Empirical compensation method for visual positioning system

Abstract

The invention discloses an empirical compensation method for a visual positioning system, and the method comprises the steps: continuously recording N groups of deviation data in the pasting operation of pasting elements on the same workpiece, carrying out the statistical analysis of the N groups of deviation data, and creating a corresponding compensation amount or compensation function according to the statistical analysis result; and when the (N + 1) mounting elements are subsequently laminated, the position deviation between the mounting elements in the workpiece and the mounting head is not detected through the visual positioning unit any more, and alignment compensation is directly carried out according to the compensation amount or the compensation function. Accurate positioning is achieved through visual alignment, and then intelligent compensation is achieved through data statistical analysis. The long-standing contradiction between precision and efficiency in the industry is solved, and the output rate of equipment is greatly improved.

Description

Technical Field

[0001] This invention relates to the field of machine vision and precision automation equipment technology, and specifically to an experience compensation method for a visual positioning system. Background Technology

[0002] In high-end equipment manufacturing fields such as semiconductor packaging and high-precision mounting, vision alignment is a key step in achieving precision machining and assembly. For example, the mainstream method for semiconductor bonding alignment is to use a fixed camera to photograph the marked points on the workpiece (i.e., the Mark on the wafer or chip), use pattern recognition (PR) algorithms to calculate its center coordinates, and compare them with the target position to guide the mechanical platform to perform compensation alignment.

[0003] The above method has an inherent "edge effect" problem: due to the image distortion that is common in optical lenses, when the marker point is located in the edge area of ​​the camera's field of view, its image will be distorted, resulting in a large error between the center coordinates calculated by the PR algorithm and the actual coordinates, which seriously restricts the overall alignment accuracy of the device.

[0004] To circumvent the aforementioned problems, existing solutions either employ expensive telecentric lenses or establish complex global distortion correction models. The former is costly, while the latter is computationally complex and time-consuming to calibrate. Through continuous research and improvement, the inventors have proposed a visual positioning system based on ROI (Region of Interest) dynamic alignment. This system dynamically moves the observation area of ​​the visual system, ensuring that the marker point is always identified within the low-distortion region at the center of the camera's field of view, fundamentally eliminating errors caused by the "edge effect." It achieves high accuracy while also offering advantages in efficiency and low cost. However, this method also has the following problems:

[0005] 1. Point-by-point addressing is inefficient. For example, in wafer bonding, a wafer containing a large number of dies; or in chip mounting, a substrate with a large number of chip units evenly distributed. If each die and chip unit uses a dynamic alignment vision positioning system, alignment requires a complete process of image acquisition, recognition, difference calculation, and mechanical compensation, which will severely limit the equipment's output rate (UPH, Units Per Hour).

[0006] 2. Insufficient utilization of deviation patterns: In actual production processes, due to factors such as mechanical guide rail wear, thermal deformation, and material stress release, the positional deviation between the mounting head and the die / chip unit often exhibits a certain trend (e.g., linear drift) or statistical regularity (e.g., fluctuation around a certain mean). Existing vision positioning systems only compensate for the current deviation in real time, without systematically collecting and analyzing historical deviation data, thus failing to achieve predictive compensation, leading to repetitive calculations and redundant movements.

[0007] Therefore, the inventors have further optimized the existing visual positioning system and proposed the following technical solution. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide an empirical compensation method for visual positioning systems.

[0009] To solve the above-mentioned technical problems, the positioning method in this invention adopts the following technical solution: an empirical compensation method for a visual positioning system, wherein the visual positioning system comprises: a workpiece with uniformly distributed mounting elements positioned on a carrier platform; the carrier platform being driven by a driving mechanism to be positioned relative to the field of view of the visual positioning unit; the visual positioning unit detecting the positional deviation between the mounting elements and the mounting head in the workpiece; and determining whether to perform alignment compensation based on the deviation data compared with a preset threshold, thereby achieving alignment between the mounting elements and the mounting head; the empirical compensation method comprises: during the mounting operation of mounting elements on the same workpiece, continuously recording N sets of deviation data; statistically analyzing the N sets of deviation data; and creating a corresponding compensation amount or compensation function based on the statistical analysis results; subsequently, when mounting N+1 mounting elements, the positional deviation between the mounting elements and the mounting head in the workpiece is no longer detected by the visual positioning unit, but alignment compensation is directly performed based on the aforementioned compensation amount or compensation function.

