Three-dimensional laser scanning method and system

Abstract

The invention discloses a three-dimensional laser scanning method and system, and relates to the technical field of three-dimensional laser scanning, and the method comprises the scanning steps: S1, transmitting sine wave laser to a to-be-detected product based on a laser transmitter, and receiving the reflected laser by a receiver; scanning at a plurality of different positions; s2, analyzing ambient light during scanning through an adaptive filtering algorithm, and adaptively adjusting filtering parameters; s3, measuring the phase difference between the emitted laser and the reflected laser, calculating the distance from the target point to the scanner, and recording and acquiring data during laser scanning; and S4, analyzing the scanned data, and setting a threshold value and a judgment condition. According to the method, distance errors caused by phase settlement errors are reduced, the accuracy of a measurement result is guaranteed, and object details can be accurately captured, so that a complete and fine three-dimensional model is generated, a high-quality data basis is provided for product detection and reverse engineering application, and the working efficiency and the analysis accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional laser scanning, and in particular to a three-dimensional laser scanning method and system. Background Art

[0002] 3D laser scanning technology has been widely used in industrial inspection, cultural relic protection, and topographic mapping due to its advantages of non-contact measurement and efficient data collection. By emitting laser beams and analyzing reflected signals, it can quickly obtain three-dimensional spatial information of the target object, providing basic data support for subsequent modeling and analysis. Existing 3D laser scanning methods still have many problems. During the actual scanning process, ambient light interference will seriously affect the accuracy of the reflected laser signal, resulting in phase measurement errors and reduced distance calculation accuracy. Some scanning methods lack the ability to adaptively process changes in ambient light and are difficult to work stably under complex lighting conditions. To this end, we proposed a 3D laser scanning method and system. Summary of the Invention

[0003] In order to solve the above technical problems, a three-dimensional laser scanning method and system are provided. This technical solution solves the above problems.

[0004] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0005] A three-dimensional laser scanning method, the scanning steps are:

[0006] S1. The laser transmitter emits a sinusoidal laser onto the product to be inspected, and the reflected laser is received by the receiver; scanning is performed at multiple different positions;

[0007] S2, using an adaptive filtering algorithm to analyze the ambient light during scanning and adaptively adjust the filtering parameters;

[0008] S3, measuring the phase difference between the emitted laser and the reflected laser, calculating the distance from the target point to the scanner, and recording the data obtained during the laser scanning;

[0009] S4. Analyze the scanned data, set thresholds and judgment conditions, identify distance data points with phase settlement errors, and correct the distance data points with settlement errors;

[0010] S5. After pre-processing the acquired data, perform point cloud stitching, and then use 3D modeling software to build a 3D model to generate a 3D model of the product to be inspected.

[0011] Preferably, the specific steps of adaptively adjusting the filtering parameters in step S2 are:

[0012] Before scanning begins, the adaptive filtering algorithm is initialized to determine the filter type, structure and initialization parameter values;

[0013] Set the initial threshold and step size parameters for ambient light analysis;

[0014] Obtain signal point sample data, extract features of the ambient light in the sample data, and obtain the intensity and spectrum information of the ambient light through signal statistical analysis and spectrum analysis;

[0015] According to the preset adjustment rules, the parameters of the adaptive filtering algorithm are adjusted. When a sudden increase in ambient light intensity is detected and strong interference occurs at a certain moment, the step size is immediately reduced and the update amplitude of the filter coefficient of the corresponding frequency band is increased;

[0016] The adjusted filter parameters are used to filter the subsequently collected reflected laser signals to remove ambient light interference.

[0017] Preferably, the specific steps of obtaining signal point sample data, extracting features of the ambient light of the sample data, and obtaining the intensity and spectrum information of the ambient light through signal statistical analysis and spectrum analysis are as follows:

[0018] Based on the photoelectric detector receiving the reflected laser signal, the optical signal is converted into an electrical signal, and the electrical signal is converted into a digital signal through the analog-to-digital converter, which is the signal point sample data;

[0019] Ambient light feature extraction is done through peak detection. Based on the sliding window method, a fixed-size window is set to slide across the signal sequence, and the maximum value within each window is found as the peak value. When the peak value exceeds the threshold, it is determined that there is abnormal strong light interference.

