1. Introduction
With the rapid development of Chinese high-speed railway construction and the continuous expansion of the international market, the demand for China Railway Track System (CRTSIII) track slabs with completely independent intellectual property rights continues to grow rapidly. The dimensional accuracy directly affects the stability, smoothness of the track and the safety of the train operation. Therefore, it is necessary to carry out a strict inspection on the external dimensions of the track slab before the delivery, and the higher requirements should also be put forward for the detection efficiency and detection accuracy of the track slab. At present, the detection of the deviation of the track slab external dimensions is mainly based on the manual inspection and contact-type measurement devices. It takes 40 min to detect a track slab, and the detection efficiency and repeatability of which are rather low, which cannot bring greater benefits to actual production [
1].
With the advancement of computer science and information technology, the rapid development of industrial photography and machine vision, 3D scanning technology has become a widely used measurement technology with its fast scanning speed, non-contact, and high measurement accuracy [
2,
3,
4,
5]. Compared with other non-contact measurement methods [
6,
7,
8,
9], 3D scanning technology has the advantages of convenient integration with the computer, real-time processing and high flexibility of 3D reconstruction. It is widely applied in various fields, especially in industrial measurement, and it has great prospects. The data acquired by the 3D scanning system is point cloud data with three-dimensional information, which are usually defined by
X,
Y, and
Z coordinates, and are often used to represent the external surface information of the object being scanned. In the last decade, capture technologies that can generate 3D point cloud data are becoming mature and usable for more accurate point cloud data acquisition. As point cloud data acquisition technology is gradually maturing, the demand for 3D scanning technology in workpiece positioning and inspection, vehicle detection, and aircraft and shipbuilding manufacturing in industrial measurement is also increasing. Yang [
10] reported an automatic welding robot path planning system based on 3D vision technology and the successfully applied 3D scanning technology to the measurement and welding of complex geometric workpieces. Ahmed et al. [
11] studied an edge and corner detection method for unstructured 3D point clouds for unorganized point clouds and its application in robot welding. Kim and Cheng et al. [
12] presented a systematic and practical approach for dimensional and surface quality assessment of precast concrete elements using building information modeling (BIM) and 3D laser scanning technology, and the approach has the potential to produce an automated and reliable dimensional and surface quality assessment for precast concrete elements. Song et al. [
13] investigated a new solution to the high-quality 3D reverse modeling problem of complex surfaces for fine workpieces by using a laser line-scanning sensor. The surface of the measured object was reconstructed completely and accurately, and the measurement accuracy of the workpiece reconstruction was relatively high. Simler and Berndt [
14] used a 3D scanner to detect the surface of the car and proposed a new algorithm for various defects in the context of industrial surface inspection of free-form metallic pieces of cars. Jovancevic and Pham et al. [
15] used three-dimensional scanning technology to automatically detect defects on the airplane exterior surface, and the result shows that their work is robust, effective, and promising for industrial applications. Reyno and Marsden et al. [
16] also used 3D scanning technology to measure and evaluate surface damage of honeycomb sandwich panels, and the results showed that this method is more efficient and reliable compared to manual methods. According to the geometry of large-scale conical workpieces, integrating three-dimensional laser scanning and virtual environments, a novel rapid on-machine geometric measurement system was presented, and the 3D scanner is used to obtain the critical contour of the workpiece surface at high speed [
17]. Javier and Jose et al. [
18] discussed different non-contact 3D measuring strategies and presented a model for measuring geometrically complex parts, manipulated through a robot arm, using a novel vision system which consists a laser triangulation sensor and a motorized linear stage.
However, the application of 3D scanners for large-scale workpiece inspection, such as high-speed railway track slabs, is relatively rare. With an increasing amount of research—by institutes, units, and scholars—on high-precision track slab detection technology, many important theoretical achievements and patents have been attained. Lu et al. [
1] investigated a photogrammetric method to detect the track slab. The method adopts the marking point and the base station ruler on the track slab. It uses a digital camera to take a picture, and then uses the image matching principle to realize the registration between the image pairs and extracts the three-dimensional coordinates of each key position to calculate each detection indicators. The method requires the inspection tooling to perform the matching measurement, which can meet the requirements of rapid measurement on site. Fan et al. [
19] introduced the concept of industrial digital photogrammetry in the detection of CRTS III track slab. The system is used as follows the track slab is laid flat, the code control bracket is placed on the track slab to be detected, and the measuring device is erected thereon, so that the laser points of the guiding device are all projected onto the key position of the supporting block, and controlled. The camera motion completes the data acquisition of the whole slab, and finally extracts all the detection indicators by using feature extraction and calculation methods based on image features. Xue et al. [
20] reported a linear detection system for the track slab rapid detection system based on the three-dimensional detection technology of line image technology. The high-precision measurement reference platform and fast-moving mechanism are used to realize the plane image and 3D model size extraction of the track slab, and the key geometry of the track slab is obtained by algorithm correction. Xu et al. [
21] investigated a CRTS III track slab detection method based on laser tracking and handheld laser scanning combination. The method uses a hand-held laser scanner to scan the track slab in all directions, and the laser tracker achieves the real-time position and posture of the scanner to complete the stitching of the point cloud data. In this cycle, the point cloud data acquisition of the entire track slab surface is completed. Finally, the point cloud classification algorithm is used to classify different feature planes, thereby extracting the detection indexes of the track slab. When this detection method is used to detect the relative dimensional deviation of the track slab embedded bushing, it needs to rely on the spherical self-centralizing tooling, so the automatic detection cannot be completely realized. When the detection method detects the dimensional deviation of the pre-embedded casing of the track slab, it is necessary to use the spherical self-centering tooling, and the automatic detection cannot be fully realized. Yang et al. [
22] discussed the application of 3D laser scanning technology in the detection of CRTS III track slab. A large amount of point cloud data on the track slab surface is used for 3D reconstruction of the target, and the target geometric data is quickly obtained by the reconstructed model library. Finally, the target geometric data is compared with the BIM model to determine the construction deviation accurately.
