Position sensitive detection spot position weighted segment fitting laser precision positioning method

Abstract

The application discloses a kind of position sensitive detection light spot position weighted segmented fitting laser precision positioning methods.Collect the measurement output value of position sensitive detector under multiple calibration positions and corresponding real light spot position as calibration data, and then carry out polynomial fitting to obtain initial correction model and calculate the fitting residual at each calibration position;The detection range of position sensitive detector is used as initial segmented interval, and the initial segmented interval is divided into several continuous subintervals according to the fitting residual of calibration position;The calibration data in each subinterval is fitted to obtain the corresponding correction model;The current light spot position is obtained according to the correction model of each subinterval and combined with the measurement output value of position sensitive detector.The application effectively suppresses the positioning error of position sensitive detector caused by stray light interference and working distance change, significantly improves the light spot positioning accuracy and engineering applicability under complex optical environment and large range distance change conditions.

Description

Technical Field

[0001] This invention relates to the field of spot positioning technology, and in particular to a laser precision positioning method for position-sensitive detection spot position weighted segment fitting. Background Technology

[0002] In the field of high-precision optoelectronic detection, position-sensitive detectors are widely used in systems such as laser tracking, pose measurement, and optical alignment due to their continuous response, high resolution, and fast response capabilities. Position-sensitive detectors calculate the center position of a light spot by outputting current; ideally, their output is linearly related to the spot offset. However, in practical applications, due to factors such as device inhomogeneity and stray light interference, especially in environments with strong background light or multiple reflections, stray light can superimpose on the measurement spot, introducing additional nonlinear errors and severely reducing the spot positioning accuracy. This problem is particularly pronounced at different working distances: in systems with a fixed working distance, the stray light distribution is relatively stable, but nonlinear errors may still be concentrated in specific spot areas; while in scenarios with variable working distances (such as laser tracking or large-scale pose measurement), as the working distance changes, the spot size, energy distribution, and stray light coupling strength all change, causing the nonlinear characteristics of the position-sensitive detector to change significantly with distance, further exacerbating the difficulty of error compensation.

[0003] Currently, commonly used error compensation methods mainly include polynomial fitting and neural network modeling. Although global fitting methods are computationally simple, they are difficult to achieve high-precision compensation in regions with severe nonlinearity. In addition, existing piecewise fitting methods mostly use equal-interval division or rely on manual experience to set segmentation points, which neither considers the distribution characteristics of errors in the position or distance dimension, nor does it have the ability to adapt to the nonlinear evolution law under varying working distances. Although intelligent methods such as neural networks have strong nonlinear fitting capabilities, they rely on a large amount of training data and have high computational complexity, which is not conducive to achieving real-time high-precision compensation in embedded systems. Summary of the Invention

[0004] To address the local nonlinear distortion caused by stray light, a method for correcting spot positioning errors is needed that is applicable to both fixed and variable working distance scenarios. This method should achieve high-precision compensation while maintaining computational efficiency, thereby improving the positioning accuracy and system robustness of position-sensitive detectors in complex optical environments. Therefore, this invention provides a laser precision positioning method using weighted piecewise fitting of the spot position of a position-sensitive detector.

[0005] The technical solution adopted in this invention is: The method of the present invention includes the following steps: S1. Collect the measurement output values ​​of the position-sensitive detector at multiple calibration positions and the corresponding real spot positions as calibration data; S2. Perform polynomial fitting on the calibration data to obtain the initial correction model, and calculate the fitting residual of the initial correction model at each calibration position; S3. Using the detection range of the position-sensitive detector as the initial segmentation interval, the initial segmentation interval is divided into several continuous sub-intervals based on the fitting residual of the calibration position. S4. Perform polynomial fitting on the calibration data in each sub-interval to obtain the correction model corresponding to each sub-interval, and use the correction models of all sub-intervals as the final correction model. S5. The current spot position is obtained by processing the final corrected model and the current measurement output value of the position-sensitive detector.

[0006] The detection range of the position-sensitive detector specifically refers to the interval on the photosensitive surface of the position-sensitive detector that can respond to the light spot.