[0010] Furthermore, in the above technical solution, N is greater than 10, for example, N equals 20.

[0011] Furthermore, in the above technical solution, the step of creating a corresponding compensation amount or compensation function based on the statistical analysis results includes the following modes: Mean mode: Calculate the mean, range, and standard deviation of the recorded N sets of deviation data. If the range and standard deviation are both within preset values, it is determined that the data fluctuates around a stable mean; Linear mode: Perform linear trend analysis on the recorded N sets of deviation data in sequence. If it conforms to the linear trend characteristics, it is determined that the data has a linear trend.

[0012] Furthermore, in the above technical solution, statistical analysis is performed on N sets of deviation data. If a corresponding compensation amount or compensation function cannot be created, then when performing bonding operations on N+1 mounting components, the positional deviation between the mounting components and the mounting head in the workpiece is detected by the vision positioning unit, and the alignment compensation is determined based on the comparison result of the deviation data and the preset threshold.

[0013] Furthermore, in the above technical solution, when replacing workpieces of different specifications or batches after completing the mounting of workpieces of the same specification or batch, the aforementioned empirical compensation method is repeated to create a new compensation amount or compensation function.

[0014] Furthermore, in the above technical solution, the positioning method of the visual positioning system of the present invention includes the following steps: S1: The control unit controls the drive mechanism to move the bearing platform with the workpiece positioned to a preset starting position according to the original preset parameters. The starting position is located within the field of view of the visual positioning unit; S2: The camera in the visual positioning unit takes an image of the workpiece within the field of view and determines whether there are any marking points on the workpiece within the observation area of ​​the image taken by the camera; if the determination result is "no", then proceed to step S3; if the determination result is "yes", then proceed to step S4; S3: The control unit controls the drive mechanism to perform path traversal movement around the starting position in a local area, taking the starting position as the starting point. During the movement, the camera continuously captures images to locate marker points. If a marker point is found in the local area, proceed to step S4. If no marker point is found in the local area, the positioning is deemed a failure, and the preset starting position is reset. S4: Determine the coordinate difference between the center of the marker point and the center of the field of view captured by the camera, and determine whether the difference is less than a set threshold. If the difference is greater than or equal to the set threshold, proceed to step S5. If the difference is less than the set threshold, proceed to step S6. S5: If the difference is greater than or equal to the set threshold, the control unit controls the drive mechanism to mechanically compensate for the difference, making the difference less than the set threshold. S6: If the difference is less than the set threshold, the positioning is completed.

[0015] Furthermore, in the positioning method of the above-mentioned visual positioning system, in step S3, the path traversal movement adopts a "U" shaped path, and the preset local area is a rectangular area with the starting point as the center and a side length of 3-10mm.

[0016] Furthermore, in the positioning method of the aforementioned visual positioning system, the image observation area refers to the low-distortion area located in the center of the camera's field of view.

[0017] Furthermore, in the positioning method of the above-mentioned visual positioning system, after step S5 is completed, the system returns to step 4 to recalculate the coordinate difference between the center of the marker point and the center of the field of view captured by the camera, and then makes a judgment again.

[0018] This invention proposes an integrated alignment compensation method for visual alignment solutions. Its core principle is to first achieve precise positioning through visual alignment, and then achieve intelligent compensation through data statistical analysis. Specifically, this invention first acquires real offset data through a visual positioning system with dynamic ROI alignment. Then, using this offset data as samples, statistical analysis is performed on continuously acquired samples, employing methods such as trend fitting and pattern recognition to establish a deviation compensation model under the current process conditions. In subsequent alignment, the system can prioritize calling this model for predictive compensation, only initiating the complete ROI positioning process when the model's confidence level is insufficient or an anomaly occurs. This solves the long-standing industry contradiction between accuracy and efficiency, significantly improving equipment output. Attached Figure Description

[0019] Figure 1 This is an overall flowchart of the visual positioning method used in this invention;

[0020] Figure 2 This is a schematic diagram of the "U"-shaped point-finding path in the visual positioning method used in this invention;

[0021] Figure 3 This is a schematic diagram of a workpiece that needs to be mounted in this invention;

[0022] Figure 4 This is a schematic diagram of the mean fluctuation change of the group data collected in this invention;

[0023] Figure 5 This is a schematic diagram showing the linear change of the group data collected in this invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0025] The visual positioning method of this invention is applicable to various workpieces such as wafers, dies, and chips, enabling high-precision mounting in high-end equipment manufacturing fields, including wafer bonding, die packaging, and chip bonding. For simplicity, the following description uses chip alignment and positioning in semiconductor chip mounting processes as an example, but the application of this invention is not limited to this.

