3D scrolling centering method and system based on point cloud fitting, and readable storage medium

Abstract

The invention relates to the field of intelligent positioning or fixing of workpieces, in particular to a 3D coiling centering method and system based on point cloud fitting and a readable storage medium. The 3D scrolling centering method based on point cloud fitting comprises the following steps: acquiring laser radar point cloud data and laser radar inertial sensor data; judging whether the equipment is in a detectable state or not according to the laser radar point cloud data and the laser radar inertial sensor data; when the equipment is in a detectable state, projecting the laser radar point cloud data to the reference surface to obtain a steel coil end face image; extracting the center height of an inner circle in the steel coil end face image; and determining a centering result according to the calibrated centering height and the center height of the inner circle. Compared with image recognition, more comprehensive and accurate data can be obtained through steel coil end face detection; the laser radar is used for data acquisition, so that the problems can be avoided, and errors and deviations of data are reduced.

Description

Technical Field

[0001] The present invention relates to the field of intelligent positioning or fixing of workpieces, and in particular to a 3D roll-up centering method and device based on point cloud fitting. Background Art

[0002] Before unwinding, the coil loading trolley needs to carry the steel coil to the correct height so that the steel coil can be moved horizontally and hung on the mandrel of the unwinding machine. Since the mandrel radius and the inner diameter of the steel coil are relatively small, correct coiling requires accurate calculation of the centering height. If the centering height deviation is large, the end face of the steel coil will collide with the mandrel head during coiling, and the coiling cannot be carried out normally, or even cause a safety accident. Due to the needs of unmanned safety production, and the height of the unwinding mandrel makes it difficult for workers to estimate the height to be supported, coil centering urgently needs an automated and accurate positioning solution.

[0003] The existing technology usually uses image recognition to align the position of the core shaft. During the image acquisition process, image recognition is used to align the position of the core shaft, which has high requirements on environmental stability, is sensitive to numerical values ​​and is prone to fluctuations. In addition, the factory environment of the winding position cannot guarantee a stable photoelectric and vibration environment, resulting in low accuracy. Summary of the invention

[0004] In order to overcome the above technical problems, the present application provides a 3D roll-up centering method and device based on point cloud fitting.

[0005] According to the first aspect of the present application, a 3D roll centering method based on point cloud fitting is provided, the method comprising: acquiring laser radar point cloud data and laser radar inertial sensor data; judging whether the device is in a detectable state according to the laser radar point cloud data and the laser radar inertial sensor data; when the device is in a detectable state, projecting the laser radar point cloud data onto a reference plane to acquire an image of the end face of the steel coil; extracting the height of the center of the inner circle in the image of the end face of the steel coil; and determining the centering result according to the calibrated centering height and the height of the center of the inner circle.

[0006] In a further scheme of the present application, whether the device is in a detectable state is determined based on the lidar point cloud data and the lidar inertial sensor data, including: calculating the lidar deflection angle based on the lidar inertial sensor data and the calibrated inertial reference solution vector cosine angle; performing a threshold judgment on the lidar deflection angle and a preset maximum deflection angle threshold; and determining that the device is in a detectable state when the lidar deflection angle is less than the maximum deflection angle threshold.

[0007] In a further scheme of the present application, whether the device is in a detectable state is determined based on the lidar point cloud data and the lidar inertial sensor data, including: clustering the lidar point cloud data to obtain steel coil point cloud data; segmenting the plane in the steel coil point cloud data to obtain the steel coil end face plane equation; extracting the Z-axis height from the steel coil end face plane equation; subtracting the Z-axis height from the preset reference plane Z-axis height to obtain the device offset; performing threshold judgment on the device offset and the preset maximum Z offset; and determining that the device is in a detectable state when the device offset is less than the maximum Z offset.

[0008] In a further scheme of the present application, the height of the center of the inner circle is extracted in the steel coil end face image, including: determining a two-dimensional contour point set of the steel coil end face in the steel coil end face image; fitting an ellipse in the two-dimensional contour point set of the steel coil end face to determine an outer circle contour point set and an inner circle contour point set in the two-dimensional contour point set of the steel coil end face; filtering the outer circle contour in the two-dimensional contour point set of the steel coil end face and retaining the inner circle contour point set; obtaining the inner circle parameters of the steel coil according to the inner circle contour point set, and extracting the height of the center of the inner circle from the inner circle parameters of the steel coil.