[0020] Spectrum analysis is based on Fourier transform analysis. The collected signal point sample data is subjected to discrete Fourier transform to obtain the frequency domain signal. The amplitude spectrum is then calculated and analyzed to determine the frequency component of ambient light interference.

[0021] Based on the peak information, the intensity of the ambient light is judged; through the amplitude spectrum obtained by spectrum analysis, the main frequency components and corresponding energy of the ambient light interference are determined.

[0022] Preferably, according to the preset adjustment rules, the steps for adjusting the parameters of the adaptive filtering algorithm are:

[0023] The step size parameter adjustment is based on the ambient light intensity. By setting the intensity standard, when the average level of the signal exceeds the intensity standard, it is judged that the ambient light intensity changes greatly, and the step size is reduced; otherwise, the step size is increased;

[0024] The filter coefficient adjustment rule is: perform spectrum analysis on the ambient light to find the frequency range of interference, and increase the update amplitude of the corresponding filter coefficient for the frequency band near the frequency in the frequency response characteristics of the filter.

[0025] Preferably, the step of calculating the distance from the target point to the scanner in step S3 is:

[0026] The wavelength of the emitted laser is λ, and the measured phase difference is The distance d is calculated as:

[0027]

[0028] Where c is the speed of light, f is the modulation frequency of the laser, and d is the distance from the target point to the scanner;

[0029] The recorded data includes angle information, timestamp, signal strength and environmental parameters.

[0030] Preferably, the threshold and judgment condition setting step in step S4 is:

[0031] The distance threshold is set based on the scanner's measurement accuracy and the scene's distance accuracy requirements;

[0032] The signal strength threshold is based on the statistical analysis results of the reflected signal strength to determine the strength range threshold;

[0033] The judgment conditions are based on the continuity of the laser beam scanning angle and the surface characteristics of the product to be inspected to set a reasonable range.

[0034] Preferably, the correction method in step S4 is based on field data interpolation, and for the identified erroneous distance data point, a correction value of the point is calculated through a linear relationship.

[0035] Preferably, the specific distance correction value d is calculated as follows:

[0036]

[0037] Where d is the distance correction value, and there are two reliable neighborhood data points P1(x1,y1,z1) and P2(x2,y2,z2) around it, with corresponding distances d1 and d2 respectively.

[0038] Preferably, the three-dimensional model generation step in step S5 is:

[0039] Preprocessing uses Gaussian filtering algorithm to remove noise, smooth data and normalize;

[0040] Point cloud stitching is based on the method of control point feature matching to unify the coordinate system of scanning data from different perspectives;

[0041] Use 3D modeling software to fit the surface from the point cloud, adjust the details and material texture mapping to obtain a 3D model of the product to be inspected.

[0042] A three-dimensional laser scanning system, comprising:

[0043] a scanning module configured to perform three-dimensional laser scanning;

[0044] An adaptive adjustment module configured to adaptively adjust filtering parameters based on ambient light analysis;

[0045] a calculation module configured to calculate a distance from a target point to the scanner and record data when acquiring the laser scan;

[0046] A correction module configured to correct distance data points with settlement errors;

[0047] The three-dimensional modeling module is configured to perform point cloud splicing after pre-processing the acquired data, and then build a three-dimensional model after splicing to generate a three-dimensional model of the product to be inspected.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention proposes to use an adaptive filtering algorithm to analyze ambient light and adjust filtering parameters, which can suppress ambient light interference in real time, ensure that the scan data is not affected by external light, improve data reliability, reduce distance errors caused by phase settlement errors, ensure the accuracy of measurement results, and accurately capture object details, thereby generating a complete and detailed three-dimensional model, providing a high-quality data foundation for product inspection and reverse engineering applications, and helping to improve work efficiency and analysis accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of the three-dimensional laser scanning steps of the present invention;

[0051] Figure 2 This is a block diagram of the three-dimensional laser scanning system of the present invention. DETAILED DESCRIPTION

[0052] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0053] Reference Figure 1 and Figure 2 As shown, a three-dimensional laser scanning method, the scanning steps are:

[0054] S1. The laser transmitter emits a sinusoidal laser onto the product to be inspected, and the reflected laser is received by the receiver; scanning is performed at multiple different positions;

[0055] S2, using an adaptive filtering algorithm to analyze the ambient light during scanning and adaptively adjust the filtering parameters;

[0056] S3, measuring the phase difference between the emitted laser and the reflected laser, calculating the distance from the target point to the scanner, and recording the data obtained during the laser scanning;

[0057] S4. Analyze the scanned data, set thresholds and judgment conditions, identify distance data points with phase settlement errors, and correct the distance data points with settlement errors;

[0058] S5. After pre-processing the acquired data, perform point cloud stitching, and then use 3D modeling software to build a 3D model to generate a 3D model of the product to be inspected.

