A method and apparatus for three-dimensional reconstruction by line laser scanning

Abstract

This invention discloses a 3D reconstruction method and apparatus based on line laser scanning, solving the problems of poor 3D reconstruction accuracy of existing binocular cameras and the difficulty of 3D reconstruction using line laser scanning. It utilizes a line laser scanner to achieve accurate and rapid 3D reconstruction of a calibration object. First, a high-precision turntable and a laser scanning module are mounted on the same plane, and a standard calibration cube is mounted on the turntable. Then, the high-precision turntable encoder is controlled to collect depth data of the calibration object around one revolution. The influence of turntable acceleration and deceleration is eliminated by removing overlapping point cloud portions at the beginning and end, and outliers are eliminated using a filtering algorithm. Next, the average depth data of the point cloud, the scanning rotation angle, and the geometric features of the calibration block are used to perform 3D reconstruction of the calibration block, obtaining the reconstructed model point cloud. Finally, plane fitting is performed on the reconstructed point cloud, and the orthogonality of the normal vectors of the four faces is used to determine whether the reconstruction is successful. This invention improves both the accuracy and efficiency of 3D reconstruction by eliminating overlapping point cloud portions to reduce the influence of turntable acceleration and deceleration, and by using the geometric features of the reconstructed model to determine reconstruction accuracy.

Description

Technical Field

[0001] This invention relates to the field of three-dimensional reconstruction technology, and more specifically to a method and apparatus for line laser scanning and three-dimensional reconstruction. Background Technology

[0002] 3D reconstruction technology has wide applications in many fields, including virtual reality, augmented reality, robot navigation, cultural heritage preservation, and architectural design. 3D reconstruction technology refers to the use of computer vision and image processing techniques to extract 3D geometric information from data collected from multiple images or sensors to generate or reconstruct corresponding 3D models or scenes. Currently, 3D reconstruction often uses binocular cameras or depth cameras. Traditional line laser scanning technology typically only provides two-dimensional line projection information, and its applications are usually 2D scanning, making it unable to achieve 3D reconstruction.

[0003] Currently, 3D reconstruction mainly relies on binocular cameras, which is inefficient and cannot achieve high accuracy. A search of existing literature reveals Chinese invention patent publication number CN110349251A, entitled "A 3D Reconstruction Method and Device Based on a Binocular Camera." This patent describes a method for calculating point cloud data of the shared viewing area of ​​two target images using parallel-arranged binocular cameras to obtain a 3D network model. However, this method requires preprocessing of the two images captured by the binocular cameras, resulting in low efficiency and lower accuracy compared to laser scanners. Chinese invention patent publication number CN114413784A, entitled "A Line Laser 3D Contour Scanning Reconstruction System and Its Control Method," describes a method and control method for 3D contour measurement using a line laser. This method involves setting a slide assembly on a mounting frame, mounting the line laser contour meter on the slide, and controlling a motor to drive the slide along a straight line, causing the scanner to displace relative to the object being measured, thereby achieving 3D reconstruction. However, this method can only reconstruct a portion of the object's surface, not the entire contour.

[0004] In summary, while existing binocular cameras can perform 3D reconstruction tasks, the images they capture often require preprocessing, resulting in low efficiency and lower accuracy compared to laser scanners. Meanwhile, current line laser scanners are typically suitable for 2D reconstruction scenarios, extracting 2D information of the object being measured by working with devices such as ball screws, but they cannot achieve the goal of 3D reconstruction. Summary of the Invention

[0005] The purpose of this invention is to solve the problems of low accuracy in 3D reconstruction using binocular cameras and the limited range of traditional line laser scanning, and to provide a method and apparatus for 3D reconstruction using line laser scanning.

[0006] The technical solution for achieving the objective of this invention is: a method and apparatus for three-dimensional reconstruction via line laser scanning, comprising the following two stages:

[0007] Phase 1, the phase of acquiring reconstruction point data, includes the following steps:

[0008] Step 1.1: Fix the bottom surface of the turntable to be on the same plane as the laser scanning device, so that the laser line scanning direction is parallel to the axis of the turntable;

[0009] Step 1.2: Install a standard calibration cube at the central axis of the turntable. The side length of the cube is known.