[0009] In a further scheme of the present application, the centering result is determined according to the calibrated centering height and the inner circle center height, including: determining the height deviation by calibrating the centering height and the inner circle center height; judging whether the height deviation is within a preset maximum height deviation threshold; determining that the centering is normal when the height deviation is less than the maximum height deviation threshold; determining that the centering is abnormal when the height deviation is greater than the maximum height deviation threshold.

[0010] In a further scheme of the present application, an ellipse is fitted in the two-dimensional contour point set of the steel coil end face to determine the outer circle contour point set and the inner circle contour point set in the two-dimensional contour point set of the steel coil end face, including: converting the two-dimensional contour point set of the steel coil end face into a polar coordinate system centered on the origin of the coordinate system; using the least squares method to fit the ellipse to the two-dimensional contour point set of the steel coil end face in the polar coordinate system, and obtaining the ellipse model parameters fitted by the least squares method through a numerical optimization algorithm, wherein the ellipse model parameters include the center of the ellipse; and dividing the points in the two-dimensional contour point set of the steel coil end face into an outer circle contour point set and an inner circle contour point set according to their distance from the center of the ellipse.

[0011] In a further scheme of the present application, an ellipse is fitted in the two-dimensional contour point set of the steel coil end face to determine the outer circle contour point set and the inner circle contour point set in the two-dimensional contour point set of the steel coil end face, including: initializing the ellipse model parameters; for each point in the two-dimensional contour point set of the steel coil end face, calculating its distance to the ellipse model and recording the minimum distance point; updating the ellipse model parameters according to the minimum distance point until the two-dimensional contour point set of the steel coil end face is traversed to obtain the final ellipse model parameters; dividing the points in the two-dimensional contour point set of the steel coil end face into an outer circle contour point set and an inner circle contour point set according to their distance from the center of the ellipse.

[0012] In a further solution of the present application, in case of abnormal centering, the steel coil is moved by height deviation for fine adjustment; and the centering test is performed again until the centering of the steel coil is normal.

[0013] The second aspect of the present application provides a 3D roll centering device based on point cloud fitting, including: a laser radar, including an inertial sensor; a transportation device for controlling the movement of a steel coil; a controller, the controller being configured to: obtain laser radar point cloud data and laser radar inertial sensor data; determine whether the device is in a detectable state based on the laser radar point cloud data and the laser radar inertial sensor data; when the device is in a detectable state, project the laser radar point cloud data onto a reference plane to obtain an end face image of the steel coil; extract the height of the center of the inner circle in the end face image of the steel coil; determine the centering result based on the calibrated centering height and the inner circle center height; and control the transportation device to move the steel coil based on the centering result.

[0014] Finally, the present application also provides a computer-readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors to implement the 3D roll-up centering method as described above.

[0015] In summary, the 3D roll alignment method based on point cloud fitting provided by the present application can obtain more comprehensive and accurate data compared to image recognition. The end face detection of steel coils obtains equipment status and location information through the collection and processing of lidar point cloud data and inertial sensor data. This method provides more data support in a more direct and comprehensive manner, making subsequent processing and analysis more accurate and reliable. In addition, the end face detection of steel coils can also effectively eliminate some interference and misjudgment. For example, image recognition may be interfered by factors such as light and reflection, resulting in deviations or errors in feature extraction and classification. The use of lidar for data collection can circumvent these problems and reduce data errors and deviations. At the same time, the end face detection of steel coils can also judge whether the equipment is in a detectable state, further improving the credibility of the data.

[0016] Other features and advantages of the embodiments of the present invention will be described in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A flow chart of a 3D roll-up centering method based on point cloud fitting provided by an embodiment of the present invention;

[0019] Figure 2 A flowchart of step S20 in the 3D roll-up centering method based on point cloud fitting provided in an embodiment of the present invention;

[0020] Figure 3 Another flow chart of step S20 in the 3D roll-up centering method based on point cloud fitting provided by an embodiment of the present invention;

[0021] Figure 4 A flowchart of step S40 in the 3D roll-up centering method based on point cloud fitting provided in an embodiment of the present invention;

[0022] Figure 5 A flowchart of step S42 in the 3D roll-up centering method based on point cloud fitting provided in an embodiment of the present invention;

[0023] Figure 6 Another flow chart of step S42 in the 3D roll-up centering method based on point cloud fitting provided by an embodiment of the present invention;

[0024] Figure 7 A flowchart of step S50 in the 3D roll-up centering method based on point cloud fitting provided in an embodiment of the present invention;

[0025] Figure 8 A schematic diagram of a centering structure for demonstrating a 3D roll-up centering method based on point cloud fitting provided in an embodiment of the present invention; and

[0026] Fig. 9 A schematic diagram of a module of a 3D roll-up alignment device based on point cloud fitting provided in an embodiment of the present invention.

