Pantograph slide plate point cloud data splicing method and system based on calibration block and improved ICP algorithm

Abstract

The invention discloses a pantograph slide plate point cloud data splicing method and system based on a calibration block and an improved ICP algorithm, and the method specifically comprises the steps: firstly continuously outputting multi-frame slide plate contour data through a laser displacement sensor, and expanding the multi-frame slide plate contour data into three-dimensional point cloud data; processing the sliding plate data by adopting a hybrid filtering denoising algorithm to obtain a sliding plate contour data set, and converting two groups of laser point cloud data acquired by the two laser displacement sensors into the same coordinate system to form complete sliding plate surface point cloud data; thirdly, completing coarse registration on the sliding plate surface point cloud data by adopting a data rapid splicing method based on a calibration block; and finally, an improved ICP (Inductively Coupled Plasma) registration algorithm is adopted to complete rapid fine registration of the point cloud data to obtain complete sliding plate surface point cloud data. The method has high precision and real-time performance, can overcome the interference caused by the influence of the surface texture, color and dynamic measurement environment of the pantograph slide plate, and provides real-time data support for intelligent operation and maintenance of rail transit.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent operation and maintenance of urban rail transit, and particularly to a method and system for splicing point cloud data of a pantograph slider based on a calibration block and an improved ICP algorithm. Background Art

[0002] With the rapid development of urban rail transit, the problem of train operation safety online has become increasingly prominent. As a key component in the pantograph that directly contacts the catenary wire, the pantograph slider undertakes the important responsibilities of stable power transmission and bearing friction and wear, ensuring the reliability and safety of the train's power supply. Once the pantograph slider shows abnormalities, it may lead to unstable power transmission, poor contact, arc generation, equipment damage, and even train power outage or operation failure, seriously threatening the train operation safety and operation efficiency. Therefore, it is necessary to regularly detect the pantograph, and by reconstructing the point cloud data of the slider surface, more comprehensive and accurate information can be provided for surface defect detection, so as to timely discover the wear condition of the slider and take corresponding maintenance measures to prevent train suspension and safety accidents caused by slider failures. In addition, regular detection not only helps to optimize the maintenance plan, but also can extend the service life of the equipment and reduce the operation and maintenance costs. Therefore, accurate detection of the slider is of crucial significance for ensuring the safe and efficient operation of urban rail transit trains.

[0003] Currently, for the detection of the pantograph slider, mainly binocular stereo vision, structured light and other technologies are used to obtain the point cloud data of the slider surface. However, limited by the slider surface texture, color and dynamic measurement environment, the accuracy of such methods needs to be improved, and there are problems such as large computational amount, poor real-time performance and high requirements for equipment. In this case, how to improve the real-time performance of the detection algorithm while ensuring the accuracy of the slider surface point cloud reconstruction has become an urgent and challenging problem.

[0004] Patent CN117808782A discloses a method for detecting surface defects of a pantograph slider. This method constructs a selection box through straight lines for the collected image to frame the slider area, effectively suppressing the interference of the surrounding background and debris on the slider surface in the image, and accurately locating the slider area; then, by performing connected component extraction in the slider area, calculating the area of the connected component, the contrast and gray level fluctuation between the connected component and the surrounding area, it is judged whether the connected component is a defect area, and defects are detected within the slider area, improving the detection efficiency and accuracy. This method mainly relies on image recognition and segmentation technologies and may not be able to comprehensively cover all types of pantograph failures.

[0005] Patent CN117611525A discloses a vision detection method and system for pantograph slide wear. It completes the stitching of the captured images of the left and right pantographs by means of feature point calibration and matching, locates the pantograph slide area using an object detection algorithm, and combines image morphology processing and an improved edge detection algorithm to refine the extraction of the slide contour. Then, it converts the sub-pixel coordinates of the image into world coordinates and obtains the wear value by calculating the distance between the fitting lines of the upper contour and the lower contour, ensuring the accuracy and reliability of the calculation results and improving the accuracy of pantograph slide wear detection. However, this method requires high computing resources, poses high requirements on the hardware performance of on-vehicle equipment, and increases the system cost. Summary of the Invention

[0006] The object of the present invention is to provide a pantograph slide point cloud data stitching method and system based on a calibration block and an improved ICP algorithm, which has high accuracy in reconstructing the surface point cloud data of the pantograph slide, strong real-time performance, and can improve the detection accuracy and efficiency of the pantograph slide.

[0007] The technical solution for achieving the object of the present invention is: A pantograph slide point cloud data stitching method based on a calibration block and an improved ICP algorithm, comprising the following steps:

[0008] Step 1: Arrange a field acquisition module to scan the slide contour data in real time during the train operation;

[0009] Step 2: Use two groups of laser displacement sensors to continuously output multiple frames of valid data and expand it into three-dimensional point cloud data;

[0010] Step 3: Process the slide data using a denoising algorithm of hybrid filtering to obtain a slide contour data set;

[0011] Step 4: Use the slide contour data set to convert the point cloud data collected by the two groups of laser displacement sensors into the same coordinate system;

[0012] Step 5: Complete rough registration of the surface point cloud data of the slide using a data stitching method based on a calibration block;

[0013] Step 6: Use an improved ICP registration algorithm to complete the fine registration of the point cloud data and obtain the complete surface point cloud data of the slide.

