Three-dimensional Distribution Testing Method of Pipeline Based on Fusion of Double Attitude Angles of Laser Spot

Abstract

The present invention discloses a method for testing the three-dimensional distribution of pipelines based on the fusion of two attitude angles of laser spots, including setting up a pipeline three-dimensional distribution detection device; controlling the pipeline three-dimensional distribution detection device to move forward at a constant speed in the pipeline, and simultaneously obtaining the data of the three-axis accelerometers and three-axis gyroscopes of two MEMS-IMU inertial modules and the position information of the laser spots collected by a COMOS image sensor; processing the data of the MEMS-IMU inertial modules to obtain the deflection angle of the pipeline to be measured; processing the position information of the laser spots collected by the CMOS image sensor to obtain the deflection angle of the pipeline to be measured; using the Kalman algorithm to fuse the deflection angle of the pipeline to be measured obtained by the CMOS image sensor and the deflection angle of the pipeline to be measured obtained by the MEMS-IMU inertial module to obtain the optimal value of the deflection angle. The present invention reduces the errors of sensors with different principles through data fusion, can adapt to the measurement of different deflection angles of pipelines, and has high efficiency and high detection accuracy.

Description

Technical Field

[0001] The present invention belongs to the technical field of metrology and detection, and specifically relates to a pipeline three-dimensional distribution testing method based on laser spot dual-attitude angle fusion. Background Art

[0002] Underground pipelines (water, electricity, coal, etc.) are an important part of urban construction. Traditionally, the motion posture of pipeline detectors is measured: first, through ground penetrating radar, but due to the complexity of the underground environment, the device is large in size and the use environment is limited. It cannot adapt to the complex and changeable engineering environment, and because it is easily affected by the magnetic field, it can only be used in a fixed environment; second, pipeline robots. Due to the large size of pipeline robots, they are only suitable for underground pipelines with larger diameters, and are limited by battery technology and unclear conditions in the pipeline to be measured. Therefore, when using pipeline robots, the robot often gets lost in the pipeline to be measured when measuring underground pipelines. In engineering measurement, especially in the spatial orientation measurement of pipelines, spatial angle measurement is very common. Usually, the pipeline running trajectory is very different from the guide design track, especially the backward detection technology after completion, resulting in a large deviation between the completion data of the pipeline project and the actual spatial coordinates, which has a certain adverse effect on the construction and use of underground pipelines. Therefore, correctly understanding the underground space location of completed pipelines can be used not only as an indicator for evaluating project quality, but also as a reference for track design and construction of new pipelines in the future, thereby avoiding pipeline crossings. Summary of the invention

[0003] The object of the present invention is to provide a pipeline three-dimensional distribution testing method based on laser spot dual-attitude angle fusion which is simple to operate and has high measurement accuracy.

[0004] The technical solution to achieve the purpose of the present invention is: a pipeline three-dimensional distribution test method based on laser spot dual-attitude angle fusion, the specific steps are:

[0005] Step 1: Setting a pipeline three-dimensional distribution detection device, the pipeline three-dimensional distribution detection device includes a master control detection module, a slave control detection module, two mechanical centering mechanisms and a universal joint, the two mechanical centering structures are connected by a universal joint, the master control detection module and the slave control detection module are respectively fixed on the two mechanical centering structures, the master control detection module includes a main control board, a first MEMS-IMU inertial module and a laser, the slave control detection module includes a slave control board, a second MEMS-IMU inertial module, an imaging screen and a CMOS image sensor, and in a straight state, the laser spot falls on the center of the imaging screen;

[0006] Step 2: Control the three-dimensional pipeline distribution detection device to move forward at a constant speed in the pipeline, and at the same time obtain the triaxial accelerometer and triaxial gyroscope data of the two MEMS-IMU inertial modules and the position information of the laser spot collected by the COMOS image sensor;

[0007] Step 3: Process the data of the MEMS-IMU inertial module to obtain the deflection angle of the pipeline to be measured;

[0008] Step 4: Process the position information of the laser spot collected by the CMOS image sensor to obtain the deflection angle of the pipeline to be measured;

[0009] Step 5: Use the Kalman algorithm to fuse the deflection angle of the pipeline to be measured obtained by the CMOS image sensor and the deflection angle of the pipeline to be measured obtained by the MEMS-IMU inertial module to obtain the optimal value of the deflection angle.