[0007] Step S3 specifically involves: S3.1. Using the detection range of the position-sensitive detector as the initial segmented interval, the root mean square error of the initial segmented interval is obtained by processing the fitting residual of the initial correction model at each calibration position. S3.2. Judge the root mean square error of all intervals: If the root mean square error of all intervals does not exceed the preset threshold, or the current total number of intervals reaches the preset number of intervals, or none of the intervals are intervals to be segmented, then the segmentation is completed and all the current intervals are taken as the segmentation result; otherwise, the interval with the largest root mean square error and the number of calibration positions in the interval reaches the preset number of calibration positions is taken as the interval to be segmented. S3.3. Take the measurement output value corresponding to the largest absolute value of the fitting residual in each interval to be segmented and the median of all measurement output values ​​in the current interval to be segmented, and perform a weighted summation to determine the segmentation point of each interval to be segmented. Then, divide each interval to be segmented into two intervals according to the segmentation point of each interval to be segmented. S3.4 Perform polynomial fitting on the calibration data in the intervals to be segmented, and then process it to obtain the fitting residuals at each calibration position in the current interval and the root mean square error of the current interval, and then return to step S3.2.

[0008] In step S3.3, the segmentation points of each interval to be segmented are determined according to the following formula: In the formula, This indicates the position of the segmentation point of the current interval to be segmented, and k represents the weight parameter, k∈(0,1). This indicates the measurement location where the absolute value of the fitted residual is the largest within the current interval to be segmented. This represents the median of all measurement locations within the current interval to be segmented. This indicates the value of the independent variable corresponding to the maximum value. Represents absolute value. This represents the polynomial fitting function within the current interval. This indicates that the median of all data in the set is taken. This represents the detector output position value at the i-th calibration point. This represents the true value location at the i-th calibration point. It represents the set of all measurement locations within the current interval to be segmented.

[0009] The specific method of performing polynomial fitting on the calibration data involves using the measurement output value of the position-sensitive detector as the input variable and the actual spot position as the output variable to construct a polynomial fitting function.

[0010] The method is applied to laser tracking measurement, which includes both fixed working distance and variable working distance application scenarios. When the application scenario is a fixed working distance application scenario, execute steps S1-S5 to obtain the spot position; When the application scenario is a variable working distance application scenario, the working distance is divided into several distance sub-intervals. Steps S1-S4 are performed on each distance sub-interval to obtain the final correction model of each distance sub-interval. Based on the working distance obtained in real time during measurement, the final correction model of the corresponding distance sub-interval is selected and step S5 is performed to obtain the spot position.

[0011] The method employs a laser precision positioning system, including a laser unit, a beam splitter, a lens group, a target sphere, a filter, a focusing lens, and a position-sensitive detector; The laser unit emits a measurement beam, which is then emitted after passing through a beam splitter and lens group to a target sphere fixed on the object to be measured. The measurement beam is reflected by the target sphere and returns parallel to the incident light. It then passes through the lens group back to the beam splitter. After being reflected by the beam splitter, the measurement beam passes through a filter and a focusing lens in sequence before entering the position-sensitive detector.

[0012] When the application scenario is a variable working distance application scenario, the laser precision positioning system also includes a ranging unit, which is used to acquire the working distance in real time.

[0013] The beneficial effects of this invention are: 1) This invention takes into account both local nonlinear correction at fixed distances and global adaptive modeling at variable distances, which significantly improves the positioning accuracy, robustness and applicability of position-sensitive detectors in complex optical environments.

[0014] 2) This invention adopts a segmentation mechanism based on the location of the maximum residual point and the median of the data, which takes into account both the response sensitivity of the high error region and the geometric balance of the data distribution. It avoids the problem of insufficient identification of nonlinear features by traditional equal-interval or empirical segmentation methods, and effectively prevents overfitting caused by local outliers, thereby improving the robustness and generalization ability of the error correction model.

[0015] 3) The weighted piecewise polynomial fitting model constructed in this invention has a simple structure and high computational efficiency. It only requires conventional calibration data to complete the modeling and can achieve real-time compensation in resource-constrained embedded systems. It has good engineering practicality and environmental adaptability.