[0014] A pantograph slide point cloud data stitching system based on a calibration block and an improved ICP algorithm, which is used to implement the pantograph slide point cloud data stitching method based on a calibration block and an improved ICP algorithm. The system includes a first unit to a sixth unit, and the functions of each unit are as follows:

[0015] The first unit scans the slide contour data in real time during the train operation through the field acquisition module;

[0016] The second unit continuously outputs multiple frames of valid data using two sets of laser displacement sensors and expands it into three-dimensional point cloud data;

[0017] The third unit processes the skateboard data using a denoising algorithm with hybrid filtering to obtain a skateboard contour data set;

[0018] The fourth unit uses the skateboard contour data set to convert the point cloud data collected by the two sets of laser displacement sensors into the same coordinate system;

[0019] The fifth unit completes rough registration of the point cloud data on the skateboard surface using a data stitching method based on a calibration block;

[0020] The sixth unit completes fine registration of the point cloud data using an improved ICP registration algorithm to obtain complete point cloud data of the skateboard surface.

[0021] Compared with the prior art, the significant advantages of the present invention are as follows: (1) While ensuring the accuracy of point cloud reconstruction on the pantograph skateboard surface, the real-time performance of the detection algorithm is improved; (2) A pantograph skateboard point cloud filtering algorithm based on slice contours is adopted, and the interference of non-pantograph skateboard data is filtered through hybrid filtering, improving the detection accuracy; (3) Combining a data fast-reading stitching method based on a calibration block and an improved ICP registration algorithm for fast and fine registration of the pantograph skateboard point cloud data, realizing fast and accurate stitching of the pantograph skateboard laser point cloud data. Brief Description of the Drawings

[0022] Figure 1 is a flowchart of a method for stitching pantograph skateboard point cloud data based on a calibration block and an improved ICP algorithm.

[0023] Figure 2 is a schematic diagram of the system equipment layout of the on-site acquisition module in an embodiment of the present invention.

[0024] Figure 3 is a schematic diagram of the sensor installation of the on-site acquisition module in an embodiment of the present invention.

[0025] Figure 4 is a schematic diagram of the change trend of valid data points in an embodiment of the present invention.

[0026] Figure 5 is a point cloud diagram after hybrid filtering and denoising of the first set of laser displacement sensors in an embodiment of the present invention.

[0027] Figure 6 is a point cloud diagram after hybrid filtering and denoising of the second set of laser displacement sensors in an embodiment of the present invention.

[0028] Figure 7 is an original data diagram collected by the two sets of laser displacement sensors in an embodiment of the present invention.

[0029] Figure 8 This is the transformation result diagram for rotating the data of the first group of laser displacement sensors to the coordinate system of the second group of laser displacement sensors in the embodiment of the present invention.

[0030] Figure 9 This is a schematic diagram of the skateboard surface point cloud data obtained by rough registration of the skateboard point cloud in the embodiment of the present invention.

[0031] Figure 10 This is a schematic diagram of the skateboard surface point cloud data obtained by fine registration of the skateboard point cloud in the embodiment of the present invention. Detailed implementation manners

[0032] As Figure 1 shown, a method for splicing the pantograph skateboard point cloud data based on a calibration block and an improved ICP algorithm in the present invention includes the following steps:

[0033] Step 1: Arrange on-site acquisition modules to scan the skateboard contour data in real time during the train operation;

[0034] Step 2: Use two groups of laser displacement sensors to continuously output multiple frames of valid data and expand it into three-dimensional point cloud data;

[0035] Step 3: Process the skateboard data using a denoising algorithm of hybrid filtering to obtain a skateboard contour data set;

[0036] Step 4: Utilize the skateboard contour data set to convert the point cloud data collected by the two groups of laser displacement sensors into the same coordinate system;

[0037] Step 5: Complete rough registration of the skateboard surface point cloud data using a data splicing method based on a calibration block;

[0038] Step 6: Complete fine registration of the point cloud data using an improved ICP registration algorithm to obtain complete skateboard surface point cloud data.

[0039] As a specific example, the arrangement of the on-site acquisition modules in Step 1 for scanning the skateboard contour data in real time during the train operation is as follows:

[0040] The on-site acquisition modules include a first group of laser displacement sensors, a second group of laser displacement sensors, a first group of photoelectric sensors, and a second group of photoelectric sensors, where:

[0041] The first group of laser displacement sensors and the second group of laser displacement sensors are installed on the same platform above the catenary. The coordinate axes of the two groups of laser displacement sensors are parallel to each other, and the laser detection surfaces are in the same plane, vertically downward collecting the upper surface contour of the skateboard; the upper surface of the skateboard is within the effective measurement range of the detection surfaces of the two groups of laser displacement sensors, and a set overlapping area is reserved;

[0042] The distance between the first group of optoelectronic sensors and the second group of optoelectronic sensors is , and the transmitting end and the receiving end of each group of optoelectronic sensors are respectively installed on both sides of the pantograph. The two groups of optoelectronic sensors are used to collect the skateboard speed in real time.

[0043] As a specific example, the continuous output of multiple frames of valid data by using two groups of laser displacement sensors in step 2 is expanded into three-dimensional point cloud data, which is specifically as follows:

[0044] Step 2.1: Use two groups of laser displacement sensors for continuous sampling and output multiple frames of valid data;

[0045] Step 2.2: Use two groups of optoelectronic sensors to collect the skateboard speed in real time, combine the sampling frequency to realize the acquisition of the third-dimensional data. Take the train forward direction as the Y axis, expand the two-dimensional contour data into three-dimensional point cloud data. Multiple groups of three-dimensional contour point clouds form the upper surface of the skateboard. Calculate the frame spacing according to the following formula Calculation:

[0046]

[0047] In the formula, , are respectively the moments when the skateboard passes through the first group of optoelectronic sensors and the second group of optoelectronic sensors, is the sampling frequency of the first group of laser displacement sensors and the second group of laser displacement sensors.