[0027] Marking Description

[0028] 100. Centering equipment;

[0029] 10. Laser radar; 20. Transportation device;

[0030] 30. Controller. DETAILED DESCRIPTION

[0031] In order to make the above and other features and advantages of the present application more clear, the present application is further described below in conjunction with the accompanying drawings. It should be understood that the specific embodiments given herein are for the purpose of explaining to those skilled in the art and are only exemplary and not restrictive.

[0032] In the following description, many specific details are set forth to provide a thorough understanding of the present application. However, it is apparent to those skilled in the art that specific details need not be adopted to practice the present application. In other cases, well-known steps or operations are not described in detail to avoid blurring the present application.

[0033] The 3D roll-up centering method based on point cloud fitting provided in the embodiment of the present application can be executed by the data detection device provided in the embodiment of the present application, and the device can be configured in an electronic device.

[0034] In a general inventive concept of the present application, a 3D laser radar is selected to tolerate more environmental noise. The equipment displacement caused by the on-site environmental vibration does not require changes at the hardware level, and only parameters need to be adjusted at the software level. Based on the laser radar, the point cloud data of the upper roll is collected and the steel coil features and the spatial position of the inner steel coil are extracted, which can provide the correct centering height indication for the upper roll trolley.

[0035] See also Figure 1 , Figure 1 The present invention provides a flowchart of a 3D roll-up centering method based on point cloud fitting according to an embodiment of the present invention.

[0036] Based on this, an embodiment of the present invention first provides a 3D roll-up centering method based on point cloud fitting, comprising:

[0037] Step S10, obtaining laser radar point cloud data and laser radar inertial sensor data;

[0038] Step S20: judging whether the device is in a detectable state according to the laser radar point cloud data and the laser radar inertial sensor data;

[0039] Step S30: When the device is in a detectable state, project the laser radar point cloud data onto a reference plane to obtain an end face image of the steel coil;

[0040] Step S40, extracting the height of the center of the inner circle in the end surface image of the steel coil;

[0041] Step S50: determining the centering result according to the calibrated centering height and the inner circle center height.

[0042] In step S10, the system will obtain the point cloud data collected by the laser radar device, that is, the discrete point set obtained by scanning the target object or scene with a laser beam. At the same time, it will also obtain inertial sensor data related to the laser radar, such as gyroscopes and accelerometers, etc., to compensate for the posture changes caused by the laser radar during movement, thereby ensuring the reliability of subsequent processing and analysis.

[0043] In step S20, by analyzing and processing the laser radar point cloud data and the laser radar inertial sensor data, the system can determine whether the equipment is in a detectable state. For example, the posture, motion state and working stability of the equipment can be detected to ensure the accuracy and reliability of data acquisition, and effective screening and control can be performed before starting the steel coil end face image processing to ensure the quality and credibility of the data, avoid centering under abnormal equipment conditions, and ensure the stability of the centering operation.

[0044] In step S30, after the device is determined to be in a detectable state, the laser radar point cloud data will be processed and projected onto the reference surface. The purpose of this step is to convert the point cloud data into a two-dimensional image of the end face of the steel coil, reducing computing power and facilitating subsequent image processing and analysis.

[0045] In step S40, the contour of the inner circle on the end face of the steel coil can be extracted from the acquired end face image of the steel coil using image processing algorithms and techniques, and the position of the center of the inner circle, that is, the height of the center of the inner circle, can be further calculated.

[0046] Then, step S50 is performed: the centering result is determined according to the calibrated centering height and the inner circle center height. According to the pre-calibrated centering height, combined with the inner circle center height extracted in step S40, the relative centering result of the steel coil on the end face image can be calculated. This result can be used to determine the position and offset of the steel coil, as well as subsequent adjustment and control operations.

[0047] In summary, the 3D roll centering method based on point cloud fitting provided by a general inventive concept of an embodiment of the present invention can obtain more comprehensive and accurate data compared to image recognition. The end face detection of the steel coil acquires the equipment status and position information by collecting and processing the laser radar point cloud data and inertial sensor data. This method provides more data support in a more direct and comprehensive way, making subsequent processing and analysis more accurate and reliable. In addition, the end face detection of the steel coil can also effectively eliminate some interference and misjudgment. For example, image recognition may be interfered by factors such as light and reflection, resulting in deviations or errors in feature extraction and classification. The use of laser radar for data collection can circumvent these problems and reduce data errors and deviations. At the same time, the end face detection of the steel coil can also judge whether the equipment is in a detectable state, further improving the credibility of the data.