[0026] Since this invention addresses experience compensation for visual positioning systems, a better explanation of the experience compensation scheme requires an understanding of the technical solution of the visual positioning system. Therefore, the visual positioning system based on ROI dynamic alignment used in this invention will be described first.

[0027] Taking chip alignment in the chip mounting process as an example, see Figure 3 As shown, a number of chips (mounting elements) 10 are evenly distributed in an array on the substrate (workpiece) 1. In the subsequent mounting process, each chip unit 10 needs to be aligned with the mounting head for mounting in sequence, and then the mounting is completed by the mounting head.

[0028] The visual positioning system based on ROI dynamic alignment used in this invention includes:

[0029] The carrier platform is used to carry and position the chip to be mounted.

[0030] A visual positioning unit, comprising at least one camera, is used for image capture during visual alignment. Typically, the camera is positioned above the support platform and captures images of the substrate 1 below for visual positioning.

[0031] A drive mechanism, connected to the support platform, is used to drive the support platform to move within a plane. Typically, the drive mechanism can employ a high-precision XY linear motor platform to achieve free movement within the plane.

[0032] The control unit is electrically connected to the drive mechanism. The control software running in the control unit executes the positioning system to perform positioning operations according to a preset process.

[0033] like Figure 1 As shown, based on the above-described visual positioning system, the implementation flow of the visual positioning method of the present invention is as follows:

[0034] S1: Starting Position

[0035] The control unit, based on preset parameters, controls the drive mechanism to move the support platform with the substrate positioned thereon to a preset starting position, which is within the field of view of the visual positioning unit. The coordinates of this starting position are within the field of view of the corresponding camera in the visual positioning unit.

[0036] S2: ROI Pre-positioning

[0037] The camera in the visual positioning unit takes pictures of the chip within its field of view and determines whether there are any marker points on the chip within the observation area of ​​the image taken by the camera. These marker points exist on the chip surface and are used as a base point for alignment. They can usually be "marks" in chip manufacturing, such as bumps or other marks that can be used for alignment.

[0038] Since there are distortion areas (the edge areas of the image) in the images captured by the camera, in order to avoid calculation errors caused by edge distortion, the present invention will select a low-distortion area at the center position of the camera's field of view as the image observation area according to the parameters of the camera itself. The specific size of this image observation area can be determined in combination with the parameters of the camera itself, the focal length of the shot, etc. Compared with the existing fixed-field-of-view recognition, the present invention only judges whether there are marked points on the chip in the "image observation area" to reduce the calculation error caused by the edge distortion of the lens. That is, this image observation area is the best recognition area for the marked points.

[0039] If no marked points on the chip are observed within the image observation area, the judgment result is "no", and then step S3 is entered; if marked points on the chip are observed within the image observation area, the judgment result is "yes", and then step S4 is entered.

[0040] S3: Dynamic point search

[0041] The control unit controls the driving mechanism to perform path traversal movement within a local area starting from the starting position, and continuously captures images through the camera during the movement to search for marked points.

[0042] Specifically, if no marked points on the chip are observed within the image observation area, it means that the marked points do not fall within this "image observation area". At this time, it is necessary to search for the position of the marked points. During the search, only a local area is searched. The size of this local area cannot be too small to avoid unnecessary errors caused by minor differences. Of course, if the size of the local area is too large, it will lead to a decrease in the point search efficiency. At this time, an error should be reported directly, indicating that there is a large deviation in the preset starting position and it needs to be reset. Usually, the preset local area is a rectangular area with a side length of 3-10 mm centered on the starting point position. Preferably, the preset local area is a 5 mm × 5 mm square area.