[0048] As a specific example, the denoising algorithm using hybrid filtering in step 3 is used to process the skateboard data to obtain the skateboard contour data set, which is specifically as follows:

[0049] Step 3.1: The collected data includes target point cloud data and non-target point cloud data. The skateboard point cloud data is composed of multiple groups of two-dimensional scanning contours. Each contour is equivalent to a slice of the point cloud data. Transfer the filtering of the three-dimensional point cloud data to the two-dimensional space to obtain the slice contour data;

[0050] Step 3.2: Use the denoising algorithm using hybrid filtering to filter the slice contour data, filter out the non-target point cloud data, and obtain the skateboard contour data set.

[0051] As a specific example, the denoising algorithm using hybrid filtering in step 3.2 is used to filter the slice contour data, filter out the non-target point cloud data, and obtain the skateboard contour data set, which is specifically as follows:

[0052] Step 3.2.1: Sequentially assign consecutive numbers to all the collected data frames according to the sampling moments. During the detection process, there are two peaks in the curve of the effective number of collected points output by the laser displacement sensor, corresponding to the effective number of points of the two sliding plates of the pantograph passing through the detection area. The effective data frames of the sliding plates are screened by determining whether the effective number of data points output by the laser displacement sensor is greater than a fixed threshold.

[0053] Step 3.2.2: Use a radius filter to filter out discrete points. It is determined whether a point is an effective point by comparing whether the number of points within the detection radius of each data point in the point cloud data is less than the set threshold. If it is less than the threshold, it is identified as an outlier and the outlier is removed; otherwise, it is determined as an effective point.

[0054] Step 3.2.3: Calculate the Euclidean distance between two adjacent data points in the point cloud data, and compare this Euclidean distance with the threshold to segment the point cloud data: If it is less than the threshold, add the subsequent data points to the current same data point set; if it is greater than the threshold, use the current data point as the segmentation point, search backward from the adjacent data points, and take the largest data point set as the sliding plate contour data set.

[0055] As a specific example, for the step 4 of using the sliding plate contour data set to convert the point cloud data collected by two groups of laser displacement sensors into the same coordinate system, the specific method is as follows:

[0056] Step 4.1: The coordinate systems of the first group of laser displacement sensors and the second group of laser displacement sensors are respectively and , let the position coordinates of the point in be . After rotation and translation transformation, the position coordinates of the point in are , then there is:

[0057]

[0058] In the formula, is a 3 3 rotation matrix, is the translation vector, is the component of the translation vector on the axis, axis, axis;

[0059] Rotate the coordinate system around the axis, axis, axis by the rotation angles , , The obtained rotation matrix is:

[0060]

[0061]

[0062]

[0063] Combining the three rotation matrices to obtain the rotation transformation matrix axis, axis, axis, respectively rotated by , , angles, as follows: , as shown in the following formula:

[0064]

[0065] Step 4.2, Solve the specific values of the parameters , , , , and so that the data collected by the two sets of laser displacement sensors can be stitched to obtain the complete point cloud data of the skateboard surface.

[0066] As a specific example, the data stitching method based on the calibration block for the point cloud data of the skateboard surface described in Step 5 is completed as follows:

[0067] Step 5.1, Use a rectangular metal calibration block with dimensions of to calibrate the spatial position relationship of the coordinate system. Place the calibration block within the effective measurement area of the laser displacement sensor, and adjust the installation parameters of the laser displacement sensor according to the positions of the laser contour lines emitted by the two sets of laser displacement sensors on the calibration block until the two laser contour lines coincide; , , represent the length, width, and height of the calibration block respectively;

[0068] The angles of the coordinate system around the axis and the axis are close to 0, and at the same time, the translation parameter in the axis direction is also close to 0, that is, the parameters , and are approximately 0, reducing the registration parameters to , and ;

[0069] Step 5.2: Using the coordinate system of the second group of laser displacement sensors as the target coordinate system, rotate and translate the coordinate system of the first group of laser displacement sensors to . The conversion formula is:

[0070]

[0071] Project it onto plane, then there is:

[0072]

[0073] Solve for the parameters , and , that is, complete the splicing of the skateboard data based on the calibration block;

[0074] Step 5.3: Calculate the rotation angle according to the feature that the two laser lines irradiate on the same straight line of the calibration block. Perform linear fitting on the data collected by the two groups of laser displacement sensors. During the calibration process, the straight lines fitted by the detection data of the two groups of laser displacement sensors are respectively , . The slopes of the straight lines are respectively , . The inclination angles are respectively , . Then the rotation angle is:

[0075]

[0076] When the laser irradiates on the same straight line of the calibration block, and each of the two groups of laser displacement sensors detects a boundary point of the calibration block. During the calibration process, the coordinates of the boundary point of the calibration block in the coordinate systems of the first group and the second group of laser displacement sensors are respectively , . Rotate the rotation angle . The rotated coordinates of the point are . The conversion formula is:

[0077]

[0078] After the coordinate system of the first group of laser displacement sensors is rotated, the coordinate axes are parallel to those of the second group of laser displacement sensors, but there is a spatial position difference at the coordinate origin. According to the feature of the fixed length of the calibration block, the translation distance of the coordinate origin in the axis direction and the translation distance in the Perform calculations, and the calculation formula is as follows:

[0079]

[0080] To improve the rough registration accuracy and reduce the influence of random errors, repeat steps 5.3 to 5.4 for multiple calibrations, and take the mean value as the final calibration value.