[0048] The following is a detailed description of the above steps:

[0049] Please refer to the figure Figure 2 , Figure 2 This is a flowchart of step S20 in the 3D roll-up centering method based on point cloud fitting provided in an embodiment of the present invention.

[0050] In step S20, judging whether the device is in a detectable state according to the laser radar point cloud data and the laser radar inertial sensor data includes:

[0051] Step S21, according to the laser radar inertial sensor data and the calibration inertial reference solution vector cosine angle;

[0052] Step S22, calculating the laser radar deflection angle by using the vector cosine angle;

[0053] Step S23, performing threshold judgment on the laser radar deflection angle and the preset maximum deflection angle threshold;

[0054] Step S24: When the laser radar deflection angle is less than the maximum deflection angle threshold, determine that the device is in a detectable state.

[0055] It is understandable that in step S21, based on the laser radar and inertial sensor data, the cosine angle between the calibrated inertial reference solution vector and the currently collected laser radar data can be calculated. This cosine angle can be used to evaluate the attitude and position information of the device.

[0056] In step S22, the deflection angle of the laser radar can be further calculated based on the cosine angle calculated in the previous step. The deflection angle represents the rotation angle of the laser radar relative to the calibrated inertial reference solution vector, which is used to reflect the azimuth and attitude changes of the device.

[0057] Next, in step S23, the calculated laser radar deflection angle is compared with a preset maximum deflection angle threshold value, which is set in advance to determine whether the deflection angle exceeds the allowable range.

[0058] Finally, in step S24, when the laser radar deflection angle is less than the maximum deflection angle threshold, it can be determined that the device is in a detectable state, which means that the azimuth and posture changes of the device are within an acceptable range, and the end face of the steel coil can be detected.

[0059] Please refer to the figure Figure 3 , Figure 3 Another flowchart of step S20 in the 3D roll-up centering method based on point cloud fitting provided by an embodiment of the present invention.

[0060] Determine whether the device is in a detectable state based on the LiDAR point cloud data and LiDAR inertial sensor data, including:

[0061] Step S21', clustering the laser radar point cloud data to obtain steel coil point cloud data;

[0062] Step S22', segmenting the plane in the steel coil point cloud data to obtain the steel coil end plane equation;

[0063] Step S23', extracting the Z-axis height from the coil end plane equation;

[0064] Step S24', subtract the Z-axis height from the preset reference plane Z-axis height to obtain the device offset;

[0065] Step S25', performing threshold determination on the device offset and the preset maximum Z offset;

[0066] Step S26': when the device offset is less than the maximum Z offset, determine that the device is in a detectable state.

[0067] In step S21', a clustering algorithm is performed on the point cloud data collected by the laser radar, such as a DBSCAN algorithm based on Euclidean distance or a convex hull segmentation algorithm based on convex hull. The point cloud data is divided into multiple steel coil areas by aggregating adjacent points.

[0068] In step S22', a plane segmentation algorithm, such as RANSAC or normal vector analysis method, is applied to each clustered steel coil point cloud data to fit the plane equation of the steel coil end surface. This plane equation can represent the geometric features of the steel coil end surface.

[0069] In step S23', the Z-axis height is extracted from the plane equation of the steel coil end surface, that is, the coefficient related to the Z-axis in the plane equation is extracted. This height value reflects the vertical distance between the steel coil end surface and the origin of the coordinate system.

[0070] In step S24', the extracted Z-axis height is subtracted from the preset Z-axis height of the reference plane to obtain the offset of the device, which reflects the position change of the device relative to the reference plane.

[0071] In step S25', the device offset is compared with a preset maximum Z offset. If the device offset is less than the maximum Z offset, it means that the position change of the device is within the allowable range.

[0072] Finally, in step S26 ′, when the device offset satisfies the condition of being less than the maximum Z offset, it can be determined that the device is in a detectable state.

[0073] Please refer to the figure Figure 4 , Figure 4 This is a flowchart of step S40 in the 3D roll-up centering method based on point cloud fitting provided in an embodiment of the present invention.