[0010] Preferably, both the first MEMS-IMU inertial module and the second MEMS-IMU inertial module include a MEMS triaxial accelerometer and a MEMS triaxial gyroscope.

[0011] Preferably, the specific method for processing the data of the MEMS-IMU inertial module to obtain the deflection angle of the pipeline to be measured is as follows:

[0012] Obtain the components of the gravitational acceleration in the X-axis, Y-axis, and Z-axis directions at different times through the MEMS triaxial accelerometer, and then solve for the attitude angle in combination with trigonometric functions; obtain the angular velocity in the X-axis, Y-axis, and Z-axis directions at different times through the MEMS triaxial gyroscope, and calculate the deflection angle of the carrier coordinate system relative to the fixed coordinate system, where the carrier coordinate system refers to taking the centroid of the MEMS-IMU inertial module as the origin, the X-axis along the forward direction of the carrier, the Z-axis along the side axis direction of the carrier pointing to the right wing, and the Y along the vertical axis direction of the carrier; calculate the deflection angle of the pipeline to be measured through the geometric relationship based on the deflection angles of the two mechanical centering mechanisms and the fixed length of the device.

[0013] Preferably, the specific method for processing the position information of the laser spot collected by the CMOS image sensor to obtain the deflection angle of the pipeline to be measured is as follows:

[0014] Convert the image collected by the CMOS image sensor into a grayscale image, and perform denoising and binarization;

[0015] Use the centroid method to calculate the pixel position coordinates of the laser spot;

[0016] Perform least squares curve fitting to obtain the plane azimuth map of the pipeline and obtain the deflection angle of the pipeline.

[0017] Preferably, the specific method for using the centroid method to calculate the pixel position coordinates of the laser spot is as follows:

[0018] Let the binary image containing the laser spot contour region be g(i, j), and the center coordinates of the laser spot be (x 0 , y 0 ). (i, j) are pixel coordinates, and the size of the image is M×N. Then, the center coordinates of the laser spot are obtained from the following formula:

[0019]

[0020]

[0021] The transformation from the row image pixel coordinate system (u - v) to the image physical coordinate system (X, Y) is specifically given by the formula:

[0022]

[0023] In homogeneous coordinate form, it is as follows:

[0024]

[0025] Where: (u, v) are the image pixel coordinates of the light spot, (u 0 , v 0 ) are the coordinates of the origin of the image physical coordinate system in the pixel coordinate system, (x d , y d ) are the physical position coordinates of the light spot, dx and dy respectively represent the corresponding positions of each pixel on the horizontal axis X and the vertical axis Y, and θ is the deflection angle from the image pixel coordinate system to the image physical coordinate system as agreed upon by this device.

[0026] Preferably, the specific method for obtaining the optimal value of the deflection angle by fusing the deflection angle of the pipeline to be measured obtained by the CMOS image sensor and the deflection angle of the pipeline to be measured obtained by the MEMS - IMU inertial module using the Kalman algorithm is as follows:

[0027] Step 5.1: Establish the state - space equation:

[0028] θ^ = θ 1 + K(θ 2 - θ 1 )

[0029] Where, θ 1 is the pipeline deflection angle obtained by the MEMS - IMU inertial module, θ 2 is the pipeline deflection angle obtained by the CMOS image sensor, K is the Kalman gain, and θ^ is the estimated value;

[0030] Step 5.2: Let the standard deviation of the estimated value be σ θ, the variance of the estimated value is Var(θ^), and the expression of the variance of the optimal estimated value is obtained:

[0031]

[0032] where σ 2 θ is another form of the variance of the estimated value, and σ 2 θ1 is the variance of the pipeline deflection angle obtained by the MEMS-IMU inertial module, and σ 2 θ2 is the variance of the pipeline deflection angle obtained by the CMOS image sensor.