[0016] This invention is applicable to high-precision photoelectric detection systems in fields such as laser tracking, optical alignment, pose measurement, and precision motion control. It is particularly effective in complex application scenarios with stray light interference and varying working distances, achieving stable, reliable, and high-precision spot position calculation. This invention effectively suppresses positioning errors in position-sensitive detectors caused by stray light interference and variations in working distance, significantly improving spot positioning accuracy and engineering applicability in complex optical environments and under conditions of wide-range distance variation. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the position-sensitive detection spot position weighting segmentation in this embodiment.

[0018] Figure 2 This is a schematic diagram of the laser precision positioning system in this embodiment.

[0019] Figure 3 This is a flowchart of the weighted piecewise polynomial fitting method in this embodiment.

[0020] Figure 4 This is a schematic diagram comparing the spot position fitting before and after in this embodiment.

[0021] In the diagram, 1 is the laser unit, 2 is the beam splitter, 3 is the lens group, 4 is the target sphere, 5 is the filter, 6 is the focusing lens, and 7 is the position-sensitive detector. Detailed Implementation

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the scope of protection of this invention.

[0024] like Figure 2 The embodiment shown employs a laser precision positioning system, including a laser unit 1, a beam splitter 2, a lens group 3, a target sphere 4, a filter 5, a focusing lens 6, and a position-sensitive detector 7. The laser unit 1 emits a measurement beam, which is collimated and shaped by the beam splitter prism 2 and lens group 3 before exiting onto the target sphere 4 fixed on the object to be measured. After being reflected by the target sphere 4, the measurement beam returns parallel to the incident light and returns to the beam splitter prism 2 through the lens group 3. After being reflected by the beam splitter prism 2, the measurement beam passes through the filter 5 and focusing lens 6 in sequence and is incident on the position-sensitive detector 7, forming a light spot on the position-sensitive detector 7. The position-sensitive detector 7 outputs the measurement output value of the light spot position and sends the measurement output value of the light spot position to the signal processor for fitting and correction to obtain the fitted light spot position.

[0025] This invention is applied to laser tracking measurement, which includes fixed working distance application scenarios and variable working distance application scenarios. When the application scenario is a variable working distance application scenario, the laser precision positioning system also includes a ranging unit, which is used to acquire the working distance (1-40 m) in real time.

[0026] like Figure 1 and Figure 3 As shown, this invention provides a high-precision spot positioning method based on weighted piecewise polynomial fitting, used to correct the nonlinear error generated by the position-sensitive detector 7 under stray light interference and working distance variation conditions, including the following steps: S1. At a fixed working distance, collect the measurement output values ​​of the position-sensitive detector 7 at multiple preset calibration positions and the corresponding real spot positions as calibration data; The actual position (true value) of the light spot during the calibration process is a pre-set known position, not the measurement output value of the position-sensitive detector.

[0027] S2. Perform polynomial fitting on the calibration data to obtain the initial correction model, and calculate the fitting residual of the initial correction model at each calibration position; The polynomial fitting of the calibration data is specifically performed by using the measurement output value of the position-sensitive detector 7 as the input variable and the actual spot position as the output variable to construct a polynomial fitting function, and then solving for the polynomial coefficients by minimizing the sum of squared fitting residuals.

[0028] S3. Using the detection range of the position-sensitive detector 7 as the initial segmentation interval, the initial segmentation interval is divided into several continuous sub-intervals based on the fitting residual of the calibration position. The detection range of the position-sensitive detector 7 specifically refers to the effective range on the photosensitive surface of the position-sensitive detector 7 that can respond to the light spot, and the calibrated position is distributed within this range.

[0029] A continuous sub-interval refers to several non-overlapping coordinate sub-intervals that are connected end-to-end after the detection range of the position-sensitive detector 7 is divided.