[0043] In addition, in order to ensure that all positions in the local area are queried, the present invention adopts a "return" - shaped path traversal movement method. As shown in Figure 2 Taking the preset local area as a 5 mm × 5 mm square area as an example, the control unit controls the driving mechanism to move step by step along a "return" - shaped path from the inside to the outside centered on the starting point position. Every time it moves a step length (for example, 0.5 mm), the camera takes an image and judges whether there are marked points within the image observation area. Once a marked point is found at a certain position, the traversal movement is stopped and step S4 is entered.

[0044] Of course, it is also possible that after traversing all the preset local areas, no marker point is found on the chip. In this case, the positioning is determined to be a failure and an error is reported directly, indicating that there is a large deviation in the preset starting position. The preset starting position needs to be reset.

[0045] S4: Deviation Judgment

[0046] In step S2 or S3, the marker point on the chip is observed within the image observation area. At this time, it is necessary to determine the coordinate difference between the center of the marker point and the center of the field of view captured by the camera, and to determine whether the difference is less than a set threshold.

[0047] For example, the center coordinates of the found marker point are (X0, Y0), and the coordinates of the center of the field of view are (X1, Y1). The difference between the two coordinates (Δx, Δy) is calculated. It is then determined whether (Δx, Δy) is within an allowed threshold range. If the difference (Δx, Δy) is greater than or equal to a set threshold (e.g., a certain number of pixel values), then proceed to step S5. If the difference (Δx, Δy) is less than the set threshold, then proceed to step S6.

[0048] S5: Mechanical Compensation

[0049] If the difference (Δx, Δy) is greater than or equal to the set threshold, the control unit controls the drive mechanism to mechanically compensate for the difference, so that the difference is less than the set threshold.

[0050] To ensure the accuracy of mechanical compensation, after compensation is completed, it is necessary to return to step 4, recalculate the coordinate difference between the center of the marker point and the center of the field of view captured by the camera, and make another judgment to ensure the accuracy of the compensation.

[0051] S6: If the difference is less than the set threshold, positioning is complete. At this time, the center coordinates of the current marker point coincide with the coordinates of the field center, the wafer is aligned, and positioning before bonding is achieved.

[0052] The visual positioning unit includes at least one of a camera located above the support platform and a camera located below the support platform.

[0053] It should be noted that the above embodiments use a mobile support platform (workpiece) as an example. Following the principles of this invention, a mobile camera or a camera and platform moving in tandem can also be used to achieve the "relative motion between the camera and the marker point".

[0054] The above is a description of the visual positioning scheme based on ROI dynamic alignment used in this invention. Based on this, and further... Figure 3As shown, with this visual positioning scheme, since there are many chips 10 evenly distributed on the substrate 1, the chip mounting process requires visual alignment of each chip 10 individually. This necessitates image acquisition, recognition, difference calculation, and mechanical compensation for each visual positioning operation, severely limiting equipment output.

[0055] In actual production, due to factors such as wear of mechanical guide rails, thermal deformation, and material stress release, the positional deviation between the chip unit and the mounting head often exhibits a certain trend (such as linear drift) or statistical regularity (such as fluctuation around a certain mean). Therefore, if the positional deviation is systematically analyzed, it can be effectively predicted and directly compensated, thus avoiding repetitive calculations and redundant movements, and improving the output rate of the equipment. The empirical compensation method described in this invention is based on this situation.

[0056] In simple terms, the empirical compensation method described in this invention is as follows: during the bonding operation of chips (mounting components) on the same substrate (workpiece), N sets of deviation data are continuously recorded, statistical analysis is performed on the N sets of deviation data, and a corresponding compensation amount or compensation function is created based on the statistical analysis results; when bonding N+1 mounting components in a subsequent operation, the positional deviation between the chip components in the workpiece and the mounting head is no longer detected by the visual positioning unit, but alignment compensation is directly performed based on the compensation amount or compensation function.

[0057] The following section provides a detailed explanation of this experience compensation method, based on the visual positioning process described above.

[0058] During step S4, the difference (Δx, Δy) is recorded each time, which is the alignment deviation data between the current chip being mounted and the mounting head. After continuously recording N sets of data, statistical analysis is performed on the N sets of deviation data. To ensure the validity and confidence of the data, the value of N should not be too small and should be greater than 10. In this embodiment, N is equal to 20.

[0059] After recording N sets of deviation data, perform statistical analysis on them, and create corresponding compensation amounts or compensation functions based on the statistical analysis results.