[0059] In this application, an adaptive filtering algorithm is used to analyze ambient light and adjust filtering parameters, which can suppress ambient light interference in real time, ensure that the scan data is not affected by external light, and improve data reliability; S3 calculates the distance by measuring the phase difference, and cooperates with S4 to identify and correct erroneous data points, reducing the distance error caused by phase settlement errors, ensuring the accuracy of the measurement results, and accurately capturing object details; S5 pre-processes the data, stitches point clouds, and performs three-dimensional modeling, removes noise, and integrates multi-position scanning data to generate a complete and detailed three-dimensional model, providing a high-quality data foundation for product inspection and reverse engineering applications, which helps to improve work efficiency and analysis accuracy.

[0060] The specific steps for adaptively adjusting the filter parameters in step S2 are:

[0061] Before scanning begins, the adaptive filtering algorithm is initialized to determine the filter type, structure and initialization parameter values;

[0062] Set the initial threshold and step size parameters for ambient light analysis;

[0063] Obtain signal point sample data, extract features of the ambient light in the sample data, and obtain the intensity and spectrum information of the ambient light through signal statistical analysis and spectrum analysis;

[0064] According to the preset adjustment rules, the parameters of the adaptive filtering algorithm are adjusted. When a sudden increase in ambient light intensity is detected and strong interference occurs at a certain moment, the step size is immediately reduced and the update amplitude of the filter coefficient of the corresponding frequency band is increased;

[0065] The adjusted filter parameters are used to filter the subsequently collected reflected laser signals to remove ambient light interference.

[0066] This application determines the type, structure and parameter value of the filter by initializing the adaptive filtering algorithm, and sets the initial threshold and step size parameters of the ambient light analysis. It can provide basic configuration for different scanning scenarios and needs, so that the algorithm can be flexibly adjusted according to actual conditions. During the scanning process, when a sudden increase in ambient light intensity and strong interference are detected, the step size and filter coefficient update amplitude can be adjusted immediately to adapt to the dynamic changes of ambient light in real time and effectively cope with various complex lighting conditions.

[0067] Obtain signal point sample data, extract features of the ambient light of the sample data, and obtain the intensity and spectrum information of the ambient light through signal statistical analysis and spectrum analysis. The specific steps are as follows:

[0068] Based on the photoelectric detector receiving the reflected laser signal, the optical signal is converted into an electrical signal, and the electrical signal is converted into a digital signal through the analog-to-digital converter, which is the signal point sample data;

[0069] Ambient light feature extraction is done through peak detection. Based on the sliding window method, a fixed-size window is set to slide across the signal sequence, and the maximum value within each window is found as the peak value. When the peak value exceeds the threshold, it is determined that there is abnormal strong light interference.

[0070] Spectrum analysis is based on Fourier transform analysis. The collected signal point sample data is subjected to discrete Fourier transform to obtain the frequency domain signal. The amplitude spectrum is then calculated and analyzed to determine the frequency component of ambient light interference.

[0071] Based on the peak information, the intensity of the ambient light is judged; through the amplitude spectrum obtained by spectrum analysis, the main frequency components and corresponding energy of the ambient light interference are determined.

[0072] The signal point sample data for this application is obtained:

[0073] The photodetector converts the light signal into an electrical signal, which is then converted into a digital signal via an analog-to-digital converter. Mathematically, let the light signal intensity be L, the converted electrical signal intensity be E, and the digital signal value after analog-to-digital conversion be D'. This can be simply understood as the functional relationship D' = f(E), E = g(L), where f and g represent the corresponding conversion functions.