[0030] Step S3 is as follows: S3.1. Using the detection range of the position-sensitive detector 7 as the initial segmented interval, the root mean square error of the initial segmented interval is obtained by processing the fitting residual at each calibration position according to the initial correction model. S3.2. Determine the root mean square error of all intervals: If the root mean square error of all intervals does not exceed the preset threshold, or the current total number of intervals reaches the preset number of intervals, or none of the intervals are intervals to be segmented, then the segmentation is completed, and all the current intervals are taken as the segmentation result, i.e., several continuous sub-intervals in step S3; otherwise, the interval with the largest root mean square error and the number of calibration positions within the interval reaches the preset number of calibration positions requirement is taken as the interval to be segmented. S3.3. Take the measurement output value corresponding to the largest absolute value of the fitting residual in each interval to be segmented and the median of all measurement output values ​​in the current interval to be segmented, and calculate the weighted sum to determine the segmentation point of each interval to be segmented. Then, divide each interval to be segmented into two sub-intervals according to the segmentation point of each interval to be segmented. In step S3.3, the segmentation points of each interval to be segmented are determined according to the following formula: In the formula, This represents the segmentation point of the current interval to be segmented, and k represents the weight parameter, k∈(0,1). This indicates the measurement location where the absolute value of the fitted residual is the largest within the current interval to be segmented. This represents the median of all measurement locations within the current interval to be segmented. This indicates the value of the independent variable corresponding to the maximum value. Represents absolute value. This represents the polynomial fitting function within the current interval. This indicates that the median of all data in the set is taken. This represents the detector output position value at the i-th calibration point. This indicates the true value position at the i-th calibration point. Indicates the current interval to be segmented [ The set consisting of all measurement locations within ) . The interval to be segmented is represented by ), which adopts a left-closed and right-open form. The last segment adopts a closed interval to ensure that all measurement data are covered without duplication or omission.

[0031] In this embodiment, the specific value of k is determined using a grid search method.

[0032] S3.4 Perform polynomial fitting on the calibration data in the intervals to be segmented, and then process it to obtain the fitting residuals at each calibration position in the current interval and the root mean square error of the current interval, and then return to step S3.2.

[0033] S4. Perform polynomial fitting on the calibration data in each sub-interval to obtain the correction model corresponding to each sub-interval, and use the correction models of all sub-intervals as the final correction model. S5. Based on the final corrected model and combined with the current measurement output value of the position-sensitive detector 7, the high-precision position of the current spot is obtained.

[0034] This invention is applied to laser tracking measurement, which includes both fixed working distance and variable working distance application scenarios. When the application scenario is a fixed working distance application scenario, execute steps S1-S5 to obtain the high-precision position of the light spot; Based on the working distance of the position-sensitive detector 7 in the measurement scenario, the measurement scenario is divided into a fixed working distance scenario or a variable working distance scenario. When the working distance of the position-sensitive detector 7 remains constant during the measurement process, it is classified as a fixed working distance scenario; when the working distance changes during the measurement process, it is classified as a variable working distance scenario.

[0035] When the application scenario is a variable working distance application scenario, the working distance range is divided into several distance sub-intervals. Steps S1-S4 are performed on each distance sub-interval to obtain the final correction model of each distance sub-interval. Based on the working distance obtained in real time during measurement, the final correction model of the corresponding distance sub-interval is selected and step S5 is performed to obtain the high-precision position of the light spot.

[0036] For a fixed working distance system, the entire distance range is considered as a single interval. First, the measured output values ​​of the position-sensitive detector at multiple calibration positions and their corresponding true position values ​​are acquired. The entire measurement interval is initialized as a continuous interval, and the least squares method is used to perform polynomial fitting on the data within this interval to establish an initial error correction model, and the fitting residual for each data point is calculated. Then, the root mean square error (RMSE) of each segment interval is calculated. If the RMS error of all intervals is lower than a preset threshold or the number of segments reaches the maximum allowable value, the final segmented fitting model is output; otherwise, the interval with the largest RMS error and meeting the minimum number of data points requirement is selected as the segment to be segmented. Based on the fitted residual distribution and data point location distribution within the segment to be segmented, a weighted splitting strategy that integrates error sensitivity and data balance is adopted. By introducing adjustable weighting coefficients to construct a segmentation point generation function, the optimal segmentation point is determined. The weighting strategy comprehensively considers the location of the maximum residual point and the location of the data median. The segment to be segmented is divided into two sub-intervals based on the optimal segmentation point. The segmentation boundary list and the current number of segments are updated, and polynomial fitting is performed again on all sub-intervals. The above error evaluation and segmentation process is repeated until the termination condition is met, ultimately forming a set of piecewise polynomial models.