[0060] Typically, when performing chip mounting operations on the same (or identical specifications and batch) substrate, the initial visual alignment process corrects any errors in the equipment itself (e.g., positioning failure in step S2, requiring parameter resetting). Deviations in subsequent mounting operations are often caused by factors such as mechanical guide wear and thermal deformation. These deviations often exhibit certain patterns, allowing for the creation of corresponding compensation amounts or functions based on statistical analysis. Generally, the following modes are included:

[0061] I. Mean Pattern: Combining Figure 4 As shown, the mean, range, and standard deviation of the N sets of recorded deviation data are calculated. If the range and standard deviation are both within preset values, the data set is determined to fluctuate around a stable mean. This pattern is generally due to errors caused by equipment precision issues; the deviation data will fluctuate within a certain range due to precision limitations.

[0062] For example, in the X direction, the standard deviation is 0.3 μm and the range is 1.5 μm, both within the equipment's accuracy control limits, so it is determined to be a mean-based model. The calculated mean is +1.1 μm. Similarly, analyzing the data in the Y direction, the calculated mean is -0.7 μm. Therefore, the mean-compensated model is generated: Comp_x = +1.1 μm, Comp_y = -0.7 μm.

[0063] Starting with the (N+1)th chip, the compensation method will directly adopt this average mode. The placement head moves directly to the position after (+1.1μm, -0.7μm) compensation, skipping the aforementioned ROI positioning step, and greatly shortening the placement time.

[0064] II. Linear Pattern: A linear trend analysis is performed on the N sets of recorded deviation data sequentially. If the data exhibits a linear trend, it is determined that the data set shows a linear change trend. This situation typically occurs due to thermal expansion caused by a continuous rise in temperature. The deviation data begins to show a linear increase.

[0065] For example, combining Figure 5 As shown, linear analysis was performed on the obtained 20 compensation values ​​to obtain proportional compensation data for x1k and y1k. Starting from the 21st chip, this linear mode will be directly adopted for compensation. Compensation will be performed using nx1k and ny1k respectively based on the number of bonding cycles (n).

[0066] III. Periodic Pattern: The recorded N sets of deviation data exhibit periodic changes, for example, roughly in the form of a sine wave. Similarly, a corresponding compensation function is established based on the oscillation frequency and amplitude of the sine wave. Starting from the (N+1)th chip, the compensation method will directly adopt this sine wave change pattern.

[0067] Of course, it's also possible that after statistical analysis of N sets of deviation data, these N sets of data exhibit random, unpredictable, and irregular changes, making it impossible to create a corresponding compensation amount or function. In this case, starting from the (N+1)th chip, the compensation method follows the aforementioned ROI positioning steps, and the deviation data of the (N+1)th chip is recorded until N consecutive sets of deviation data are found that can create a corresponding compensation amount or function. This situation typically occurs in the early stages of equipment operation due to mechanical break-in, unstable working environment, and inherent randomness of the equipment.

[0068] When a workpiece of the same specification or batch is mounted, and then a workpiece of a different specification or batch is replaced, the previously established compensation amount or compensation function will no longer be suitable. It is necessary to re-perform the empirical compensation method and create a new compensation amount or compensation function.

[0069] Furthermore, to prevent significant deviations in the experience-based compensation mode due to prolonged operation or sudden changes, periodic recalibration and updates can be performed during the placement process. For example, in experience-based compensation mode, a visual positioning verification is forced after every M placement units are completed, and a new compensation amount or function is created again based on the experience-based compensation method.

[0070] In summary, this invention ensures the accuracy of single-point alignment through ROI positioning. By statistically analyzing deviation data, it identifies common equipment deviation patterns such as mean drift and linear trends, and establishes a predictive model. Through empirical compensation, it improves the efficiency of multi-point alignment, resolving the long-standing industry contradiction between accuracy and efficiency. Furthermore, the empirical compensation employed in this invention does not introduce additional data or parameters, avoiding the risk of secondary errors caused by new data or parameters, and ensuring the stability of the entire system.

[0071] Of course, the above description is only a specific embodiment of the present invention and is not intended to limit the scope of the present invention. All equivalent changes or modifications made to the structure, features and principles described in the claims of the present invention should be included in the scope of the claims of the present invention.