[0081] As a specific example, the improved ICP registration algorithm described in step 6 is used to complete the fine registration of the point cloud data to obtain the complete point cloud data of the skateboard surface, specifically as follows:

[0082] Step 6.1: The data of the first group of laser displacement sensors constitute the source point cloud data set , and the data of the second group of laser displacement sensors constitute the target point cloud data set , represents the three-dimensional real number set, and respectively represent the sizes of the two point cloud data sets corresponding to the first and second groups of laser displacement sensors, , respectively represent the data point numbers in the two point cloud data sets corresponding to the first and second groups of laser displacement sensors; the source point cloud data set and the target point cloud data set find matching point pairs according to the set constraint conditions , represents the matching point pair number;

[0083] Use the least squares algorithm to calculate the optimal rotation matrix , translation matrix , so that the registration error function is minimized, and the error function is defined as follows:

[0084]

[0085] In the formula, , is the number of matching point pairs;

[0086] The ultimate goal of point cloud registration is to solve the rotation matrix and translation matrix so that the two groups of laser point clouds after registration form the complete point cloud of the skateboard surface;

[0087] First, calculate the centroids and of , , and , the covariance matrix of

[0088]

[0089]

[0090] Then, for the covariance matrix perform singular value decomposition , and is a 3×3 orthogonal matrix, is a non - negative diagonal matrix composed of the eigenvalues of the covariance matrix. Then the rotation matrix and the translation matrix are calculated as follows:

[0091]

[0092] Step 6.2: Since there must be an inclusion relationship between the target point cloud and the source point cloud, and the judgment of corresponding points depends on the Euclidean distance, which may lead to wrong matches or getting stuck in local minimum problems. Combining the characteristics of the pantograph slider and the sensor scanning characteristics, the ICP algorithm is optimized from three aspects: matching point set, wrong point removal, and error function construction, to improve the matching speed and accuracy, as follows:

[0093] Step 6.2.1: Matching point set: Calculate the overlapping area of the two groups of laser point clouds, and use the data in the overlapping area for registration, instead of making all points participate in the operation;

[0094] The two groups of laser point clouds are partially overlapping. Incorporating the data in the non - overlapping area into the matching process will not only greatly increase the computational complexity of the algorithm but also provide wrong matching point pairs that affect the registration accuracy of the algorithm. Therefore, extract the overlapping area of the two groups of point clouds for point cloud registration to reduce the computational complexity of the algorithm. Set the minimum value of the first group of laser displacement sensor point cloud data in the axis direction as , and the maximum value of the second group of laser displacement sensor point cloud data in the axis direction as . Then the overlapping area in the X - axis direction of the two is ;

[0095] Step 6.2.2: Wrong point removal: In actual situations, due to the influence of the slider structure characteristics and noise points, there will inevitably be wrong matching point pairs. Sort all the matching point pairs in ascending order of distance, and then use the fixed - ratio method to retain the first point pairs for the calculation of the transformation matrix, , and remove the remaining point pairs; where is the set fixed ratio, is the number of retained point pairs;

[0096] Step 6.2.3, Error function construction: Optimize the error function selection strategy in the iterative stage. Combine the point-to-point error function and the point-to-plane error function. The construction method of the point-to-point error function means constructing the error function with the sum of the squares of the distances between all corresponding points in two point clouds. The construction method of the point-to-plane error function means constructing the error function with the sum of the squares of the distances from the points in the source point cloud to the tangent planes of their corresponding points. In the previous multiple iterations where the error is greater than the threshold, select the point-to-point error function, and in the subsequent iterations, select the point-to-plane error function;

[0097] Point-to-plane error function As shown in the following formula:

[0098]

[0099] In the formula, is the point in , is the three-axis coordinate component of ; is the point in , is the three-axis coordinate component of ; is the normal vector of , , is the three-axis coordinate component of ; is the rigid body transformation matrix of , as shown in the following formula:

[0100]

[0101] When the rotation angle approaches 0, the point-to-plane error function is approximately optimized into a linear least squares problem. Therefore, when , , approach 0, the rotation matrix is approximately :

[0102]

[0103] The rigid body transformation matrix is approximately expressed as :

[0104]

[0105] The point-to-plane error function is approximately expressed as :

[0106]

[0107] For each point pair in the above formula can all be written as an expression containing parameters , , , , and :

[0108]

[0109] For the matching point pairs, the following expressions are obtained:

[0110]

[0111] where

[0112]

[0113]

[0114]

[0115] and are constant terms; in , , , ;

[0116] The optimal solution of the transformation parameters is:

[0117]

[0118] The above formula is a standard linear least squares problem, which is solved by SVD.