Track slab detection methods based on three-dimensional scanners, photogrammetry, and other technologies are constantly being proposed. Such detection methods considerably improve detection efficiency while ensuring detection accuracy. The detection time of one slab can be controlled within 15 min, and the efficiency is improved by 60% compared with the traditional contact detection. The track slab detection technology has a qualitative leap, but there are some limitations of the current methods. To overcome these limitations of the current methods, this study uses 3D scanning technology combined with intelligent robot to obtain 3D point cloud data of the track slab detection index area and achieves the rapid detection of CRTSIII track slab. An improved RANSAC method is presented to extract the point cloud of the supporting block plane.
In this paper, we collected the data in the operation site by the system which includes the 3D scanner and the intelligent robot. Then, the obtained point cloud data was denoised, spliced, registered, and the surface of the supporting block plane was extracted. A lot of experimental work was done, and the detection results of the supporting block plane were analyzed and evaluated. Our final findings provide theoretical support for track slab detection and improve work efficiency in the field of track slab detection.
3. Supporting Block Plane Extraction
After completing the registration of the point cloud data and the standard track slab design model, the measured values of the characteristics of each detection index are calculated. Based on the geometric size (measured value) of each detected feature of the extracted track slab and the standard size (nominal value) of the standard design model, the deviation values of the two are calculated. The basis for extracting the parameters of each detection index is to fit the point cloud of the geometric parameter. In order to extract the slope of the supporting block, the angle between the supporting block plane and the jaw plane, the features of the height of the convexity and the skew, and the supporting block plane should be extracted first. Therefore, the plane fitting of the point cloud data of the supporting block plane should be carried out first.
In this paper, the point cloud plane fitting of the supporting block plane is an important part of the point cloud data processing and information extraction process, and it is the basis for the outer dimensions of the track slab. At present, the commonly used point cloud fitting algorithms are least squares method, eigenvalue method, global least squares method, RANSAC algorithm, etc. [
36,
37,
38,
39].
3.1. Improved RANSAC Plane Fitting Algorithm
Among many point cloud fitting algorithms, the RANSAC algorithm is widely used. Its robustness to noise and outliers makes RANSAC a suitable choice for performing shape detection on real-world scan data. Buer et al. [
40] successfully used RANSAC to extract the main surface from a very dense 3D point cloud. Schnabel et al. [
41] used the advantageous properties of the RANSAC algorithm to fit planes, cylinders, spheres, and torus in point clouds. Tarsha-kurdi et al. [
42] successfully extracted the roof plane of the building automatically from the airborne laser data based on the RANSAC algorithm. All the research results show that the algorithm has a good effect on the plane fitting. RANSAC judgement criterion are as follows:
According to the basic criteria of the RANSAC algorithm, at least one set of sampling result points in the
K group sampling under the confidence probability
P is all valid points (inliers) [
43].
Among them:
ε is the sample contamination rate (roughness ratio); n is the minimum number of points required to determine the parameters of model M; P is the probability of successful confidence; (1 − ε) is the probability of extracting a point as the correct point; indicates the probability of extracting all n points as correct points, and extracts n points to determine the model parameters; means that, under the condition that the above formula is satisfied, the probability that the K group samples are all correct points is P.
However, some improvements and corrections are required in order to make the algorithm more efficient for extracting the supporting block point clouds captured by the scanner. Therefore, compared with the basic method, we propose an improved RANSAC algorithm to improve the accuracy of extraction and the processing efficiency.
When the RANSAC algorithm performs plane fitting, its plane parameters are only fitted by three initial points (it takes at least three points to determine a plane). The plane fitting error of these three points may affect the fitting error judgment of all subsequent points. The farther away from the center of gravity of the three points, the larger the influence. Therefore, when the final optimal model (Best_Model_final) is selected, it is more reasonable to use the principle of least fitting error as the optimal model judgment criterion [
36,
44,
45,
46].
In order to avoid the cumulative effect of the error caused by using only three points to judge plane parameters, this paper improves the conventional RANSAC algorithm. After the RANSAC iteration is completed, the plane point sets obtained by RANSAC are re-fitted with the least squares to fit the plane parameters, and finally, the best fitting plane parameters of each plane are obtained. Take the fitting of a plane as an example:
The RANSAC algorithm is used to select the optimal plane. The selection condition is that the minimum plane of the plane fitting error is used, and the minimum plane of the model fitting error is taken as the optimal fitting plane to obtain the model “Candidate_Best_Model”.
