parameters (X-axis rotation parameter ωx, Y-axis rotation parameter ωy, Z-axis rotation parameter ωz) of the scanning site relative to the reference point. This model is directly based on the accurate position information of the scanning site. Compared with the traditional stitching method that finds the optimal transformation matrix through iterative calculation, it fundamentally avoids the problem of stitching error accumulation caused by poor initial conditions, provides a solid mathematical basis for achieving high-precision point cloud stitching, and solves the accuracy and efficiency problems of point cloud stitching at the same time.

[0009] The present invention also provides a point cloud stitching device, which uses a high-precision slide rail, a multi-degree-of-freedom robotic arm, a depth camera, and a laser scanner to collect point cloud data, and the technical means are simple and easy to implement.

[0010] The object of the present invention is achieved through the following technical solutions:

[0011] The present invention provides a point cloud stitching method based on a high-precision coordinate measuring machine, including the following steps:

[0012] S1 Control the depth camera to pre-scan the object to be measured, and obtain the position information of the occluder in the scene and the external contour of the object to be measured;

[0013] S2 According to the results of the pre-scan, use a path planning algorithm to plan the scanning sites and scanning paths of the laser scanner, and determine the coordinate position parameters of each scanning site;

[0014] S3 According to the planning results of step S2, set the scanning sites and scanning paths, and control the laser scanner to collect multi-viewpoint cloud data of the object to be measured according to the set scanning sites and scanning paths, and obtain multi-viewpoint cloud data;

[0015] S4 According to the coordinate position parameters of each scanning site obtained in step S2, obtain the transformation matrix of the point cloud data corresponding to each scanning site, and perform position transformation on each point cloud data obtained in step S3;

[0016] S5 Stitch the multi-viewpoint cloud data after position transformation in step S4 to obtain three-dimensional point cloud data, and remove duplicate points from the three-dimensional point cloud data.

[0017] Preferably, in step S2, according to the results of the pre-scan, using a path planning algorithm to plan the scanning sites and scanning paths of the laser scanner, and determine the coordinate position parameters of each scanning site, specifically:

[0018] S21 processes the pre-scanning results obtained in step S1, and converts the contour information and occlusion information of the object to be measured obtained by pre-scanning into point cloud data;

[0019] S22 adopts a greedy covering algorithm, selects the point that can cover the most un-scanned contours as the first station from the starting point, marks the covered area and continues to select the next station that can cover the most un-scanned contours until the contours of the entire object to be measured are covered;

[0020] S23 performs path planning, sets the stations as graph nodes, sets the connections as edges and weights them, searches for the shortest path sequence and checks the feasibility in combination with the robot motion constraints. At the same time, a dynamic adjustment mechanism is designed. In case of an anomaly, the RRT algorithm is used to re-plan the local path to bypass obstacles to the target station;

[0021] S24 uses all the finally determined stations as scanning stations, records their coordinate position parameters as the external parameters for point cloud stitching.

[0022] Preferably, in step 24, the search for the shortest path sequence is specifically: using the A* or Dijkstra algorithm to search for the shortest path sequence.

[0023] Preferably, for the i-th scanning station, its coordinate position parameters include the X-axis translation parameter Xi relative to the reference point, the Y-axis translation parameter Yi relative to the reference point, the Z-axis translation parameter Zi relative to the reference point, and the X-axis rotation parameter ω x relative to the reference point, the Y-axis rotation parameter ω y relative to the reference point, the Z-axis rotation parameter ω z ; where i = 1, 2, 3..., representing the number of scanning stations.

[0024] Preferably, in step S4, the translation matrix, rotation matrix and transformation matrix of the i-th scanning station are shown in formulas (1), (2), and (3);

[0025]

[0026] where i = 1, 2, 3..., j = x, y, z.

[0027] Preferably, in step S5, the removal of duplicate points from the three-dimensional point cloud data is specifically:

[0028] By dividing the data into grid cells, detecting and removing coordinate points smaller than the set threshold to eliminate duplicate points, and then performing data optimization and smoothing processing.