[0119] The present invention also provides a pantograph slider point cloud data stitching system based on a calibration block and an improved ICP algorithm. This system is used to implement the pantograph slider point cloud data stitching method based on the calibration block and the improved ICP algorithm. The system includes a first unit to a sixth unit, and the functions of each unit are as follows:

[0120] The first unit, through the on-site acquisition module, scans the slider contour data in real time during the train operation;

[0121] The second unit, uses two groups of laser displacement sensors to continuously output multiple frames of valid data and expands it into three-dimensional point cloud data;

[0122] The third unit processes the skateboard data using a denoising algorithm with hybrid filtering to obtain a skateboard contour data set;

[0123] The fourth unit uses the skateboard contour data set to transform the point cloud data collected by two groups of laser displacement sensors into the same coordinate system;

[0124] The fifth unit completes rough registration of the skateboard surface point cloud data using a data stitching method based on a calibration block;

[0125] The sixth unit uses an improved ICP registration algorithm to complete fine registration of the point cloud data and obtains the complete skateboard surface point cloud data.

[0126] The following further elaborates on the present invention in conjunction with the accompanying drawings and specific embodiments.

[0127] Embodiment

[0128] Combined with Figure 1 , a method for stitching pantograph skateboard point cloud data based on a calibration block and an improved ICP algorithm provided in this embodiment includes the following steps:

[0129] Step 1: Arrange the on-site acquisition module to scan the skateboard contour data in real time during train operation, specifically as follows:

[0130] Arrange the on-site acquisition module, including two groups of 2D laser displacement sensors and two groups of photoelectric sensors. The 2D laser displacement sensors are of the Keyence LJ-X890 type, and the photoelectric sensors are of the Keyence PZ-G52CP photoelectric sensors;

[0131] The two groups of 2D laser displacement sensors are installed symmetrically about the center line of the rail and collect skateboard data vertically downward. The installation schematic is as shown in Figure 2 、 Figure 3 . The two groups of 2D laser displacement sensors are installed above the contact wire and collect the upper surface contour of the skateboard vertically downward. The laser detection surfaces of the two groups of 2D laser displacement sensors are in the same plane. The upper surface of the skateboard is within the effective measurement range of the laser detection surface, and a certain overlapping area is reserved. Moreover, the two groups of laser sensors are installed on the same platform, and the coordinate axes are parallel to each other; the distance between the two groups of photoelectric sensors is d. The transmitting end and receiving end of the photoelectric sensors are respectively installed on both sides of the pantograph, and two groups of photoelectric sensors are used to collect the real-time speed of the skateboard.

[0132] Step 2: Use the laser displacement sensor to continuously output multiple frames of valid data and expand it into three-dimensional point cloud data, specifically as follows:

[0133] Step 2.1: Use the laser displacement sensor for continuous sampling and output multiple frames of valid data;

[0134] Step 2.2: Use a photoelectric sensor to collect the skateboard speed in real time, combine the collection frequency to achieve the collection of the third-dimensional data. Take the train's forward direction as the Y-axis, expand the two-dimensional contour data into three-dimensional point cloud data. Multiple groups of three-dimensional contour point clouds form the upper surface of the skateboard. Calculate the frame spacing according to the following formula: Calculation:

[0135]

[0136] In the formula, is the distance between the first group of photoelectric sensors and the second group of photoelectric sensors, , are the moments when the skateboard passes through the first group of photoelectric sensors and the second group of photoelectric sensors respectively, is the collection frequency of the first group of laser displacement sensors and the second group of laser displacement sensors.

[0137] Step 3: Use a denoising algorithm of hybrid filtering to process the skateboard data to obtain a skateboard contour data set, specifically as follows:

[0138] Step 3.1: The data collected by the system includes point cloud target data and non-target point cloud data. The skateboard point cloud data consists of multiple groups of two-dimensional scanning contours. Each contour is equivalent to a slice of the point cloud. Transfer the filtering of the three-dimensional point cloud data to the two-dimensional space and perform filtering processing on the slice contour data;

[0139] Step 3.2: Use a denoising algorithm of hybrid filtering to process the skateboard data, filter out the non-target object point cloud data, and obtain a skateboard contour data set, specifically as follows:

[0140] Step 3.2.1: Take the single-sided sensor system as an example. When a complete pantograph enters the detection area and then leaves the detection area, the change trend of the number of valid points output by the laser displacement sensor is as Figure 4 shown. When the pantograph is not in the measurement area, the number of valid data points output by the laser displacement sensor is 0; when the pantograph enters the measurement area, the number of valid data points output by the laser displacement sensor will increase significantly. During the detection process, there are two peaks in the curve of the number of valid acquisition points output by the laser displacement sensor, corresponding to the number of valid data points when the two skateboards of the pantograph pass through the detection area;

[0141] Step 3.2.2: Use a radius filter to remove discrete points. The principle of radius filtering is that each data point in the point cloud has at least a specified number of neighboring points in a specified radius neighborhood. By comparing whether the number of points within its detection radius of each point is less than the set threshold to determine whether it is a valid point. If it is less than the threshold, it is identified as an outlier and removed; otherwise, it is determined as a valid point;

[0142] Step 3.2.3: There is a certain gap between the pantograph and the skateboard assembly. The laser displacement sensor is installed vertically downward, and the value of the gap data in the Z-axis direction has a certain amplitude mutation compared with the data at other positions. Based on this feature, the data points are segmented.

[0143] Start searching in order from the starting point and calculate the Euclidean distance between adjacent two points , and compare it with the threshold . If it meets , then add the subsequent points to the same point set ; if , then use the point as the segmentation point, and the next point set starts searching from as the starting point. After the data segmentation is completed, count the number of data points in each point set , and take the point set with the largest number of data points as the skateboard data. The filtered data is as shown in Figure 5 、 Figure 6 .