[0010] Step 1.3: Turn on the line laser scanner, determine the initial position under the turntable encoder, and control the turntable to rotate more than one revolution to collect point cloud data;

[0011] Step 1.4: Remove point cloud data where the start and end points coincide, based on the encoder position;

[0012] Step 1.5: Calculate the average distance between point clouds using a statistical filtering algorithm, remove outlier point clouds, and retain the point cloud coordinates of the calibrated portion;

[0013] Step 1.6: Convert the filtered point cloud coordinates into reconstructed point cloud coordinates according to the 3D reconstruction algorithm.

[0014] Phase Two, the assessment of 3D reconstruction quality, includes the following steps:

[0015] Step 2.1: Extract the point cloud with the height value at the middle position and calculate the cross-sectional area of ​​the 3D reconstructed model;

[0016] Step 2.2: Compare the cross-sectional area of ​​the cross-sectional cube calibration block of the reconstructed model. If its area is greater than the cross-sectional area of ​​the calibration cube, adjust the parameters in the 3D reconstruction algorithm; otherwise, increase the parameters and repeat step 1.6. When the areas are the same, the reconstruction can be considered successful.

[0017] Step 2.3: The coordinates of the reconstructed point cloud are fitted to a plane using the least squares method to obtain four fitting surfaces for the reconstructed calibration cube.

[0018] Step 2.4: Calculate the normal vectors of the four fitted surfaces based on their equations and determine whether the normal vectors are pairwise orthogonal. If they are pairwise orthogonal, the 3D reconstruction is successful.

[0019] Furthermore, in step 1.4, the overlapping portion of the start and end point clouds of the high-precision turntable is removed to reduce the impact of the turntable's acceleration at start and deceleration at end.

[0020] Furthermore, in step 1.6, the principle of the 3D reconstruction algorithm is as follows:

[0021]

[0022]

[0023] Z′=x

[0024] The coordinates of the point cloud after scanning and filtering are P(x, y, z), the coordinates of the reconstructed point cloud are P′(x′, y′, z′), the average z value of the point cloud is h, and the side length of the cube calibration block is l.

[0025] Furthermore, in step 2.2, the method for adjusting the reconstruction parameters is as follows:

[0026] g=s(l+h)-l 2

[0027]

[0028] The average z-value of the point cloud is h, the side length of the cube calibration block is l, the area enclosed by the point cloud with the median z′ value after reconstruction is s(l+h), and g is the parameter judgment function.

[0029] Furthermore, in step 2.3, the method for calculating the equation of the fitted plane using the least squares method is as follows:

[0030] AX = 0

[0031] Where A is an n×4 fitted point cloud coordinate matrix, n is the number of fitted point clouds, and x is a 4×1 matrix composed of plane equation parameters, where the parameters are a, b, c, and d. Singular value decomposition is used to obtain the eigenvectors corresponding to the last column of the singular vector matrix U, yielding the least squares fitting result. The plane equation is ax + by + cz + d = 0.

[0032] Furthermore, in step 2.4, the method for determining whether the normal vectors of the four fitted surfaces are pairwise orthogonal is as follows:

[0033] α T ·β=0

[0034] α T ·γ=0

[0035] α T ·δ=0

[0036] β T ·γ=0

[0037] β T ·δ=0

[0038] γ T ·δ=0

[0039] Where α, β, γ, and δ are the normal vectors of the four fitting surfaces in step 2.4. If the normal vectors of the four fitting surfaces are pairwise orthogonal, it means that the three-dimensional reconstruction is complete.

[0040] Compared with the prior art, the significant advantages of this invention are: (1) This invention uses a line laser scanner for three-dimensional reconstruction, which improves the accuracy of three-dimensional reconstruction. (2) This invention uses a turntable for three-dimensional reconstruction of the object under test, which results in a more complete reconstruction area compared with traditional devices. (3) This invention judges the reconstruction quality by comparing the cross-sectional area of ​​the reconstructed model with the area of ​​the standard calibration block and the normal vectors of the four fitted surfaces, which improves the accuracy of reconstruction compared with other algorithms.

[0041] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0042] Figure 1 This is a flowchart of a 3D reconstruction method based on line laser scanning.

[0043] Figure 2 This is a schematic diagram of a 3D reconstruction device based on line laser scanning. The measurement and control module controls the scanning task of the laser scanning module and simultaneously controls the rotation of the high-precision turntable to realize the 3D reconstruction of the calibration module.