[0074] Ambient light feature extraction-peak detection:

[0075] Assume that the signal sequence is x'(n), n=1,2,...,N (N is the signal length), and the sliding window size is M(M <N);

[0076] For the kth window (k=1,2,…,N-M+1), the signals in the window are x(k),x(k+1),…,x(k+M-1);

[0077] The peak value P within the window k For: P k =max{x(k),x(k+1),…,x(k+M-1)};

[0078] Set the threshold to T peak , when P k >T peak When the light intensity is abnormally high, it is judged that there is abnormal strong light interference;

[0079] Spectral analysis - Discrete Fourier Transform (DFT) and amplitude spectrum calculation:

[0080] Perform discrete Fourier transform on the signal x'(n):

[0081] Calculate the amplitude spectrum Where Re(X'(k)) and Im(X'(k)) are the real and imaginary parts of X'(k), respectively;

[0082] By analyzing |X'(k)|, the frequency component of ambient light interference is determined;

[0083] Determine the ambient light intensity based on peak information and spectrum analysis to determine the interference frequency and energy:

[0084] In comparison, the larger the peak value P is, the stronger the ambient light intensity is;

[0085] From the amplitude spectrum |X'(k)|, find the k value with the largest amplitude and the corresponding frequency (f s is the sampling frequency) is the main frequency component of the ambient light interference, and the value of |X'(k)| corresponds to the energy of the corresponding frequency component.

[0086] According to the preset adjustment rules, the steps for adjusting the parameters of the adaptive filtering algorithm are as follows:

[0087] The step size parameter adjustment is based on the ambient light intensity. By setting the intensity standard, when the average level of the signal exceeds the intensity standard, it is judged that the ambient light intensity changes greatly, and the step size is reduced; otherwise, the step size is increased;

[0088] The filter coefficient adjustment rule is: perform spectrum analysis on the ambient light to find the frequency range of interference, and increase the update amplitude of the corresponding filter coefficient for the frequency band near the frequency in the frequency response characteristics of the filter.

[0089] The step size parameter of this application is dynamically adjusted according to the ambient light intensity. When the average signal level exceeds the set standard, that is, the ambient light intensity changes drastically, the step size is reduced in time to make the filter parameter update more precise, avoiding system instability due to excessive adjustment amplitude; and increasing the step size when the ambient light intensity is stable can speed up the convergence of the algorithm and improve the filtering efficiency. This dynamic adjustment mechanism ensures that the algorithm can quickly adapt to different lighting conditions and always maintain a good filtering effect, laying the foundation for accurately obtaining the reflected laser signal; the filter coefficient adjustment focuses on the frequency range of ambient light interference. After locking the interference frequency band through spectrum analysis, the update amplitude of the filter coefficient corresponding to the frequency band is targeted and increased.

[0090] The steps for calculating the distance from the target point to the scanner in step S3 are:

[0091] The wavelength of the emitted laser is λ, and the measured phase difference is The distance d is calculated as:

[0092]

[0093] Where c is the speed of light, f is the modulation frequency of the laser, and d is the distance from the target point to the scanner;

[0094] The recorded data includes angle information, timestamp, signal strength and environmental parameters.

[0095] This application records angle information to determine the directional position of the target point in three-dimensional space, and can accurately locate it when combined with distance data; the timestamp gives the data a time dimension, which facilitates data splicing and dynamic analysis during multi-station scanning; the signal strength reflects the material properties of the object surface, providing a basis for material analysis and model rendering; environmental parameters can be used to correct measurement errors caused by environmental factors to ensure the reliability and stability of the data.

[0096] The steps for setting the threshold and judgment conditions in step S4 are as follows:

[0097] The distance threshold is set based on the scanner's measurement accuracy and the scene's distance accuracy requirements;

[0098] The signal strength threshold is based on the statistical analysis results of the reflected signal strength to determine the strength range threshold;

[0099] The judgment conditions are based on the continuity of the laser beam scanning angle and the surface characteristics of the product to be inspected to set a reasonable range.

[0100] The distance threshold of this application is set based on the scanner measurement accuracy index and scene requirements to ensure that the judgment standard fits the actual application. In high-precision industrial detection scenarios, setting a strict distance threshold can promptly detect distance deviation data points caused by phase settlement errors, avoiding the impact of error accumulation on product detection accuracy; in scenarios where terrain surveying and mapping have relatively low accuracy requirements, reasonably relaxing the threshold can improve data processing efficiency while ensuring data availability.

[0101] The correction method in step S4 is based on domain data interpolation, and for the identified erroneous distance data point, the correction value of the point is calculated through a linear relationship.

[0102] The specific distance correction value d is calculated as follows:

[0103]

[0104] Where d is the distance correction value, and there are two reliable neighborhood data points P1(x1,y1,z1) and P2(x2,y2,z2) around it, with corresponding distances d1 and d2 respectively.