[0029] The present invention also provides a point cloud stitching device for implementing the point cloud stitching method based on a high-precision coordinate measuring machine, including a depth camera, a laser scanner, a multi-degree-of-freedom robotic arm, a high-precision slide rail, and a control device; the depth camera and the laser scanner are installed on the multi-degree-of-freedom robotic arm; the multi-degree-of-freedom robotic arm is installed on the slide rail;

[0030] The depth camera is used for pre-scanning the object to be measured and storing the scan data;

[0031] The laser scanner is used for collecting, storing, and processing multi-viewpoint cloud data of the object to be measured;

[0032] The control device is used for planning the path of the high-precision slide rail, setting the motion trajectory of the multi-degree-of-freedom robotic arm, and sending instructions to the multi-degree-of-freedom robotic arm to control its motion.

[0033] Preferably, the positioning accuracy of the high-precision slide rail is at the micron level.

[0034] The present invention also provides an electronic device, including a processor and a memory;

[0035] The memory is used for storing non-transitory computer instructions;

[0036] When the non-transitory computer instructions are executed by the processor, the processor implements the point cloud stitching method based on the high-precision coordinate measuring machine.

[0037] The present invention also provides a storage medium for storing non-transitory computer instructions, which, when run, execute the point cloud stitching method based on the high-precision coordinate measuring machine.

[0038] In the present invention, the X-axis translation parameter, the Y-axis translation parameter, and the Z-axis translation parameter respectively represent the translation distance differences of the point cloud data in the X, Y, and Z-axis directions of the scanner position relative to the reference point; the X-axis rotation parameter, the Y-axis rotation parameter, and the Z-axis rotation parameter respectively represent the rotation angle differences of the scanner position relative to the reference point in the X, Y, and Z-axis directions, the X, Y, and Z axes are perpendicular to each other, the Z axis is the vertical direction, and the Y axis faces the zero-degree direction of the laser scanner.

[0039] In the present invention, the multi-degree-of-freedom robotic arm is connected to a high-precision slide rail. According to the set path, it automatically adjusts and flexibly moves the laser scanner device around the object to be measured, so as to achieve a full-range scan of the object; the multi-degree-of-freedom robotic arm has high flexibility, high precision and strong adaptive ability. The multi-degree-of-freedom enables it to quickly and accurately complete a variety of complex tasks in a complex and changeable working environment; the multi-degree-of-freedom robotic arm endows the depth camera and the laser scanner mounted on the robotic arm head with a high degree of mobility, realizing precise movement and data acquisition at any position within the working space.

[0040] In the present invention, the depth camera has high-resolution and low-latency imaging performance, can stably collect information under different lighting conditions, pre-scan the object to be measured, efficiently obtain the rough positions of the scan points, and reduce the data collection time.

[0041] In the present invention, the high-precision slide rail is a slide rail with high precision, high rigidity and low friction coefficient, has excellent linear motion accuracy and stability, can maintain micron-level positioning accuracy during high-speed movement, and can accurately obtain position information; the high-precision slide rail not only ensures the smooth movement of the scanning device, but also provides extremely high positioning accuracy, can accurately obtain position information, enabling the laser scanner to maintain consistency and accuracy when collecting point cloud information.

[0042] In the present invention, the laser scanner has high-speed scanning ability, high resolution and excellent measurement accuracy, and can obtain accurate point cloud data in a short time.

[0043] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0044] (1) For the point cloud stitching method based on a high-precision coordinate measuring machine of the present invention, first, the depth camera pre-scans the object to be measured to obtain the position information of the occluders in the scene and the external contour of the object to be measured; according to the results of the pre-scan, a path planning algorithm is used to plan the scanning stations and scanning paths of the laser scanner, and determine the coordinate position parameters of each scanning station; the laser scanner collects point cloud data of multiple perspectives of the object to be measured according to the set scanning stations and scanning paths, and obtains point cloud data of multiple perspectives; since the present invention accurately determines three translation amounts and three rotation amounts based on the reference point of each scanning station during the path planning process, the transformation matrix corresponding to each scanning station is obtained accordingly; after the laser scanner obtains the point cloud data of each scanning station, the position transformation can be directly performed according to the transformation matrix, and the complete three-dimensional point cloud data can be obtained by one stitching. The method of the present invention does not require the steps of rough stitching and fine stitching, has simple steps, and is highly accurate and efficient.