[0074] In step S40, the center height of the inner circle is extracted from the end surface image of the steel coil, including:

[0075] Step S41, determining a two-dimensional contour point set of the steel coil end surface in the steel coil end surface image;

[0076] Step S42, fitting an ellipse in the two-dimensional contour point set of the steel coil end surface to determine an outer circle contour point set and an inner circle contour point set in the two-dimensional contour point set of the steel coil end surface;

[0077] Step S43, filtering the outer circle contour in the two-dimensional contour point set of the steel coil end face, and retaining the inner circle contour point set;

[0078] Step S44, obtaining the inner circle parameters of the steel coil according to the inner circle contour point set, and extracting the inner circle center height from the inner circle parameters of the steel coil.

[0079] It can be understood that in step S41, the two-dimensional contour point set of the steel coil end face is determined in the steel coil end face image by an image processing algorithm. The contour of the steel coil end face can be obtained by using an edge detection algorithm (such as Canny edge detection) or a contour extraction algorithm (such as the findContours function in OpenCV). In step S42, an ellipse is fitted to the two-dimensional contour point set of the steel coil end face to determine the outer circle contour point set and the inner circle contour point set. The parameters of the ellipse can be estimated by a fitting algorithm such as the least squares method or other algorithms. In step S43, the outer circle contour in the two-dimensional contour point set of the steel coil end face is filtered, and the inner circle contour point set is retained. The judgment can be made based on the fitted ellipse parameters and the distance relationship between the points in the contour point set and the ellipse, and the points located in the inner circle are retained. In step S44, the parameters of the inner circle of the steel coil can be obtained based on the inner circle contour point set, such as the radius of the inner circle or the length of the major and minor axes of the ellipse. At the same time, the center height of the inner circle can be calculated by the parameters of the inner circle. The center height is the vertical distance between the center of the inner circle and the reference plane. Through the above steps, the end face image of the steel coil can be effectively processed, the inner circle contour point set can be extracted, and the inner circle parameters of the steel coil and its center height can be obtained.

[0080] Please refer to the figure Figure 5 , Figure 5 A flowchart of step S42 in the 3D coil centering method based on point cloud fitting provided in an embodiment of the present invention. In an optional manner of the embodiment of the present invention, in step S42, an ellipse is fitted in the two-dimensional contour point set of the coil end face to determine the outer circle contour point set and the inner circle contour point set in the two-dimensional contour point set of the coil end face, including:

[0081] Step S421, converting the two-dimensional contour point set of the steel coil end surface into a polar coordinate system centered at the origin of the coordinate system;

[0082] Step S422, using the least squares method to perform ellipse fitting on the two-dimensional contour point set of the steel coil end surface in the polar coordinate system, and obtaining the ellipse model parameters of the least squares method fitting through a numerical optimization algorithm, wherein the ellipse model parameters include the ellipse center;

[0083] Step S423, dividing the points in the two-dimensional contour point set of the end face of the steel coil into an outer circle contour point set and an inner circle contour point set according to their distance from the center of the ellipse.

[0084] It can be understood that using polar coordinates and ellipse model parameters to describe the shape of the end face of the steel coil can more accurately reflect its true situation. The polar coordinate system takes into account the distance from the point to the origin and the angle between the point and the positive x-axis, while the ellipse model can better conform to the ellipse shape of the end face of the steel coil. The least squares method is used to fit the ellipse to the two-dimensional contour point set of the end face of the steel coil in the polar coordinate system. Numerical optimization algorithms, such as the Levenberg-Marquardt algorithm, can be used in conjunction with the least squares method to estimate the parameters of the ellipse model. The discrete degree of the data in the point set can be reduced, and the precision and accuracy of the measurement can be improved. The ellipse model parameters obtained by fitting can more accurately describe the shape of the end face of the steel coil. According to the distance from the center of the ellipse, the points in the two-dimensional contour point set of the end face of the steel coil are divided into an outer circle contour point set and an inner circle contour point set. Usually, the inner circle contour point set is located inside the ellipse, while the outer circle contour point set is located outside the ellipse. By calculating the distance from each point to the center of the ellipse, points with a distance less than a certain threshold can be divided into an inner circle contour point set, and points with a distance greater than the threshold can be divided into an outer circle contour point set.