[0044] Figure 3 This is a 3D reconstruction result based on line laser scanning. The depth point cloud distribution of the plane is obtained by scanning the object being measured, and then the model is reproduced by a 3D reconstruction algorithm. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0046] Please see Figure 1 This is a flowchart illustrating a three-dimensional reconstruction method based on line laser scanning according to an embodiment of this application. The three-dimensional reconstruction method and apparatus based on line laser scanning according to an embodiment of this application includes the following steps:

[0047] S1, Obtain the reconstruction point data. The specific process is as follows:

[0048] S1.1, The fixed turntable and the laser scanning module are on the same plane, so that the laser line scanning direction is parallel to the axis of the turntable;

[0049] S1.2, Install a standard calibration cube at the central axis of the turntable;

[0050] S1.3, turn on the line laser scanner, determine the initial position under the turntable encoder and control the turntable to rotate more than one revolution to collect point cloud data;

[0051] S1.4, Combine encoder position information to remove point cloud data where the start and end coincide;

[0052] S1.5, a statistical filtering algorithm is used to remove outlier point clouds while retaining the point cloud coordinates of the calibrated portion;

[0053] S1.6, Based on the 3D reconstruction algorithm, the filtered point cloud coordinates are converted into reconstructed point cloud coordinates. The principle of the algorithm is as follows:

[0054]

[0055]

[0056] Z′=x

[0057] The coordinates of the point cloud after scanning and filtering are P(x, y, z), the coordinates of the reconstructed point cloud are P′(x′, y′, z′), the average z value of the point cloud is h, and the side length of the cube calibration block is l.

[0058] S2, determine the reconstruction quality, the specific process is as follows:

[0059] S2.1, Extract the point cloud with the height value at the middle position and calculate the cross-sectional area of ​​the 3D reconstructed model;

[0060] S2.2 compares the cross-sectional area of ​​the calibration cube block of the reconstructed model with that of the calibration cube. If the area is larger than the cross-sectional area of ​​the calibration cube, the parameters in the 3D reconstruction algorithm are adjusted to be smaller; otherwise, the parameters are adjusted to be larger, and S1.6 is repeated. When the areas are the same, the reconstruction is considered successful. The specific judgment method is as follows:

[0061] g=s(l+h)-l 2

[0062]

[0063] The average z-value of the point cloud is h, the side length of the cube calibration block is l, the area enclosed by the point cloud with the median z′ value after reconstruction is s(l+h), and 9 is the parameter judgment function.

[0064] S2.3, the least squares method is used to fit the reconstructed point cloud coordinates to obtain four fitting surfaces for the reconstructed calibration cube. The fitting method is as follows:

[0065] AX = 0

[0066] Where A is an n×4 fitted point cloud coordinate matrix, n is the number of fitted point clouds, and X is a 4×1 matrix composed of plane equation parameters, where the parameters are a, b, c, and d. Singular value decomposition is used to obtain the eigenvectors corresponding to the last column of the singular vector matrix U, yielding the least squares fitting result. The plane equation is ax + by + cz + d = 0.

[0067] S2.4 Calculate the normal vectors of the four fitted planes based on their equations and determine if they are pairwise orthogonal. If they are pairwise orthogonal, the 3D reconstruction is successful. The method for determining whether the normal vectors of the four fitted planes are orthogonal is as follows:

[0068] α T ·β=0

[0069] α T ·γ=0

[0070] α T ·δ=0

[0071] β T ·γ=0

[0072] β T ·δ=0

[0073] γ T ·δ=0

[0074] Where α, β, γ, and δ are the normal vectors of the four fitting surfaces in S2.3. If the normal vectors of the four fitting surfaces are pairwise orthogonal, it means that the three-dimensional reconstruction is complete.

[0075] Please see Figure 3 This is a three-dimensional reconstruction effect diagram of an embodiment of this application.