[0144] Step 4: Using the skateboard contour data set, convert the two sets of laser point cloud data collected by the two laser displacement sensors into the same coordinate system to form the complete skateboard surface point cloud data, specifically as follows:

[0145] Step 4.1: The coordinate systems of the first group of laser displacement sensors and the second group of laser displacement sensors are and respectively. Let the position coordinates of the point in be . After rotation and translation transformation, the position coordinates of the point in are , then there is:

[0146]

[0147] In the formula, is a 3 3 rotation matrix, is the translation vector, is the component of the translation vector on the axis, axis, axis;

[0148] Rotate the coordinate system around the axis, axis, axis by the rotation angle , , The obtained rotation matrix is:

[0149]

[0150]

[0151]

[0152] Combining the three rotation matrices to obtain the rotation transformation matrix axis, axis, axis, respectively rotated , , angle, as follows: The following formula:

[0153]

[0154] Step 4.2, solve the specific values of the parameters , , , , and to splice the data collected by the two sets of laser displacement sensors to obtain the complete point cloud data of the skateboard surface.

[0155] Step 5. Coarse registration of the point cloud data of the skateboard surface is completed by using the data fast splicing method based on the calibration block, specifically as follows:

[0156] Step 5.1. Calibrate the space of the two sets of laser displacement sensors with a calibration block of size ;

[0157] Step 5.2. Place the calibration block in the effective measurement area of the system. First, finely adjust the position of the sensor according to the laser contour line to make the two laser contour lines coincide. The original data collected by the laser displacement sensor is as shown in Figure 7 . Linearly fit the surface contour of the calibration block and extract the coordinate values of the two side endpoints to obtain the rough registration parameters , , , of the sensor. Rotate the data of the first set of laser displacement sensors to the coordinate system of the second set of laser displacement sensors. The transformation result is shown in Figure 8 .

[0158] Step 5.3: Based on the rough registration parameters of the sensors, rotate and transform the surface point cloud collected by the first group of laser displacement sensors to the coordinate system of the second group of laser displacement sensors to complete the rough registration of the skateboard point cloud. The rough registration result is as shown in Figure 9 as follows.

[0159] Step 6: Use the improved ICP registration algorithm to complete the fast fine registration of the point cloud data and obtain the complete skateboard surface point cloud data, which is specifically as follows:

[0160] Step 6.1: Select the minimum value of the first group of laser point cloud data in the axis direction as , and the maximum value of the second group of laser point cloud data in the axis direction as . The area in the X-axis direction of the two is used as the overlapping area of the two groups of point clouds;

[0161] Step 6.2: Sort all the matched points according to the distance according to Step 6.1, and then use the fixed ratio method to remove a part of the point pairs with too large distances. The remaining point pairs are used for the calculation of the transformation matrix.

[0162] Step 6.3: Combine the "point-to-point" and "point-to-plane" error functions, set the rotation angle between the two groups of point clouds to be very small, and approximate and optimize the formula into a linear least squares problem. The registration visual effect is as shown in Figure 10 as follows.

[0163] It can be seen from the figure that the present invention adopts the pantograph skateboard point cloud filtering algorithm based on the slice contour, filters out the interference of non-pantograph skateboard data through hybrid filtering, combines the data fast reading and stitching method based on the calibration block and the improved ICP registration algorithm for the fast fine registration of the pantograph skateboard point cloud data, improves the efficiency of point cloud data stitching at the same time, and enhances the real-time performance of the detection algorithm.

[0164] The above is only a preferred embodiment of the present invention, and does not impose any form of limitation on the present invention. Therefore, any modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for stitching pantograph slide point cloud data based on calibration block and improved ICP algorithm, characterized in that: The following steps are involved: Step 1: Arrange the on-site acquisition module to scan the skateboard profile data in real time during the train operation; Step 2: Use two sets of laser displacement sensors to continuously output multiple frames of valid data and expand them into three-dimensional point cloud data; Step 3, using a hybrid filtering denoising algorithm to process the skateboard data to obtain a skateboard contour data set; Step 4: Using the skateboard contour data set, transform the point cloud data collected by the two sets of laser displacement sensors into the same coordinate system; Step 5: Coarse registration of the skateboard surface point cloud data is completed using a data stitching method based on calibration blocks; Step 6: Use the improved ICP registration algorithm to complete the precise registration of the point cloud data and obtain the complete skateboard surface point cloud data.

2. The pantograph slide point cloud data splicing method based on calibration block and improved ICP algorithm according to claim 1 is characterized in that: The arrangement of the on-site acquisition module described in step 1 scans the skateboard profile data in real time during the train operation, as follows: The field acquisition module includes a first group of laser displacement sensors, a second group of laser displacement sensors, a first group of photoelectric sensors, and a second group of photoelectric sensors, wherein: The first group of laser displacement sensors and the second group of laser displacement sensors are installed on the same platform above the contact line. The coordinate axes of the two groups of laser displacement sensors are parallel to each other, and the laser detection surfaces are in the same plane. The upper surface contour of the slide plate is collected vertically downward. The upper surface of the slide plate is within the effective range of the detection surfaces of the two groups of laser displacement sensors, and the set overlapping area is retained. The distance between the first group of photoelectric sensors and the second group of photoelectric sensors is The transmitting end and receiving end of each group of photoelectric sensors are installed on both sides of the pantograph respectively. The two groups of photoelectric sensors are used to collect the skateboard speed in real time.