[0085] Please refer to the figure Figure 6 , Figure 6 Another flowchart of step S42 in the 3D coil centering method based on point cloud fitting provided by an embodiment of the present invention. In another embodiment of the present invention, an ellipse is fitted in the two-dimensional contour point set of the steel coil end face to determine the outer circle contour point set and the inner circle contour point set in the two-dimensional contour point set of the steel coil end face, including:

[0086] Step S421', initializing ellipse model parameters;

[0087] Step S422', for each point in the two-dimensional contour point set of the steel coil end surface, calculate the distance from the point to the ellipse model, and record the minimum distance point;

[0088] Step S423', updating the ellipse model parameters according to the minimum distance point, until the two-dimensional contour point set of the steel coil end surface is traversed to obtain the final ellipse model parameters;

[0089] Step S424', dividing the points in the two-dimensional contour point set of the end face of the steel coil into an outer circle contour point set and an inner circle contour point set according to their distance from the center of the ellipse.

[0090] Specifically, a set of initial ellipse model parameters can be selected as starting values. These parameters usually include the center coordinates, major axis length, minor axis length and rotation angle of the ellipse. For each point in the two-dimensional contour point set of the steel coil end face, calculate its distance to the current ellipse model and record the minimum distance point. The distance can be calculated using Euclidean distance or other suitable distance measurement methods. According to the recorded minimum distance point, a numerical optimization algorithm (such as the least squares method) is used to update the parameters of the ellipse model. Specifically, by adjusting the parameters such as the center, major axis length, minor axis length and rotation angle of the ellipse, the fitting error between the minimum distance point and the ellipse model is minimized. Similarly, after fitting the ellipse model parameters, the two-dimensional contour point set of the steel coil end face can be divided into an outer circle contour point set and an inner circle contour point set according to the distance from the center of the ellipse. Generally, points farther from the center of the ellipse are classified as outer circle contour points, while points closer are classified as inner circle contour points.

[0091] Please refer to the figure Figure 7 , Figure 7 This is a flow chart of step S50 in the 3D roll-up centering method based on point cloud fitting provided by an embodiment of the present invention. In step S50, the centering result is determined according to the calibrated centering height and the inner circle center height, including:

[0092] Step S51, determining the height deviation by calibrating the centering height and the inner circle center height;

[0093] Step S52, determining whether the height deviation is within a preset maximum height deviation threshold;

[0094] Step S53: when the height deviation is less than the maximum height deviation threshold, determine that the centering is normal;

[0095] Step S54: When the height deviation is greater than the maximum height deviation threshold, determine that the centering is abnormal.

[0096] In this step, a calibration operation can be performed to determine the height deviation by measuring the difference between the middle height and the inner circle center height. The calibration operation can be performed using appropriate measuring tools and methods to ensure the accuracy of the measurement results. By comparing the measured height deviation with the preset maximum height deviation threshold, it is determined whether the height deviation is within the allowable range. If the height deviation is less than or equal to the maximum height deviation threshold, when the height deviation is less than or equal to the maximum height deviation threshold, it can be determined that the centering is normal. This means that the difference between the measured middle height and the inner circle center height is within an acceptable range and meets the expected requirements. When the height deviation is greater than the maximum height deviation threshold, it can be determined that the centering is abnormal.

[0097] like Figure 8The figure shows the effect of coil centering recognition. On the left is the point cloud of the steel coil obtained after LiDAR scanning and clustering processing, together with the coil trolley pallet below it. The centering deviation shown on the right is the height centering deviation, and the end face distance is the distance that the end face of the steel coil deviates from the reference plane. Since the centering deviation is greater than the maximum allowable deviation, the red word "abnormal centering" is displayed in the figure.

[0098] The 3D coil centering method further includes: step S60, moving the steel coil according to the centering result.

[0099] Specifically, moving the steel coil according to the alignment results includes:

[0100] Step S61, in case of abnormal centering, moving the steel coil by height deviation for fine adjustment;

[0101] Step S62: Re-center the coil until it is centered properly.

[0102] It is understood that the way the steel coil needs to be moved is determined according to the specific situation of the centering anomaly. For example, if the centering deviates from the vertical direction, it can be considered to raise or lower the steel coil in the vertical direction. The distance or angle of movement is determined according to the size of the height deviation and the accuracy required for the adjustment. It can be estimated or measured according to the actual situation to ensure the accuracy of the movement. According to the determined movement distance or angle, use appropriate equipment or tools to move the steel coil to make fine adjustments to bring the steel coil to the desired position.

[0103] After the coil is moved, re-check the centering to confirm whether the coil has been adjusted to the correct position. If the centering is still abnormal, make necessary adjustments and re-check until the centering is normal.