[0105] This application is based on domain data interpolation and utilizes the local correlation between data points. In three-dimensional laser scanning data, adjacent data points usually have similar characteristics and spatial relationships. By selecting two reliable neighborhood data points around the point to be corrected, this local correlation can be fully utilized to reasonably infer the correction value of the erroneous distance data point, so that the corrected data can better conform to the overall data distribution law; the correction value is calculated by linear relationship, and the calculation process is relatively simple, which does not require complex mathematical models or a large amount of computing resources. In practical applications, the identified erroneous distance data points can be quickly corrected.

[0106] The steps for generating the 3D model in step S5 are:

[0107] Preprocessing uses Gaussian filtering algorithm to remove noise, smooth data and normalize;

[0108] Point cloud stitching is based on the method of control point feature matching to unify the coordinate system of scanning data from different perspectives;

[0109] Use 3D modeling software to fit the surface from the point cloud, adjust the details and material texture mapping to obtain a 3D model of the product to be inspected.

[0110] This application uses a Gaussian filtering algorithm for preprocessing, which can effectively filter out noise in the scanned data, smooth data fluctuations, and make the point cloud data smoother and closer to the real surface of the object; normalization processing unifies the data scale to avoid the impact of subsequent operations due to differences in data magnitude, ensure the accuracy and reliability of the data, and lay a good foundation for three-dimensional model generation; the point cloud stitching method based on control point feature matching can accurately integrate scan data from different perspectives into the same coordinate system, effectively make up for the limitations of a single scan, completely cover all parts of the product to be inspected, ensure the integrity of the three-dimensional model, and avoid model missing or misalignment problems.

[0111] A three-dimensional laser scanning system, comprising:

[0112] a scanning module configured to perform three-dimensional laser scanning;

[0113] An adaptive adjustment module configured to adaptively adjust filtering parameters based on ambient light analysis;

[0114] a calculation module configured to calculate a distance from a target point to the scanner and record data when acquiring the laser scan;

[0115] A correction module configured to correct distance data points with settlement errors;

[0116] The three-dimensional modeling module is configured to perform point cloud splicing after pre-processing the acquired data, and then build a three-dimensional model after splicing to generate a three-dimensional model of the product to be inspected.

[0117] This application can quickly and accurately obtain the three-dimensional coordinate information of the object surface, generate point cloud data, and provide basic data support for subsequent modeling and analysis. It can perform all-round and multi-angle scanning of the object to ensure that complete object surface information is obtained and avoid missing important details. It can adaptively adjust the filtering parameters according to changes in ambient light, and can effectively suppress the impact of ambient light interference on the scan data, so that the system can maintain stable performance under different lighting conditions, improve the quality and reliability of the data, and reduce measurement errors caused by changes in ambient light.

[0118] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. Various changes and improvements are possible without departing from the spirit and scope of the present invention, and such changes and improvements fall within the scope of the invention as claimed.

Claims

1. A three-dimensional laser scanning method, characterized in that: The scanning steps are: S1. The laser transmitter emits a sinusoidal laser onto the product to be inspected, and the reflected laser is received by the receiver; scanning is performed at multiple different positions; S2, using an adaptive filtering algorithm to analyze the ambient light during scanning and adaptively adjust the filtering parameters; S3, measuring the phase difference between the emitted laser and the reflected laser, calculating the distance from the target point to the scanner, and recording the data obtained during the laser scanning; S4. Analyze the scanned data, set thresholds and judgment conditions, identify distance data points with phase settlement errors, and correct the distance data points with settlement errors; S5. After pre-processing the acquired data, perform point cloud stitching, and then use 3D modeling software to build a 3D model to generate a 3D model of the product to be inspected.

2. A three-dimensional laser scanning method according to claim 1, characterized in that: The specific steps for adaptively adjusting the filter parameters in step S2 are: Before scanning begins, the adaptive filtering algorithm is initialized to determine the filter type, structure and initialization parameter values; Set the initial threshold and step size parameters for ambient light analysis; Obtain signal point sample data, extract features of the ambient light in the sample data, and obtain the intensity and spectrum information of the ambient light through signal statistical analysis and spectrum analysis; According to the preset adjustment rules, the parameters of the adaptive filtering algorithm are adjusted. When a sudden increase in ambient light intensity is detected and strong interference occurs at a certain moment, the step size is immediately reduced and the update amplitude of the filter coefficient of the corresponding frequency band is increased; The adjusted filter parameters are used to filter the subsequently collected reflected laser signals to remove ambient light interference.