[0037] For applications with variable working distances, the entire effective measurement distance range is divided into several distance sub-intervals. Within each sub-interval, the output signals of the position-sensitive detector 7 at multiple calibration positions and their corresponding actual spot positions are independently collected, forming a calibration dataset for that distance segment. To adapt to the requirements of variable working distance measurement, this invention employs distance-segmented fitting positioning: the entire effective working distance range is divided into several distance segments, and the aforementioned weighted segmented fitting process is independently executed within each segment, forming a segmented calibration system coupled with the working distance and detector position, i.e., each distance segment corresponds to a set of corresponding position correction models. During system operation, based on the real-time acquired working distance, the distance segment to which it belongs is determined, and the corresponding segmented polynomial fitting model is called, combined with the output signal of the position-sensitive detector 7 to complete the online high-precision spot position calculation.

[0038] In this embodiment, when the application scenario is a fixed working distance application scenario, the detection range is divided into multiple sub-intervals, and a weighted polynomial correction model is independently established in each sub-interval. Based on the maximum value of the fitting residual and the distribution characteristics of the data in the position dimension, the segmentation point is dynamically optimized, thereby achieving fine modeling of the local nonlinear region.

[0039] First, perform piecewise fitting, initialize the intervals, and then perform fitting: like Figure 1 As shown, the entire photosensitive surface detection range of the position-sensitive detector 7 is initialized as a continuous interval. E= [ p , q], towards interval E Add a split point to the interval E Divide into C+1 continuous subintervals as follows: Select one of its sub-intervals [ e s-1 , e s Let the corresponding dataset be { x d,i : x m,i}( i =1,2,…, n Construct the polynomial fitting function as follows: f s ( x d,i ) is in the interval [ e s-1 , e s Within ) the location value is detected using PSD. x d,i The input is the position correction value calculated by the polynomial fitting function. a i0 , a i1 … a in Let be the coefficients of each order of the polynomial. Solve for the coefficients of each order in formula (2) according to the least squares criterion, so that each fitted value obtained through polynomial calculation is... f s ( x d,i ) and calibration value x m,i The minimum sum of squares of the residuals between them is: Treating the coefficients of each order of the polynomial as unknowns, we take the partial derivative of the above equation and set it equal to zero to solve for the coefficients. Substituting these coefficients into the formula, we obtain the segmented PSD position detection fitting model. Then, we calculate the local root mean square error of each segment. If the root mean square error of a segment exceeds a preset threshold, we continue to segment that segment until the root mean square error of each segment is lower than the set threshold or the maximum number of segments is reached.

[0040] like Figure 3 As shown, to avoid the subjectivity of manually specifying intervals and improve the robustness of the splitting strategy, this invention employs a weighted splitting strategy that integrates error sensitivity and data balance. The core of this strategy lies in introducing an adjustable weighting coefficient. k∈[0,1], construct the weighted split point generating function as follows s 1 indicates the sample location with the largest absolute value of the residual within the current interval. s 2 represents the median of the input variables in the current interval. This can be adjusted... k It can achieve precise error correction ( k =1) to structural stability ( k A smooth transition of 0. Due to noise and nonlinear characteristics in real-world data, k The optimal value is difficult to determine through prior knowledge. If k If the value is too large, the algorithm becomes susceptible to noise, causing the splitting points to prematurely focus on local abnormal regions; if... k If the value is too small, it may ignore the high error region, reducing fitting efficiency. Therefore, k This can be considered a key hyperparameter of the model. To find the optimal hyperparameter, this invention employs a grid search method, constructing a discretized search space on the unit interval [0,1]. That is, sampling at equal intervals in the interval [0,1]. N =100 candidate values, forming a discretized parameter grid. For each k i ∈ K Execute the weighted piecewise polynomial fitting program independently and record the root mean square error of the final output global interval as follows: In the formula, Indicates that in a given k i The piecewise polynomial fitting output is obtained under the given conditions. Finally, the parameter value with the minimum root mean square error across the global interval is selected as the optimal hyperparameter, i.e. , to obtain Figure 4 The segmented fitting and positioning are shown.