[0104] Based on the above method, the embodiment of the present invention detected and sampled 14 steel coils for verification, statistically analyzed the algorithm detection results, compared them with the actual field test values, statistically analyzed the error of each steel coil, and statistically analyzed the absolute error value. The average error of coil No. 1 was 2.8 mm, and the average error of coil No. 2 was 3.4 mm, as shown in Table 1.

[0105] Table 1

[0106]

[0107]

[0108] From the above, it can be seen that the 3D coil centering method based on point cloud fitting provided by the embodiment of the present invention has lower errors and deviations, and can bring the following effects in a commercial environment: by using 3D point cloud data for centering operations, more accurate coil position adjustment can be achieved. Compared with traditional manual or 2D image centering methods, the point cloud fitting-based method can more accurately capture the shape and position information of the coil surface, thereby improving the accuracy and efficiency of centering. The point cloud fitting-based method can achieve precise positioning and centering of the coil in three-dimensional space, so that subsequent processing can be carried out more accurately. This helps to improve product quality and consistency, and reduce size, shape and other issues caused by coil offset.

[0109] Please continue to refer to the figure Fig. 9 , Fig. 9 A schematic diagram of a module of a 3D roll-up alignment device 100 based on point cloud fitting provided in an embodiment of the present invention.

[0110] The embodiment of the present invention further provides a 3D roll-up centering device 100 based on point cloud fitting, comprising:

[0111] Lidar 10, including an inertial sensor;

[0112] The transport device 20 is used to control the movement of the steel coil;

[0113] The controller 30 is configured to execute the above 3D roll-up centering method based on point cloud fitting.

[0114] It should be understood that the specific features, operations and details described hereinabove with respect to the method of the present application may also be similarly applied to the device and system of the present application, or vice versa. In addition, each step of the method of the present application described above may be performed by a corresponding component or unit of the device or system of the present application.

[0115] It should be understood that each module / unit of the device of the present application can be implemented in whole or in part by software, hardware, firmware or a combination thereof. Each module / unit can be embedded in the processor of the electronic device or independent of the processor in the form of hardware or firmware, or can be stored in the memory of the electronic device in the form of software for the processor to call to perform the operation of each module / unit. Each module / unit can be implemented as an independent component or module, or two or more modules / units can be implemented as a single component or module.

[0116] The present application also provides an electronic device, which includes a processor and a memory storing computer program instructions. When the processor executes the computer program instructions, each step of the above-mentioned method for centering a steel coil and a saddle is implemented. The electronic device can be a server, a terminal, or any other electronic device with necessary computing and / or processing capabilities.

[0117] In one embodiment, the electronic device may include a processor, a memory, a network interface, a communication interface, etc. connected through a system bus. The processor of the electronic device may be used to provide necessary computing, processing and / or control capabilities. The memory of the electronic device may include a non-volatile storage medium and an internal memory. The non-volatile storage medium may store an operating system, a computer program, etc. The internal memory may provide an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface and the communication interface of the electronic device may be used to connect and communicate with external devices through a network. When the computer program is executed by the processor, the steps of the method of the present application are executed.

[0118] The present application provides a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the above-mentioned method for centering a steel coil and a saddle is implemented.

[0119] Those skilled in the art will appreciate that the method steps of the present application can be completed by instructing related hardware such as electronic devices or processors through a computer program, and the computer program can be stored in a non-temporary computer-readable storage medium, and the computer program causes the steps of the present application to be executed when it is executed. Depending on the circumstances, any reference to memory, storage or other media herein may include non-volatile or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0120] The various technical features described above can be combined arbitrarily. Although all possible combinations of these technical features are not described, any combination of these technical features should be considered to be covered by this specification as long as there is no contradiction in such combination.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A 3D roll-up centering method based on point cloud fitting, characterized in that: include: Obtain lidar point cloud data and lidar inertial sensor data; Determine whether the device is in a detectable state based on the lidar point cloud data and lidar inertial sensor data; When the equipment is in a detectable state, projecting the laser radar point cloud data onto a reference plane to obtain an end face image of the steel coil; Extracting the height of the center of the inner circle in the end surface image of the steel coil; The centering result is determined according to the calibrated centering height and the inner circle center height.