[0045] (2) The point cloud stitching method based on a high-precision coordinate measuring machine of the present invention pre-scans the object to be measured through a depth camera to obtain the position information of the occluder in the scene and the outer contour of the object to be measured. Then, according to the results of the pre-scan, a path planning algorithm is used to plan the scanning stations and scanning paths of the laser scanner, determine the coordinate position parameters of each scanning station, and comprehensively consider factors such as scanning efficiency, coverage, and collision avoidance to optimize the scanning path. Through precise coordinate transformation and calibration, the accurate position of the scanned points in the three-dimensional space is obtained, providing a necessary prerequisite for high-precision scanning.

[0046] (3) The point cloud stitching device based on a high-precision coordinate measuring machine of the present invention uses a depth camera, a high-precision slide rail, a multi-degree-of-freedom robotic arm, and a laser scanner, effectively solving the problems that the frame-mounted laser scanner has incomplete point cloud data due to occlusion and inaccurate external parameters, large errors in multi-station point cloud stitching, difficulties in processing complex large-scale point cloud data, and it is difficult to meet the requirements for high-precision data in the detection link. The technical means is simple and easy to implement. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the composition of the point cloud stitching device based on a high-precision coordinate measuring machine according to an embodiment of the present invention.

[0048] Figure 2 It is a flowchart of the point cloud stitching method based on a high-precision coordinate measuring machine according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] The following will further describe the present invention in detail with reference to embodiments, but the embodiments of the present invention are not limited thereto.

[0050] Some embodiments of the present invention provide a point cloud stitching device. As Figure 1 shown, the point cloud stitching device includes a depth camera 4, a laser scanner 3, a multi-degree-of-freedom robotic arm 2, a high-precision slide rail 1, and a control device; the depth camera 4 and the laser scanner 3 are installed on the multi-degree-of-freedom robotic arm 2; the multi-degree-of-freedom robotic arm 2 is installed on the high-precision slide rail 1; the depth camera is used to pre-scan the object to be measured and store the scan data; the laser scanner is used to collect, store, and process the multi-viewpoint cloud data of the object to be measured; the control device (which can be a computer) is used to plan the path of the high-precision slide rail, set the movement trajectory of the multi-degree-of-freedom robotic arm, and send commands to the multi-degree-of-freedom robotic arm to control its movement.

[0051] In this embodiment, the high-precision slide rail is not only a slide rail with high precision, high rigidity and low friction coefficient, but also has excellent linear motion precision and stability, and can maintain micron-level positioning precision during high-speed movement; the multi-degree-of-freedom robotic arm can automatically adjust and flexibly move the laser scanner device around the object to be measured, giving the depth camera and laser scanner mounted on the head of the robotic arm a high degree of mobility, so as to achieve precise movement and data collection at any position within the working space.

[0052] Some other embodiments of the present invention provide a point cloud stitching method based on a high-precision coordinate measuring machine. As Figure 2 shown, the point cloud stitching method includes the following steps:

[0053] S1 Control the depth camera to pre-scan the object to be measured to obtain the position information of the occluder in the scene and the outer contour of the object to be measured;

[0054] S2 According to the results of the pre-scan, use a path planning algorithm to plan the scanning stations and scanning paths of the laser scanner, and determine the coordinate position parameters of each scanning station;

[0055] S3 Set the scanning stations and scanning paths according to the planning results of step S2, and control the laser scanner to collect point cloud data of multiple viewpoints of the object to be measured according to the set scanning stations and scanning paths, and obtain point cloud data of multiple viewpoints;

[0056] S4 According to the coordinate position parameters of each scanning station obtained in step S2, calculate the transformation matrix corresponding to each scanning station, and perform position transformation on each point cloud data obtained in step S3;

[0057] S5 Stitch the point cloud data of multiple viewpoints after position transformation in step S4 to obtain three-dimensional point cloud data, and remove duplicate points from the three-dimensional point cloud data.

[0058] The following takes a specific embodiment to make a detailed description of the point cloud stitching method based on a high-precision coordinate measuring machine of the embodiments of the present invention:

[0059] The point cloud stitching method based on a high-precision coordinate measuring machine includes the following steps:

[0060] S1 Control the depth camera to pre-scan the object to be measured to obtain the position of the occluder in the complex scene and the outer contour of the object to be measured, so as to perform path planning subsequently;

[0061] By using the depth camera to perform a preliminary comprehensive scan of the object to be measured, a preliminary outer contour of the object is established, aiming to roughly obtain the position information of possible scanning points; at this stage, basic data is provided for subsequent precise scanning and path planning.