[0041] In this embodiment, when the application scenario is a variable working distance application scenario, the present invention adopts distance segmented fitting positioning to improve the universality of the fitting model under different working distances. That is, according to the preset working distance interval, corresponding fitting models are established to compensate for the additional errors caused by the distance change.

[0042] The entire measurement range is divided into several distance intervals. Piecewise polynomial fitting is performed independently within each interval to establish a calibration model for each interval. During actual measurement, the fitting formula corresponding to the nearest calibration distance is selected based on the real-time acquired working distance for position calculation. In this embodiment, the measurement range is 40m, divided into intervals of 1-5m, 5-15m, and 15-40m, representing near, medium, and far working distance scenarios, respectively. The 5m and 15m intervals are repeatedly included as boundary points between adjacent intervals to enhance the continuity of interval transitions. In the formula, , , These represent the fitted values ​​of the light spot position in the near, medium, and far distance ranges, respectively. W SR_u , W MR_u , W LR_u ( u =1,2,…,9) represent the coefficients of the piecewise polynomial of each order within the near, medium, and far distance intervals, respectively. e v_SR , e v_MR , e v_LR ( v =0,1,2,C) are the segmentation points within each distance interval.

[0043] The specific laser unit uses a high-precision absolute distance measurement unit, the position sensitive detector 7 uses the PDP90A detector from Soleber, the displacement platform uses the MAX606M six-degree-of-freedom precision platform from Soleber, and the target sphere uses the Lambda 205025 high-precision hollow metal target sphere.

[0044] Through the above steps, high-precision modeling and error compensation for the position-sensitive detector 7 were achieved. For example... Figure 4As shown, the measurement range of the position-sensitive detector is divided into five intervals, with the dividing points marked by yellow vertical dashed lines. The fitted curve (blue dashed line) corresponding to the left vertical axis in the figure shows that, after correction by this method, the nonlinear deviation between the detector output value and the true value of the spot position is significantly corrected. The error curve corresponding to the right vertical axis shows that the error before fitting (red solid line) increases as the spot position moves away from the center of the photosensitive surface, while the fitted error after correction by this method (blue solid line) converges to a small range near zero. The weighted piecewise fitting strategy effectively focuses on the local nonlinear region, significantly suppressing the additional error caused by stray light interference. Based on this, the present invention is further extended to a correction mechanism applicable to variable-distance scenarios, namely, the distance piecewise fitting method, which independently constructs a high-precision local model within each working distance interval. This method effectively overcomes the problem that the calibration model is difficult to adapt to variable-distance scenarios, improving the accuracy of spot positioning and system stability.

[0045] The above detailed embodiments illustrate the technical solution and beneficial effects of the present invention. It should be understood that the above description is only the most preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, additions, and equivalent substitutions made within the scope of the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for precise laser positioning using weighted piecewise fitting of position-sensitive detection spot location, characterized in that, The method includes the following steps: S1. Collect the measurement output values ​​of the position-sensitive detector (7) at multiple calibration positions and the corresponding real spot positions as calibration data; S2. Perform polynomial fitting on the calibration data to obtain the initial correction model, and calculate the fitting residual of the initial correction model at each calibration position; S3. The detection range of the position-sensitive detector (7) is used as the initial segmentation interval. The initial segmentation interval is divided into several continuous sub-intervals according to the fitting residual of the calibration position. S4. Perform polynomial fitting on the calibration data in each sub-interval to obtain the correction model corresponding to each sub-interval, and use the correction models of all sub-intervals as the final correction model. S5. The current spot position is obtained by processing the final corrected model and the current measurement output value of the position-sensitive detector (7).

2. The position-sensitive detection spot position weighted piecewise fitting laser precision positioning method according to claim 1, characterized in that: The detection range of the position-sensitive detector (7) specifically refers to the range on the photosensitive surface of the position-sensitive detector (7) that can respond to the light spot.