2. The 3D roll-up centering method according to claim 1, characterized in that: The determining whether the device is in a detectable state according to the laser radar point cloud data and the laser radar inertial sensor data includes: According to the laser radar inertial sensor data and the calibrated inertial reference solution vector cosine angle; Calculate the laser radar deflection angle through the vector cosine angle; Performing threshold judgment on the laser radar deflection angle and a preset maximum deflection angle threshold; When the laser radar deflection angle is less than the maximum deflection angle threshold, it is determined that the device is in a detectable state.

3. The 3D roll-up centering method according to claim 1, characterized in that: The determining whether the device is in a detectable state according to the laser radar point cloud data and the laser radar inertial sensor data includes: Clustering the LiDAR point cloud data to obtain the steel coil point cloud data; Segment the plane in the steel coil point cloud data to obtain the plane equation of the steel coil end face; Extracting the Z-axis height from the coil end plane equation; Subtract the Z-axis height from the preset reference plane Z-axis height to obtain the device offset; Perform threshold determination on the device offset and a preset maximum Z offset; When the device offset is less than the maximum Z offset, it is determined that the device is in a detectable state.

4. The 3D roll-up centering method according to claim 1, characterized in that: The step of extracting the center height of the inner circle from the end surface image of the steel coil comprises: Determining a two-dimensional contour point set of the steel coil end surface in the steel coil end surface image; Fitting an ellipse in the two-dimensional contour point set of the steel coil end surface to determine an outer circle contour point set and an inner circle contour point set in the two-dimensional contour point set of the steel coil end surface; Filter the outer circle contour in the two-dimensional contour point set of the steel coil end face, and retain the inner circle contour point set; The inner circle parameters of the steel coil are obtained according to the inner circle contour point set, and the inner circle center height in the inner circle parameters of the steel coil is extracted.

5. The 3D roll-up centering method according to claim 1, characterized in that: Determining the centering result according to the calibrated centering height and the inner circle center height includes: Determine the height deviation by calibrating the centering height and the inner circle center height; Determining whether the height deviation is within a preset maximum height deviation threshold; When the height deviation is less than the maximum height deviation threshold, determining that the centering is normal; When the height deviation is greater than the maximum height deviation threshold, a centering abnormality is determined.

6. The 3D roll-up centering method according to claim 4, characterized in that: Fitting an ellipse in the two-dimensional contour point set of the steel coil end surface to determine an outer circle contour point set and an inner circle contour point set in the two-dimensional contour point set of the steel coil end surface, including: Convert the two-dimensional contour point set of the steel coil end surface into a polar coordinate system centered at the origin of the coordinate system; The least square method is used to perform ellipse fitting on the two-dimensional contour point set of the steel coil end surface in the polar coordinate system, and the ellipse model parameters of the least square method fitting are obtained through a numerical optimization algorithm, wherein the ellipse model parameters include the ellipse center; The points in the two-dimensional contour point set of the steel coil end surface are divided into an outer circle contour point set and an inner circle contour point set according to their distance from the center of the ellipse.

7. The 3D roll-up centering method according to claim 4, characterized in that: Fitting an ellipse in the two-dimensional contour point set of the steel coil end surface to determine an outer circle contour point set and an inner circle contour point set in the two-dimensional contour point set of the steel coil end surface, including: Initialize ellipse model parameters; For each point in the two-dimensional contour point set of the coil end face, calculate its distance to the ellipse model and record the minimum distance point; According to the minimum distance point, the ellipse model parameters are updated until the two-dimensional contour point set of the steel coil end surface is traversed to obtain the final ellipse model parameters; The points in the two-dimensional contour point set of the steel coil end surface are divided into an outer circle contour point set and an inner circle contour point set according to their distance from the center of the ellipse.

8. The 3D roll-up centering method according to claim 4, characterized in that: Also includes: In case of abnormal centering, the coil is moved by height deviation to make fine adjustments; Re-check the centering until the steel coil is properly aligned.

9. A 3D roll-up centering device based on point cloud fitting, characterized in that: include: LiDAR, including inertial sensors; Transport device, used to control the movement of steel coils; A controller, the controller being configured to: Obtain lidar point cloud data and lidar inertial sensor data; Determine whether the device is in a detectable state based on the lidar point cloud data and lidar inertial sensor data; When the equipment is in a detectable state, projecting the laser radar point cloud data onto a reference plane to obtain an end face image of the steel coil; Extracting the height of the center of the inner circle in the end surface image of the steel coil; Determine the centering result according to the calibrated centering height and the center height of the inner circle; The transport equipment is controlled to move the steel coil according to the alignment result.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the 3D roll-up centering method according to any one of claims 1 to 8.

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