[0033] Step 5.3: Find K to minimize the variance of the estimated value and deduce the Kalman gain K:

[0034]

[0035] Step 5.4: Calculate the optimal estimated value at the current moment:

[0036] θ^ = θ 1 + K(θ 2 - θ 1 )

[0037] Step 5.5: Update the variance of the estimated value and verify whether the error obtained by fusing the data is smaller and closer to the true

[0038] value:

[0039]

[0040] Compared with the prior art, the present invention has the following remarkable advantages: Based on the CMOS image sensor to collect the laser spot image, the displacement information of the laser spot center on the image is cleverly converted into the pipeline spatial orientation information, and the pipeline curvature is collected by the MEMS-IMU inertial modules at both ends of the detector. The data of different principle sensors are mutually calibrated to reduce errors and accurately measure the spatial position distribution of the pipeline to be measured.

[0041] The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is the detection principle diagram of the new method for testing the three-dimensional distribution of pipelines based on the fusion of double attitude angles of laser spots of the present invention.

[0043] Figure 2 is the principle diagram of testing the pipeline curvature based on double attitude angles of the present invention.

[0044] Figure 3 is the structural diagram of the overall device of the detector used in the present invention.

[0045] Figure 4 It is the structural diagram of three pairs of arms mechanical centering used in the present invention.

[0046] Figure 5 It is the flow chart of the present invention for testing the pipeline deflection based on the laser spot. Specific embodiments

[0047] As Figures 1 to 5 shown, a three-dimensional distribution testing method for pipelines based on the fusion of two attitude angles of laser spots. Since the laser has good directivity, a laser source, an imaging screen, and a camera are respectively installed at two fixed ends connected by a universal joint. When the two fixed ends move in the pipeline to be measured, when the pipeline to be measured is in a straight state, the laser spot falls on the center position of the screen; when the pipeline to be measured deflects, the laser spot deviates from the center position of the screen. The bending degree of the pipeline is deduced through the attitude angles of the front and rear detection modules of the detector and the distance between the detection modules, and the spatial position distribution of the pipeline to be measured is solved through data fusion. The specific steps are as follows:

[0048] Step 1: Set up the pipeline three-dimensional distribution detection device. According to Figure 1 , Figure 2 , Figure 3 shown, the pipeline three-dimensional distribution detection device includes a main control detection module, a slave control detection module, two mechanical centering mechanisms, and a universal joint. The main control detection module includes a main control board, a first MEMS-IMU inertial module, and a laser. The slave control detection module includes a slave control board, a second MEMS-IMU inertial module, an imaging screen, and a CMOS image sensor. The two mechanical centering structures are connected by a universal joint. The mechanical centering structure consists of a shaft and three pairs of support arms. As Figure 3 shown, every two support arms form a pair. The main control detection module and the slave control detection module are respectively fixed on the shafts of the two mechanical centering structures to ensure that in the straight state, the laser spot falls on the exact center of the screen. The universal joint connects the front and rear parts, enabling the angle of the rotation axis of the front and rear parts to change, so that the device can move stably in the curved pipeline, and the laser can irradiate at different positions on the imaging screen.

[0049] In a further embodiment, both the first MEMS-IMU inertial module and the second MEMS-IMU inertial module include a three-axis accelerometer and a three-axis gyroscope;

[0050] Step 2: Control the pipeline three-dimensional distribution detection device to move forward at a constant speed in the pipeline, and at the same time, obtain the data of the three-axis accelerometer and three-axis gyroscope of the two MEMS-IMU inertial modules and the position information of the laser spot collected by the COMOS image sensor.

[0051] Step 3: Process the data of the MEMS-IMU inertial module to obtain the attitude angle, the deflection angle of the carrier coordinate system relative to the fixed coordinate system, and the deflection angle of the pipeline to be measured.

[0052] Obtain the components of the gravitational acceleration in the X-axis, Y-axis, and Z-axis directions at different times through the MEMS triaxial accelerometer, and then solve the attitude angle by combining trigonometric functions; calculate the deflection angle of the carrier coordinate system relative to the fixed coordinate system through the angular velocities in the X-axis, Y-axis, and Z-axis directions of the MEMS triaxial gyroscope at different times. Among them, the carrier coordinate system is a right-handed coordinate system with the centroid of the MEMS-IMU inertial module as the origin, the X-axis along the longitudinal axis direction, that is, the forward direction of the carrier, the Z-axis along the side axis direction of the carrier, pointing to the right wing, and the Y-axis along the vertical axis direction of the carrier (i.e., pointing to the sky).