3. A three-dimensional laser scanning method according to claim 2, characterized in that: Obtain signal point sample data, extract features of the ambient light of the sample data, and obtain the intensity and spectrum information of the ambient light through signal statistical analysis and spectrum analysis. The specific steps are as follows: Based on the photoelectric detector receiving the reflected laser signal, the optical signal is converted into an electrical signal, and the electrical signal is converted into a digital signal through the analog-to-digital converter, which is the signal point sample data; Ambient light feature extraction is done through peak detection. Based on the sliding window method, a fixed-size window is set to slide across the signal sequence, and the maximum value within each window is found as the peak value. When the peak value exceeds the threshold, it is determined that there is abnormal strong light interference. Spectrum analysis is based on Fourier transform analysis. The collected signal point sample data is subjected to discrete Fourier transform to obtain the frequency domain signal. The amplitude spectrum is then calculated and analyzed to determine the frequency component of ambient light interference. Based on the peak information, the intensity of the ambient light is judged; through the amplitude spectrum obtained by spectrum analysis, the main frequency components and corresponding energy of the ambient light interference are determined.

4. A three-dimensional laser scanning method according to claim 2, characterized in that: According to the preset adjustment rules, the steps for adjusting the parameters of the adaptive filtering algorithm are as follows: The step size parameter adjustment is based on the ambient light intensity. By setting the intensity standard, when the average level of the signal exceeds the intensity standard, it is judged that the ambient light intensity changes greatly, and the step size is reduced; otherwise, the step size is increased; The filter coefficient adjustment rule is: perform spectrum analysis on the ambient light to find the frequency range of interference, and increase the update amplitude of the corresponding filter coefficient for the frequency band near the frequency in the frequency response characteristics of the filter.

5. The three-dimensional laser scanning method according to claim 1, characterized in that: The steps for calculating the distance from the target point to the scanner in step S3 are: The wavelength of the emitted laser is λ, and the measured phase difference is The distance d is calculated as: Where c is the speed of light, f is the modulation frequency of the laser, and d is the distance from the target point to the scanner; The recorded data includes angle information, timestamp, signal strength and environmental parameters.

6. The three-dimensional laser scanning method according to claim 1, characterized in that: The steps for setting the threshold and judgment conditions in step S4 are as follows: The distance threshold is set based on the scanner's measurement accuracy and the scene's distance accuracy requirements; The signal strength threshold is based on the statistical analysis results of the reflected signal strength to determine the strength range threshold; The judgment conditions are based on the continuity of the laser beam scanning angle and the surface characteristics of the product to be inspected to set a reasonable range.

7. The three-dimensional laser scanning method according to claim 1, characterized in that: The correction method in step S4 is based on domain data interpolation, and for the identified erroneous distance data point, the correction value of the point is calculated through a linear relationship.

8. A three-dimensional laser scanning method according to claim 7, characterized in that: The specific distance correction value d is calculated as follows: Where d is the distance correction value, and there are two reliable neighborhood data points P1(x1,y1,z1) and P2(x2,y2,z2) around it, with corresponding distances d1 and d2 respectively.

9. The three-dimensional laser scanning method according to claim 1, characterized in that: The steps for generating the 3D model in step S5 are: Preprocessing uses Gaussian filtering algorithm to remove noise, smooth data and normalize; Point cloud stitching is based on the method of control point feature matching to unify the coordinate system of scanning data from different perspectives; Use 3D modeling software to fit the surface from the point cloud, adjust the details and material texture mapping to obtain a 3D model of the product to be inspected.

10. A three-dimensional laser scanning system, characterized in that: include: a scanning module configured to perform three-dimensional laser scanning; An adaptive adjustment module configured to adaptively adjust filtering parameters based on ambient light analysis; a calculation module configured to calculate a distance from a target point to the scanner and record data when acquiring the laser scan; A correction module configured to correct distance data points with settlement errors; The three-dimensional modeling module is configured to perform point cloud splicing after pre-processing the acquired data, and then build a three-dimensional model after splicing to generate a three-dimensional model of the product to be inspected.

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