3. The pantograph slide point cloud data splicing method based on calibration block and improved ICP algorithm according to claim 2 is characterized in that: The two sets of laser displacement sensors described in step 2 continuously output multiple frames of valid data and expand them into three-dimensional point cloud data, as follows: Step 2.1, use two sets of laser displacement sensors to perform continuous sampling and output multiple frames of valid data; Step 2.2, use two sets of photoelectric sensors to collect the speed of the skateboard in real time, combine the acquisition frequency to realize the acquisition of the third dimension data, take the train forward direction as the Y axis, expand the two-dimensional contour data into three-dimensional point cloud data, and multiple sets of three-dimensional contour point cloud data constitute the upper surface of the skateboard. The frame spacing is calculated according to the following formula: calculate: ； In the formula, , are the moments when the skateboard passes through the first set of photoelectric sensors and the second set of photoelectric sensors, respectively. is the acquisition frequency of the first group of laser displacement sensors and the second group of laser displacement sensors.

4. The pantograph slide point cloud data splicing method based on calibration block and improved ICP algorithm according to claim 3 is characterized in that: The denoising algorithm using hybrid filtering described in step 3 is used to process the skateboard data to obtain a skateboard contour data set, which is as follows: Step 3.1, the collected data includes target point cloud data and non-target point cloud data. The skateboard point cloud data is composed of multiple sets of two-dimensional scanning contours. Each contour is equivalent to a slice of the point cloud data. The filtering of the three-dimensional point cloud data is transferred to the two-dimensional space to obtain the slice contour data; Step 3.2: Use a hybrid filtering denoising algorithm to filter the slice contour data, filter out non-target point cloud data, and obtain the skateboard contour data set.

5. The pantograph slide point cloud data splicing method based on calibration block and improved ICP algorithm according to claim 4 is characterized in that: The hybrid filtering denoising algorithm described in step 3.2 is used to filter the slice contour data, filter out non-target point cloud data, and obtain the skateboard contour data set, as follows: Step 3.2.1, serially number all collected data frames according to the sampling time. During the detection process, there are two peaks in the effective collection point curve output by the laser displacement sensor, which correspond to the effective data points of the two slides of the pantograph passing through the detection area. The effective data frames of the slides are screened by judging whether the effective data points output by the laser displacement sensor are greater than a fixed threshold; Step 3.2.2: Use a radius filter to filter out discrete points. By comparing the number of points within the detection radius of each data point in the point cloud data to see if it is less than the set threshold, it is determined whether it is a valid point. If it is less than the threshold, it is confirmed as an outlier and the outlier is removed. Otherwise, it is determined to be a valid point. Step 3.2.3, calculate the Euclidean distance between two adjacent data points in the point cloud data, compare the Euclidean distance with the threshold, and segment the point cloud data: if it is less than the threshold, add the subsequent data points to the current set of the same data points; if it is greater than the threshold, use the current data point as the segmentation point, search backward from the adjacent data points, and use the largest set of data points as the skateboard contour data set.

6. The method for stitching pantograph slide point cloud data based on calibration block and improved ICP algorithm according to claim 5, characterized in that: The point cloud data collected by the two sets of laser displacement sensors are converted into the same coordinate system using the skateboard contour data set described in step 4, as follows: Step 4.1: The coordinate systems of the first group of laser displacement sensors and the second group of laser displacement sensors are and , set point exist The position coordinates in are , after rotation and translation transformation, point exist The position coordinates in are , then: ； In the formula, For 3 3's rotation matrix, is the translation vector, is the translation vector axis, axis, The weight of the axis; Will The coordinate system revolves around axis, axis, Axis rotation angle , , The resulting rotation matrix is: ； ； ； Combine the three rotation matrices to get axis, axis, Axis rotation , , The rotation transformation matrix after the angle , as follows: ； Step 4.2: Solve the parameters , , , , and The specific value of is used to stitch the data collected by the two sets of laser displacement sensors to obtain complete point cloud data of the skateboard surface.

7. The method for stitching pantograph slide point cloud data based on calibration block and improved ICP algorithm according to claim 6, characterized in that: The rough registration of the skateboard surface point cloud data described in step 5 is completed by using the data stitching method based on the calibration block, as follows: Step 5.1, use the size The spatial position relationship of the coordinate system is calibrated by a rectangular metal calibration block, and the calibration block is placed in the effective measurement area of ​​the laser displacement sensor. The installation parameters of the laser displacement sensor are adjusted at the position of the calibration block through the laser contour lines produced by the two groups of laser displacement sensors, and finally adjusted to make the two laser contour lines coincide with each other; , , Respectively represent the length, width and height of the calibration block; Coordinate system around Axis and The angle of the axis is close to 0, and at The translation parameter in the axis direction is also close to 0, that is, the parameter , and Approximately 0, reducing the registration parameters to , and ; Step 5.2: Use the coordinate system of the second set of laser displacement sensors As the target coordinate system, the coordinate system of the first group of laser displacement sensors Rotate and translate to , the conversion formula is: ； Project to Plane, then: ； Solving Parameters , and , that is, completing the skateboard data splicing based on the calibration block; Step 5.3: Realize the rotation angle based on the features of two laser lines irradiating the same straight line on the calibration block The data collected by the two sets of laser displacement sensors are linearly fitted according to the calculation. During the calibration process, the straight lines fitting the detection data of the two sets of laser displacement sensors are , The slopes of the straight lines are , The tilt angles are , , then the rotation angle for: ； Step 5.4: Irradiate the laser on the same straight line of the calibration block, and the two groups of laser displacement sensors each detect a boundary point of the calibration block. During the calibration process, the coordinates of the boundary points of the calibration block in the first and second groups of laser displacement sensor coordinate systems are respectively , , the rotation angle ,point The coordinates after rotation are , the conversion formula is: ； After the first set of laser displacement sensor coordinate systems rotate, the coordinate axes are parallel to the second set of laser displacement sensor coordinate axes, but there is a spatial position difference between the coordinate origins. According to the fixed length feature of the calibration block, the coordinate origin is Translation distance in the axis direction and Translation distance in the axis direction Calculate, the calculation formula is: ； Repeat steps 5.3 to 5.4 for multiple calibrations, and take the average as the final calibration value.