[0053] In underground pipeline detection, the movement direction of the overall device changes continuously with the trend of the pipeline to be measured. By establishing a fixed coordinate system that remains constant throughout the detection process and using the MEMS-IMU inertial module to measure the deflection angle between the carrier coordinate system of the detection module and the fixed coordinate system. A set of MEMS-IMU inertial modules are installed at the front and rear ends of the device respectively. Through the deflection angles at the front and rear ends of the device and the fixed length of the device, the deflection angle of the pipeline to be measured is calculated through geometric relationships.

[0054] From Figure 4 it can be seen that the deflection angle of the front-end detection module is ∠DBA, and the deflection angle of the rear-end detection module is ∠DAB. The lengths of the two line segments AD and BD are used to approximately represent the length of the arc AB. Since the installation positions of the front-end detection module and the rear-end detection module are fixed, the length of the arc AB is a known quantity. Assume that the pipeline deflection angle is θ and ρ is the radius of curvature. From Figure 4 it can be seen that CD is the radius of curvature. Triangles DAC and DBC are both right triangles, ∠CAD and ∠CBD are right angles, CD and AB are perpendicular to each other. It can be known that ∠ABD = ∠BCD, ∠BAD = ∠ACD. From the sum of the interior angles of the quadrilateral, it can be known that:

[0055] θ = 360° - ∠ADB - ∠DAC - ∠DBC

[0056] = 180° - ∠ADB

[0057] = ∠DAB + ∠DBA

[0058] From mathematical trigonometric functions, it can be known that:

[0059]

[0060]

[0061] Combining the above formulas, the curvature can be obtained:

[0062]

[0063] Step 4: Process the position information of the laser spot collected by the CMOS image sensor to obtain the pixel position coordinates of the laser spot, and use the least squares curve fitting to obtain the planar orientation map of the pipeline and obtain the deflection angle of the pipeline to be measured.

[0064] Preprocess the acquired image. Mainly use OpenCV to first convert the initial image into a grayscale image, and then perform adaptive median filtering and Otsu's method to obtain a binary image. Finally, only the circular spot contour of the laser spot remains on the image.

[0065] Since the video collected by the COMOS image sensor in the present invention is in color, it should be processed into a grayscale image during processing. In the grayscale image in RGB format, the three components of R, G, and B are all equal and equal to the grayscale value. Image denoising is a common step in image preprocessing. Common image denoising algorithms include adaptive median filtering, Gaussian filtering, etc. Among them, adaptive median filtering is more suitable for salt-and-pepper noise with sudden white or black points. Image noise mainly comes from the image acquisition and transmission processes. Common noises include additive noise, multiplicative noise, quantization noise, and salt-and-pepper noise, etc. Therefore, the present invention uses adaptive median filtering to eliminate noise. In the binary operation of OpenCV, the image is made into a binary image. Through the filtered binary image, combined with the centroid method, the pixel position coordinates of the laser spot are calculated.

[0066] The centroid method of the present invention is a method of internal region representation. Assume that the binary image containing the region of the laser circular spot contour is g(i,j), and the center coordinates of the laser circular spot are (x 0 , y 0 ), (i,j) is the pixel coordinate, and the size of the image is M×N. Then the center coordinates of the laser circular spot can be obtained by the following formula:

[0067]

[0068]

[0069] Find the center position of the white pixel points in the whole image, that is, the center position of the laser spot.

[0070] Then perform the transformation from the image pixel coordinate system (u-v) to the image physical coordinate system (X,Y). The formula used is:

[0071]

[0072] The homogeneous coordinate form is as follows:

[0073]

[0074] Wherein: (u, v) are the image pixel coordinates of the light spot, and (u 0 , v 0 ) are the coordinates of the origin of the image physical coordinate system in the pixel coordinate system, and (x d , y d ) are the physical position coordinates of the light spot. dx and dy respectively represent the corresponding positions of each pixel on the horizontal axis X and the vertical axis Y, and θ is the deflection angle of the image pixel coordinate system to the image physical coordinate system agreed upon by this device.