Claims

1. An empirical compensation method for a vision positioning system, the vision positioning system comprising: a workpiece with uniformly distributed mounting elements positioned on a carrier platform; the carrier platform being driven to move relative to the field of view of a vision positioning unit via a drive mechanism; the vision positioning unit detecting the positional deviation between the mounting elements and the mounting head in the workpiece; and determining whether to perform alignment compensation based on a comparison of the deviation data with a preset threshold, thereby achieving alignment between the mounting elements and the mounting head; characterized in that... The empirical compensation method is as follows: during the bonding operation of mounting components on the same workpiece, N sets of deviation data are continuously recorded, statistical analysis is performed on the N sets of deviation data, and a corresponding compensation amount or compensation function is created based on the statistical analysis results; when bonding N+1 mounting components in the subsequent operation, the positional deviation between the mounting components and the mounting head in the workpiece is no longer detected by the visual positioning unit, but alignment compensation is directly performed based on the compensation amount or compensation function.

2. The empirical compensation method for a visual positioning system according to claim 1, characterized in that: The N is greater than 10.

3. The empirical compensation method for a visual positioning system according to claim 1, characterized in that: The value of N is 20.

4. The empirical compensation method for a visual positioning system according to claim 1, characterized in that: The creation of corresponding compensation amounts or compensation functions based on statistical analysis results includes the following modes: Mean mode: Calculate the mean, range, and standard deviation of the N sets of deviation data recorded. If the range and standard deviation are both within the preset values, it is determined that the data fluctuates around a stable mean. Linear mode: Perform linear trend analysis on the N sets of deviation data recorded in sequence. If the data meets the linear trend characteristics, the data set is determined to have a linear trend.

5. The empirical compensation method for a visual positioning system according to claim 1, characterized in that: If the corresponding compensation amount or compensation function cannot be created after statistical analysis of the N sets of deviation data, then when performing bonding operations on the N+1 mounting components, the positional deviation between the mounting components and the mounting head in the workpiece will continue to be detected by the vision positioning unit, and the alignment compensation will be determined based on the comparison result of the deviation data and the preset threshold.

6. The empirical compensation method for a visual positioning system according to claim 1, characterized in that: After completing the mounting of workpieces of the same specification or batch, when changing to workpieces of different specifications or batches, the aforementioned empirical compensation method is repeated to create a new compensation amount or compensation function.

7. An empirical compensation method for a visual positioning system according to any one of claims 1-6, characterized in that: The positioning method of the visual positioning system includes the following steps: S1: The control unit controls the drive mechanism to move the carrying platform with the workpiece positioned to a preset starting position according to the original preset parameters. The starting position is within the field of view of the vision positioning unit. S2: The camera in the visual positioning unit takes images of the workpiece within its field of view and determines whether there are any markers on the workpiece within the observation area of ​​the image taken by the camera. If the judgment result is "no", then proceed to step S3. If the judgment result is "yes", then proceed to step S4; S3: The control unit controls the drive mechanism to move along a path within a local area around the starting position, starting from the starting position, and continuously captures images with a camera during the movement to find marker points; If a marker point is found within the local area, proceed to step S4. If no marker point is found in the local area, the positioning is deemed to have failed, and the preset starting position is reset. S4: Determine the coordinate difference between the center of the marker point and the center of the field of view captured by the camera, and determine whether the difference is less than a set threshold. If the difference is greater than or equal to the set threshold, proceed to step S5. If the difference is less than the set threshold, proceed to step S6; S5: If the difference is greater than or equal to the set threshold, the control unit controls the drive mechanism to mechanically compensate for the difference, so that the difference is less than the set threshold. S6: If the difference is less than the set threshold, the positioning is completed.

8. The empirical compensation method for a visual positioning system according to claim 7, characterized in that: In step S3, the path traversal movement adopts a "U" shaped path, and the preset local area is a rectangular area with the starting point as the center and a side length of 3-10mm.

9. The empirical compensation method for a visual positioning system according to claim 7, characterized in that: The image observation area refers to the low-distortion region located in the center of the camera's field of view.

10. The empirical compensation method for a visual positioning system according to claim 7, characterized in that: After step S5 is completed, return to step 4 to recalculate the coordinate difference between the center of the marker point and the center of the field of view captured by the camera, and make a judgment again.

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