Based on the point of the model “Candidate_Best_Model”, the least squares fitting method is used to solve the plane parameters of “Best_Model_Temporary”, and the “Best_Model_Temporary” temporary optimal plane parameters are obtained.
Based on the parameters, all points in the point clouds are re-selected and judged instead of using the random sampling method. According to the rejection threshold t, all the points in the point clouds whose error with the “Best_Model_Temporary” is less than the threshold t are included in the plane, and together with the point clouds in n, the previous model constitute the new “Best_Model_Temporary” model;
Repeat steps 2 and 3 above to end. Each time an iteration is completed, the temporary best model will be recorded. Repeat the iteration to get the final best plane “Best_Model_Final”. The determination condition of the end of the iteration is that the total number of interior points reaches a certain threshold. The threshold value should be determined according to the overall situation of the point clouds. In this paper, the fitting object is the point cloud data of the outer surface of the track slab supporting block. The overall flatness is smooth and the noise is less. Therefore, the threshold can be set to 90% of the total number of point clouds.
3.2. Algorithm Implementation
According to the above analysis and algorithm optimization, the improved RANSAC algorithm is used to planarly fit the point cloud data of the supporting block. The algorithm sets the sample contamination rate, the rejection threshold
t, the minimum number of points to determine the parameters of a plane model
n = 3, and the minimum number of points
N in a single plane according to the point cloud density, the scanning quality, and the roughness of the outer surface of the supporting block. According to the formula (8) transformation, the logarithm of the two sides can calculate the maximum number of iterations
K, and the plane point cloud automatic fitting process is shown in
Figure 6.
3.3. Algorithm Comparison
In order to compare the fitting results of the proposed algorithm with the classical RANSAC, a plane
Z =
X + 2
Y + 1 was set up. 2000 points are randomly selected from the planes and 500 outliers are added. The classical RANSAC algorithm and the algorithm presented in this paper are used for plane fitting, and the parameters are estimated. The standard deviation between the estimated value and the set parameters is calculated, and the results obtained by the two methods are compared. As shown in
Table 1.
Table 1 shows the results of two algorithms. Compared with the classical RANSAC, the algorithm proposed in this paper can eliminate outliers effectively, reduce model errors and improve the accuracy of parameter estimation.
6. Conclusions
Aimed at the problem of detecting the dimensional deviation of high-speed railway track slabs, this paper proposes a method for detecting the supporting block plane based on a 3D scanner. It is the first time that the three-dimensional scanner and robots have been applied to the detection of the high-speed railway track slab supporting block plane. Compared with traditional measurement method which takes 40 min to complete the detection of a track slab, this method not only guarantees the detection accuracy, but also improves the detection efficiency of track slab, and improves a plane extraction algorithm. We used the robot to carry a 3D scanner to detect the outer dimensions of the track slab and did a lot of experiments. We obtained the real point cloud data of the supporting block planes of track slab and processed the obtained point cloud data. Firstly, the point cloud data was denoised, and the data was spliced according to the physical space location, and then registered with the established model. Finally, the improved RANSAC algorithm was used to extract the point cloud data and analyze the final results. This method provides support for the subsequent automatic extraction of the index of the angle between the supporting block plane and the jaw surface, the slope of the supporting block plane, the features of the height of the convexity, etc. The summary is as follows:
Compared with the traditional detection method, the detection method can obtain high-quality supporting block detection data, and there is no need to touch the finished slab during the detection process, so as to avoid collision between the detection equipment and its transmission equipment. The detection method is fast and reliable, it takes only 7 min to complete the detection of a track slab, which greatly improves the detection efficiency of the track slab. The requirement for the detection environment is reduced, and the investment of manpower is reduced;
The point cloud data obtained by the detection method used in this paper can truly reflect the actual situation of the surface of the detected supporting block. The proposed RANSAC algorithm which is robust can efficiently extract the point cloud data of the supporting block planes, and the extraction results are higher. Compared with the design value of the model, the dimensional deviation of the appearance of the track slab is obtained. The establishment of the standard model enhances the visual expression of the detect parameters of the track slab and realizes the control of the dimensional quality;
The manufacturing process of the CTRSIII track slab and its geometric dimensional accuracy determine that the inspection of the outer dimensions of the track slab belongs to the category of industrial measurement. The minimum allowable deviation of the detect indexes of the track slab dimensions is 0.500 mm. Several detection indicators related to the supporting block plane are detected respectively by the detection method proposed in this paper and the contact measurement. By comparing the measurement results of the two methods, it is concluded that the average error of the method proposed in this paper is less than 0.100 mm and the maximum error is less than 0.200 mm, which meets the accuracy requirements of CRTSIII track slab detection.
The detection method has wide application prospects in the field of railway component detection. In future work, we will improve the detection method, add other sensors, and propose a multi-sensor integrated detection method.