[0076] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method and apparatus for three-dimensional reconstruction using line laser scanning, characterized in that, It includes the following two stages: Phase 1: Obtaining reconstruction point data, including the following steps: Step 1.1: Fix the bottom surface of the turntable to be on the same plane as the laser scanning device, so that the laser line scanning direction is parallel to the axis of the turntable; Step 1.2: Install the standard calibration cube at the central axis of the turntable; Step 1.3: Turn on the line laser scanner, determine the initial position under the turntable encoder, and control the turntable to rotate more than one revolution to collect point cloud data; Step 1.4: Remove point cloud data where the start and end points coincide, based on the encoder position; Step 1.5: Calculate the average distance between point clouds using a statistical filtering algorithm, remove outlier point clouds, and retain the point cloud coordinates of the calibrated portion; Step 1.6: Convert the filtered point cloud coordinates into reconstructed point cloud coordinates according to the 3D reconstruction algorithm; the principle of the 3D reconstruction algorithm is as follows: Z' = x The coordinates of the point cloud after scanning and filtering are P(x, y, z), the coordinates of the reconstructed point cloud are P′(x′, y′, z′), the average z value of the point cloud is h, and the side length of the cube calibration block is l. Phase Two: Assessing the quality of the 3D reconstruction, including the following steps: Step 2.1: Extract the point cloud with the height value at the middle position and calculate the cross-sectional area of ​​the 3D reconstructed model; Step 2.2: Compare the cross-sectional area of ​​the cross-sectional cube calibration block of the reconstructed model. If its area is greater than the cross-sectional area of ​​the calibration cube, adjust the parameters in the 3D reconstruction algorithm; otherwise, increase the parameters and repeat step 1.

6. When the areas are the same, the reconstruction can be considered successful. Step 2.3: The coordinates of the reconstructed point cloud are fitted to a plane using the least squares method to obtain four fitting surfaces for the reconstructed calibration cube. Step 2.4: Calculate the normal vectors of the four fitted surfaces based on their equations and determine whether the normal vectors are pairwise orthogonal. If they are pairwise orthogonal, the 3D reconstruction is successful.

2. The method and apparatus for three-dimensional reconstruction by line laser scanning according to claim 1, characterized in that: In Phase 1 and Phase 2, the 3D reconstruction device includes a laser scanning module, a high-precision turntable, a reconstruction calibration module, and a measurement control module.

3. The method and apparatus for three-dimensional reconstruction by line laser scanning according to claim 1, characterized in that: In step 1.2, the upper plane of the line laser scanner and the upper plane of the cube calibration block are adjusted to be perpendicular to the ground by placing a level, thereby ensuring that the line laser is parallel to the axis of the calibration block.

4. The method and apparatus for three-dimensional reconstruction by line laser scanning according to claim 1, characterized in that: In step 1.3, the specific angle at which the turntable rotates more than one revolution is within the range of 370° to 380° based on experience.

5. The method and apparatus for three-dimensional reconstruction by line laser scanning according to claim 1, characterized in that: In step 1.4, the overlapping part of the start and end point clouds of the high-precision turntable is removed to reduce the impact of the turntable's starting acceleration and ending deceleration.

6. The method and apparatus for three-dimensional reconstruction by line laser scanning according to claim 1, characterized in that: In step 2.1, the point cloud with the median height is the point cloud coordinate P′(x′, y′, z′) after reconstruction, where z′ is the median value.

7. The method and apparatus for three-dimensional reconstruction by line laser scanning according to claim 1, characterized in that: In step 2.2, g=s(l+h)-l 2 The average z-value of the point cloud is h, the side length of the cube calibration block is l, the area enclosed by the point cloud with the median z′ value after reconstruction is s(l+h), and g is the parameter judgment function.

8. The method and apparatus for three-dimensional reconstruction by line laser scanning according to claim 1, characterized in that: In step 2.3 AX = 0 Where A is an n×4 fitted point cloud coordinate matrix, n is the number of fitted point clouds, and X is a 4×1 matrix composed of plane equation parameters, where the parameters are a, b, c, and d; singular value decomposition is used to find the eigenvectors corresponding to the last column of the singular vector matrix U, and the least squares fitting result is obtained, and the plane equation is ax + by + cz + d = 0.

9. The method and apparatus for three-dimensional reconstruction by line laser scanning according to claim 1, characterized in that: In step 2.4 α T ·β＝0 a T ·γ=0 a T ·δ=0 b T ·γ=0 b T ·δ=0 c T ·δ=0 Where α, β, γ, and δ are the normal vectors of the four fitting surfaces in step 2.

4. If the normal vectors of the four fitting surfaces are pairwise orthogonal, it means that the three-dimensional reconstruction is complete.

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