8. The method for stitching pantograph slide point cloud data based on calibration block and improved ICP algorithm according to claim 7, characterized in that: The improved ICP registration algorithm described in step 6 is used to complete the precise registration of the point cloud data to obtain the complete skateboard surface point cloud data, as follows: Step 6.1: The data of the first group of laser displacement sensors constitute the source point cloud dataset , the data of the second group of laser displacement sensors constitute the target point cloud dataset , represents the three-dimensional set of real numbers, and Respectively represent the sizes of the two point cloud data sets corresponding to the first and second groups of laser displacement sensors, , Respectively represent the data point numbers in the two point cloud datasets corresponding to the first and second groups of laser displacement sensors; source point cloud dataset And the target point cloud dataset Find matching point pairs according to the set constraints , Indicates the matching point pair number; Calculate the optimal rotation matrix using the least squares algorithm , translation matrix , so that the registration error function is minimized. The error function is defined as follows: ； In the formula, , is the number of matching point pairs; The ultimate goal of point cloud registration is to solve the rotation matrix and translation matrix , so that the two groups of registered laser point clouds constitute a complete skateboard surface point cloud; First, calculate and The centroid , ,as well as , The covariance matrix of : ； ； Then, the covariance matrix Perform singular value decomposition , , is a 3×3 orthogonal matrix, is a nonnegative diagonal matrix consisting of the eigenvalues ​​of the covariance matrix, then the rotation matrix and translation matrix The calculation formula is: ； Step 6.2: Optimize the ICP algorithm from three aspects: matching point set, error point removal, and error function construction.

9. The method for stitching pantograph slide point cloud data based on calibration block and improved ICP algorithm according to claim 8, characterized in that: Step 6.2 describes the optimization of the ICP algorithm from three aspects: matching point set, error point removal, and error function construction. The details are as follows: Step 6.2.1, matching point set: calculate the overlapping area of ​​the two sets of laser point clouds and use the overlapping area data for registration; Step 6.2.2, error point removal: remove all matching points Sort by distance from small to large, and then use the fixed ratio method to retain the top Point pairs are used to calculate the transformation matrix. , the remaining point pairs are removed; To set a fixed ratio, To retain the number of point pairs; Step 6.2.3, error function construction: optimize the error function selection strategy in the iteration stage, combine the point-to-point error function and the point-to-surface error function. The point-to-point error function construction method refers to constructing the error function using the square sum of the distances between all corresponding points in two point clouds, and the point-to-surface error function construction method refers to constructing the error function using the square sum of the distances from a point in the source point cloud to the tangent plane of its corresponding point. In the first multiple iterations when the error is greater than the threshold, the point-to-point error function is selected, and the point-to-surface error function is selected in the subsequent iterations. Point-to-surface error function As shown below: ； In the formula, for The point in for The three-axis coordinate components of For point exist The corresponding points in for The three-axis coordinate components of for The normal vector of , for The three-axis coordinate components of for The rigid body transformation matrix is ​​as follows: ； When the rotation angle approaches 0, the point-to-surface error function is approximately optimized as a linear least squares problem. , , When it approaches 0, the rotation matrix Approximately : ； Rigid body transformation matrix Approximately expressed as : ； Point-to-surface error function Approximately expressed as : ； In the above formula, each point pair can be written as a parameter , , , , and The expression is: ； for For the matching point pairs, we get the following expression: ； in ； ； ； and is a constant term; In , , , ; Optimal solution for transformation parameters for: ； The above formula is a standard linear least squares problem, which is solved by SVD.

10. A pantograph slide point cloud data splicing system based on calibration block and improved ICP algorithm, characterized in that: The system is used to implement the pantograph slide point cloud data splicing method based on the calibration block and the improved ICP algorithm as described in any one of claims 1 to 9. The system includes the first unit to the sixth unit, and the functions of each unit are as follows: The first unit, through the on-site acquisition module, scans the skateboard profile data in real time during the train operation; The second unit uses two sets of laser displacement sensors to continuously output multiple frames of valid data and expand them into three-dimensional point cloud data; In the third unit, the skateboard data is processed using a hybrid filtering denoising algorithm to obtain a skateboard contour data set; Unit 4: Using the skateboard contour data set, the point cloud data collected by two sets of laser displacement sensors are converted into the same coordinate system; Unit 5: The rough registration of the skateboard surface point cloud data is completed using the calibration block-based data stitching method; In Unit 6, the improved ICP registration algorithm is used to complete the precise registration of point cloud data and obtain the complete point cloud data of the skateboard surface.

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