[0075] Finally, the plane azimuth map of the pipeline can be obtained by using the least squares curve fitting of matlab, and the deflection angle of the pipeline can be obtained.

[0076] Step Five: Data Fusion Processing

[0077] Use the Kalman algorithm to fuse the deflection angle of the pipeline to be measured measured by the CMOS image sensor and the MEMS-IMU inertial module to obtain the deflection angle of the pipeline to be measured, and obtain an optimal value for obtaining the three-dimensional spatial distribution of the pipeline.

[0078] The Kalman algorithm is a data fusion algorithm that adopts a recursive form in the time domain. Based on the degree of trust in the system state estimate value, the Kalman filter continuously "predicts - corrects" and automatically recursively calculates the best estimate value at the current moment to reduce errors.

[0079] Step 5.1: Establish a state equation in space:

[0080] θ^ = θ 1 + K(θ 2 - θ 1 )

[0081] Wherein, θ 1 is the pipeline deflection angle obtained by the MEMS-IMU inertial module, θ 2 is the pipeline deflection angle obtained by the CMOS image sensor, K is the Kalman gain, and θ^ is the estimated value;

[0082] Step 5.2: Let the standard deviation of the estimated value be σ θ , the variance of the estimated value be Var(θ^), and find the expression of the optimal estimated value variance:

[0083]

[0084] Wherein, σ 2 θ is another form of the estimated value variance, σ 2 θ1 is the variance of the pipeline deflection angle obtained by the MEMS-IMU inertial module, σ2 θ2 The variance of the pipeline deflection angle obtained for the CMOS image sensor.

[0085] Step 5.3: Find K to minimize the variance of the estimated value and calculate the Kalman gain K:

[0086]

[0087] Step 5.4: Calculate the optimal estimated value at the current moment:

[0088] θ^ = θ 1 + K(θ 2 - θ 1 )

[0089] Step 5.5: Update the variance of the estimated value and verify whether the error obtained by fusing the data is smaller and closer to the true

[0090] value:

[0091]

[0092] During the detection process, the mechanical centering structure of the present invention effectively ensures that the MEMS-IMU inertial module is as much as possible on the same horizontal line as the central axis of the pipeline, which can reduce the error introduced by the installation position of the detection module.

[0093] The present invention adopts a MEMS-IMU inertial module to calculate the curvature of the pipeline to be measured by measuring the attitude angles at the front and rear ends of the detector;

[0094] The laser beam emitted by the laser of the present invention has good collimation. When the detector moves in the curved pipeline to be measured, the laser emitted by the laser at the rear end of the detector irradiates on the screen at the front end of the detector and is photographed by the CMOS image sensor, and the spatial position distribution of the pipeline to be measured is calculated based on the deviation degree of the light spot from the center of the screen;

[0095] Existing underground pipeline detectors have problems such as large volume, complex use, and poor measurement accuracy. The present invention is based on multi-sensor data fusion and effectively reduces the measurement error.