3. The position-sensitive detection spot position weighted piecewise fitting laser precision positioning method according to claim 1, characterized in that: Step S3 specifically involves: S3.

1. The detection range of the position-sensitive detector (7) is used as the initial segmented interval. The root mean square error of the initial segmented interval is obtained by processing the fitting residual at each calibration position according to the initial correction model. S3.

2. Judge the root mean square error of all intervals: If the root mean square error of all intervals does not exceed the preset threshold, or the current total number of intervals reaches the preset number of intervals, or none of the intervals are intervals to be segmented, then the segmentation is completed and all the current intervals are taken as the segmentation result; otherwise, the interval with the largest root mean square error and the number of calibration positions in the interval reaches the preset number of calibration positions is taken as the interval to be segmented. S3.

3. Take the measurement output value corresponding to the largest absolute value of the fitting residual in each interval to be segmented and the median of all measurement output values ​​in the current interval to be segmented, and perform a weighted summation to determine the segmentation point of each interval to be segmented. Then, divide each interval to be segmented into two intervals according to the segmentation point of each interval to be segmented. S3.4 Perform polynomial fitting on the calibration data in the intervals to be segmented, and then process it to obtain the fitting residuals at each calibration position in the current interval and the root mean square error of the current interval, and then return to step S3.

2.

4. The position-sensitive detection spot position weighted piecewise fitting laser precision positioning method according to claim 3, characterized in that: In step S3.3, the segmentation points of each interval to be segmented are determined according to the following formula: In the formula, This indicates the position of the segmentation point of the current interval to be segmented, and k represents the weight parameter, k∈(0,1). This indicates the measurement location where the absolute value of the fitted residual is the largest within the current interval to be segmented. This represents the median of all measurement locations within the current interval to be segmented. This indicates the value of the independent variable corresponding to the maximum value. Represents absolute value. This represents the polynomial fitting function within the current interval. This indicates that the median of all data in the set is taken. This represents the detector output position value at the i-th calibration point. This represents the true value location at the i-th calibration point. This represents the set of all measurement locations within the current interval to be segmented.

5. The position-sensitive detection spot position weighted piecewise fitting laser precision positioning method according to claim 1, characterized in that: The specific method of performing polynomial fitting on the calibration data is to construct a polynomial fitting function by taking the measurement output value of the position-sensitive detector (7) as the input variable and the actual spot position as the output variable.

6. The position-sensitive detection spot position weighted piecewise fitting laser precision positioning method according to claim 1, characterized in that: The method is applied to laser tracking measurement, which includes both fixed working distance and variable working distance application scenarios. When the application scenario is a fixed working distance application scenario, execute steps S1-S5 to obtain the spot position; When the application scenario is a variable working distance application scenario, the working distance is divided into several distance sub-intervals. Steps S1-S4 are performed on each distance sub-interval to obtain the final correction model of each distance sub-interval. Based on the working distance obtained in real time during measurement, the final correction model of the corresponding distance sub-interval is selected and step S5 is performed to obtain the spot position.

7. The position-sensitive detection spot position weighted piecewise fitting laser precision positioning method according to claim 1, characterized in that: The method employs a laser precision positioning system, including a laser unit (1), a beam splitter (2), a lens group (3), a target sphere (4), a filter (5), a focusing lens (6), and a position-sensitive detector (7). The laser unit (1) emits a measurement beam, which passes through the beam splitter (2) and lens group (3) and is then emitted to the target ball (4) fixed on the object to be measured. After being reflected by the target ball (4), the measurement beam returns parallel to the incident light and passes through the lens group (3) back to the beam splitter (2). After being reflected by the beam splitter (2), the measurement beam passes through the filter (5) and focusing lens (6) in sequence and is emitted to the position sensitive detector (7).

8. The position-sensitive detection spot position weighted piecewise fitting laser precision positioning method according to claim 7, characterized in that: When the application scenario is a variable working distance application scenario, the laser precision positioning system also includes a ranging unit, which is used to acquire the working distance in real time.

Images