[0062] S2 According to the results of the pre-scan, use a path planning algorithm to plan the scanning sites and scanning paths of the laser scanner, and determine the coordinate position parameters of each scanning site; in this embodiment, the specific implementation method is as follows:

[0063] S21 Perform data processing on the pre-scan results obtained in step S1, and convert the contour information and occlusion information of the measured object obtained by the pre-scan into the form of point clouds or polygons;

[0064] S22 Use the greedy coverage algorithm to adjust the distance between the laser scanner and the measured object to an appropriate value. Select the point that can cover the most un-scanned contours as the first site from the starting point, mark the covered area, and continue to select the next site that can cover the most un-scanned contours until the contours of the entire measured object are covered; for occlusions, preferentially select sites with less impact. When there is a large area of occlusion, first determine the peripheral auxiliary sites to obtain edge information and then plan the internal sites;

[0065] S23 Perform path planning. Set the sites as graph nodes, the connections as edges and weight them, find the shortest path sequence and check its feasibility in combination with the motion limitations of the robot. At the same time, design a dynamic adjustment mechanism. In case of an anomaly, use the RRT algorithm to re-plan the local path to avoid obstacles and reach the target site;

[0066] S24 Take all the finally determined sites as scanning sites, record their coordinate position parameters, and use them as the external parameters for point cloud stitching.

[0067] For the i-th (i = 1, 2, 3...) scanning site, its coordinate position parameters include the X-axis translation parameter Xi relative to the reference point, the Y-axis translation parameter Yi relative to the reference point, the Z-axis translation parameter Zi relative to the reference point, the X-axis rotation parameter ω x relative to the reference point, the Y-axis rotation parameter ω y relative to the reference point, the Z-axis rotation parameter ω z ;

[0068] The translation matrix, rotation matrix and transformation matrix of the i-th scanning site are shown in equations (1), (2), and (3);

[0069]

[0070]

[0071] where i = 1, 2, 3..., j = x, y, z.

[0072] S3 According to the planning results of step S2, set the scanning sites and scanning paths, and control the laser scanner to collect multi-viewpoint cloud data of the measured object according to the set scanning sites and scanning paths, so as to obtain multi-viewpoint cloud data;

[0073] S4 calculates the transformation matrix corresponding to each scanning site according to the coordinate position parameters of each scanning site obtained in step S2, and performs position transformation on each point cloud data obtained in step S3;

[0074] S5 splices the multi-viewpoint point cloud data after position transformation in step S4 to obtain three-dimensional point cloud data, and removes duplicate points from the three-dimensional point cloud data; In this embodiment, the implementation of removing duplicate points from the three-dimensional point cloud data is specifically as follows:

[0075] By dividing the data into grid cells, detecting and removing coordinate points smaller than a set threshold to eliminate duplicate points, and then performing data optimization and smoothing processing.

[0076] An embodiment of the present invention also provides an electronic device, including a processor and a memory;

[0077] The memory is used to store non-temporary computer instructions;

[0078] When the non-temporary computer instructions are executed by the processor, the processor executes the point cloud splicing method based on a high-precision coordinate measuring machine.

[0079] An embodiment of the present invention also provides a storage medium for storing non-temporary computer instructions, which, when run, execute the point cloud splicing method based on a high-precision coordinate measuring machine.

[0080] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited by the embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement methods and are all included in the protection scope of the present invention.

Claims

1. A point cloud stitching method based on a high-precision coordinate measuring machine, characterized in that: The following steps are involved: S1 controls the depth camera to pre-scan the object to be measured to obtain the position information of the obstructions in the scene and the outline of the object to be measured; S2 plans the scanning sites and scanning paths of the laser scanner using a path planning algorithm according to the results of the pre-scan, and determines the coordinate position parameters of each scanning site; S3 sets the scanning site and the scanning path according to the planning result of step S2, controls the laser scanner to collect multi-view point cloud data of the object to be measured according to the set scanning site and scanning path, and obtains multi-view point cloud data; S4 calculates the transformation matrix corresponding to each scanning station according to the coordinate position parameters of each scanning station obtained in step S2, and performs position transformation on each point cloud data obtained in step S3; S5 stitches the multi-view point cloud data after the position transformation in step S4 to obtain three-dimensional point cloud data, and removes duplicate points from the three-dimensional point cloud data.