Claims

1. A three-dimensional distribution testing method for pipelines based on the fusion of double attitude angles of laser spots, characterized in that, the specific steps are as follows: Step 1: Set up a pipeline three-dimensional distribution detection device. The pipeline three-dimensional distribution detection device includes a main control detection module, a slave control detection module, two mechanical centering mechanisms and a universal joint. The two mechanical centering structures are connected by a universal joint. The main control detection module and the slave control detection module are respectively fixed on the two mechanical centering structures. The main control detection module includes a main control board, a first MEMS-IMU inertial module and a laser. The slave control detection module includes a slave control board, a second MEMS-IMU inertial module, an imaging screen and a CMOS image sensor. In the flat state, the laser spot falls on the center of the imaging screen; Step 2: Control the pipeline three-dimensional distribution detection device to move forward at a constant speed in the pipeline, and at the same time obtain the data of the three-axis accelerometers and three-axis gyroscopes of the two MEMS-IMU inertial modules and the position information of the laser spot collected by the COMOS image sensor; Step 3: Process the data of the MEMS-IMU inertial module to obtain the deflection angle of the pipeline to be measured; Step 4: Process the position information of the laser spot collected by the CMOS image sensor to obtain the deflection angle of the pipeline to be measured; Step 5: Use the Kalman algorithm to fuse the deflection angle of the pipeline to be measured obtained by the CMOS image sensor and the deflection angle of the pipeline to be measured obtained by the MEMS-IMU inertial module to obtain the optimal value of the deflection angle. The specific method is as follows: Step 5.1: Establish a state equation in space: θ^ = θ 1 + K(θ 2 - θ 1 ) Among them, θ 1 is the pipeline deflection angle obtained by the MEMS-IMU inertial module, and θ 2 is the pipeline deflection angle obtained by the CMOS image sensor. K is the Kalman gain, and θ^ is the estimated value; Step 5.2: Let the standard deviation of the estimated value be σ θ , the variance of the estimated value be Var(θ^), and obtain the expression for the variance of the optimal estimated value: Among them, σ 2 θ1 is the variance of the pipeline deflection angle obtained by the MEMS-IMU inertial module, and σ 2 θ2 is the variance of the pipeline deflection angle obtained by the CMOS image sensor; Step 5.3: Find K to minimize the variance of the estimated value, and calculate the Kalman gain K: Step 5.4: Calculate the optimal estimated value at the current moment: θ^ = θ 1 + K(θ 2 - θ 1 ) Step 5.5: Update the variance of the estimated value:

2. The three-dimensional distribution testing method for pipelines based on the fusion of double attitude angles of laser spots according to claim 1, characterized in that, both the first MEMS-IMU inertial module and the second MEMS-IMU inertial module include MEMS three-axis accelerometers and MEMS three-axis gyroscopes.

3. The three-dimensional distribution testing method for pipelines based on the fusion of double attitude angles of laser spots according to claim 1, characterized in that, the specific method for processing the data of the MEMS-IMU inertial module to obtain the deflection angle of the pipeline to be measured is: Obtain the components of the gravitational acceleration in the X-axis, Y-axis, and Z-axis directions at different times through the MEMS three-axis accelerometer, and then solve the attitude angle in combination with trigonometric functions; obtain the angular velocities in the X-axis, Y-axis, and Z-axis directions at different times through the MEMS three-axis gyroscope, and calculate the deflection angle of the carrier coordinate system relative to the fixed coordinate system. Among them, the carrier coordinate system refers to the origin with the centroid of the MEMS-IMU inertial module as the origin, the X-axis along the forward direction of the carrier, the Z-axis along the side axis direction of the carrier pointing to the right wing, and the Y along the vertical axis direction of the carrier; calculate the deflection angle of the pipeline to be measured through the geometric relationship based on the deflection angles of the two mechanical centering mechanisms and the fixed length of the device.

4. The three-dimensional distribution testing method for pipelines based on the fusion of double attitude angles of laser spots according to claim 1, characterized in that, The specific method for processing the position information of the laser spot collected by the CMOS image sensor to obtain the deflection angle of the pipeline to be measured is as follows: Convert the image collected by the CMOS image sensor into a grayscale image, and perform denoising and binarization; Use the centroid method to calculate the pixel position coordinates of the laser spot; Use the least squares method for curve fitting to obtain the plane orientation map of the pipeline and get the deflection angle of the pipeline.

5. According to the pipeline three-dimensional distribution test method based on the fusion of double attitude angles of the laser spot described in claim 4, characterized in that, The specific method for using the centroid method to calculate the pixel position coordinates of the laser spot is as follows: Let the binary image containing the laser spot contour region be g(i, j), and the center coordinates of the laser spot be (x 0 , y 0 ). (i, j) are pixel coordinates, and the size of the image is M×N. Then the center coordinates of the laser spot are obtained by the following formula: The transformation from the row image pixel coordinate system (u-v) to the image physical coordinate system (X, Y), and the specific formula is: The homogeneous coordinate form is as follows: Wherein: (u, v) are the image pixel coordinates of the light spot, and (u 0 , v 0 ) are the coordinates of the origin of the image physical coordinate system in the pixel coordinate system, and (x d , y d ) are the physical position coordinates of the light spot. dx and dy respectively represent the corresponding positions of each pixel on the horizontal axis X and the vertical axis Y, and θ is the deflection angle from the image pixel coordinate system to the image physical coordinate system.

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