2. The point cloud stitching method based on a high-precision coordinate measuring machine according to claim 1, characterized in that: According to the result of the pre-scan, the path planning algorithm is used to plan the scanning sites and scanning paths of the laser scanner, and the coordinate position parameters of each scanning site are determined, specifically: S21 performs data processing on the pre-scanning result obtained in step S1, and converts the contour information and the obstruction information of the object to be measured obtained in the pre-scanning into point cloud data; S22 uses a greedy coverage algorithm, which selects the point that can cover the most unscanned contours from the starting point as the first station, marks the covered area and continues to select the next station that can cover the most unscanned contours until the contour of the entire object under test is covered; S23 performs path planning, sets the stations as graph nodes, sets the connections as edges and weights them, finds the shortest path sequence and checks the feasibility in combination with the robot motion restrictions, and designs a dynamic adjustment mechanism. In case of anomalies, the RRT algorithm is used to replan the local path to bypass obstacles to the target station; S24 uses all the finally determined sites as scanning sites and records their coordinate position parameters as external parameters for point cloud stitching.

3. The point cloud stitching method based on a high-precision coordinate measuring machine according to claim 2, characterized in that: The step 24 of finding the shortest path sequence specifically includes: using the A* or Dijkstra algorithm to find the shortest path sequence.

4. The point cloud stitching method based on a high-precision coordinate measuring machine according to claim 1, characterized in that: For the i-th scanning station, its coordinate position parameters include the X-axis translation parameter Xi of the scanning station relative to the reference point, the Y-axis translation parameter Yi relative to the reference point, the Z-axis translation parameter Zi relative to the reference point, and the X-axis rotation parameter ω relative to the reference point. x , Y-axis rotation parameter ω relative to the reference point y , Z-axis rotation parameter ω relative to the reference point z ; Where i = 1, 2, 3..., represents the number of scanning sites.

5. The point cloud stitching method based on a high-precision coordinate measuring machine according to claim 4, characterized in that: In step S4, the translation matrix, rotation matrix and transformation matrix of the i-th scanning station are shown in equations (1), (2) and (3); Among them, i=1, 2, 3…, j=x, y, z.

6. The point cloud stitching method based on a high-precision coordinate measuring machine according to claim 5, characterized in that: The step S5 of removing duplicate points from the three-dimensional point cloud data is specifically as follows: By dividing the data into grid cells, the coordinate points with a value less than a set threshold are detected and removed to eliminate duplicate points, followed by data optimization and smoothing.

7. A point cloud stitching device for implementing the point cloud stitching method based on a high-precision coordinate measuring machine as described in any one of claims 1 to 6, characterized in that: It includes a depth camera, a laser scanner, a multi-degree-of-freedom robotic arm, a high-precision slide rail, and a control device; the depth camera and the laser scanner are installed on the multi-degree-of-freedom robotic arm; the multi-degree-of-freedom robotic arm is installed on the slide rail; The depth camera is used to pre-scan the object to be measured and store the scan data; The laser scanner is used to collect, store and process multi-view point cloud data of the object being measured; The control device is used to plan a high-precision slide rail path, set a motion trajectory of a multi-degree-of-freedom robotic arm, and send instructions to the multi-degree-of-freedom robotic arm to control its motion.

8. The point cloud stitching device according to claim 7, characterized in that: The positioning accuracy of the high-precision slide rail is at the micron level.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store non-temporary computer instructions; When the non-temporary computer instruction is executed by the processor, the processor implements the point cloud stitching method based on a high-precision coordinate measuring machine as described in any one of claims 1 to 6.

10. A storage medium, characterized in that: Used to store non-temporary computer instructions, when the non-temporary computer instructions are executed, the point cloud stitching method based on a high-precision coordinate measuring machine according to any one of claims 1 to 6 is executed.

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