Pipeline distance determination method based on three-vision ranging technology

Abstract

The present application relates to a pipeline distance determination method based on a three-view vision ranging technology, which is applied to a patrol vehicle measurement, the patrol vehicle is provided with three cameras, the three cameras are distributed in a triangular shape, and the method comprises the following steps: step one: establishing a rectangular plane coordinate system with a pipeline to be measured as a horizontal axis, the optical center coordinates of the three cameras are O1, O2 and O3 respectively, the coordinates of a measurement target to be solved are X, Y and Z, and the real-time distances of the three cameras to the measurement target are L1, L2 and L3 respectively. The present application can realize accurate distance calculation of a risk object to a pipeline through multi-baseline intersection positioning, and provide reliable data support for hierarchical early warning.

Description

Technical Field

[0001] This invention relates to the field of visual ranging technology, and specifically to a method for determining pipeline distance based on trinocular visual ranging technology. Background Technology

[0002] As a core infrastructure for energy transmission, pipelines' safe operation is directly related to public safety and economic stability. Currently, pipeline inspections are still primarily manual, which suffers from low efficiency, limited coverage, and delayed response to risks from third-party construction. With the advancement of smart pipeline network construction, the industry has begun exploring smart inspection solutions centered on vehicle-mounted cameras, edge computing boxes, and AI vision algorithms. By identifying high-risk objects such as excavators, pipe jacking machines, and construction barriers, these solutions enable automatic early warnings and backend push notifications for construction risks, significantly improving inspection efficiency and response speed.

[0003] However, existing solutions have significant shortcomings in accurately determining the distance between risk objects and pipelines: the current common method is an indirect estimation using "vehicle GPS + pipeline coordinates," which substitutes the distance between the vehicle and the pipeline for the distance between the risk object and the pipeline. That is, when the distance between the vehicle and the pipeline is greater than a preset threshold (e.g., 30m), it is assumed that there is no risk warning for the risk object; when the distance is less than the threshold, a warning is issued. This simplified approach completely ignores the actual positional deviation between the risk object and the vehicle, resulting in large distance calculation errors and inaccurate warning judgments. It is very easy to have "missed warnings" and "false warnings," and it cannot meet the core requirement of accurate distance measurement for pipeline safety management. Summary of the Invention

[0004] The purpose of this invention is to provide a pipeline distance judgment method based on trinocular vision ranging technology, which can achieve accurate distance calculation from risk objects to pipelines through multi-baseline cross-positioning, providing reliable data support for graded early warning.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A pipeline distance determination method based on trinocular vision ranging technology is applied to the measurement of inspection vehicles. The inspection vehicle is equipped with three cameras arranged in a triangle. The method is characterized by the following steps: Step 1: Establish a rectangular plane coordinate system with the pipe to be measured as the horizontal axis. The optical center coordinates of the three cameras are as follows: , , The coordinates of the target to be measured are: The measured real-time distances from the three cameras to the target were as follows: , , ; Based on the formula for the distance between two points, establish a system of equations; Formula 1: ; Formula 2: ; Formula 3: ; Subtracting equation two from equation one, we get equation four:

[0006] Subtracting equation 3 from equation 2, we get equation 5:

[0007] Step 2: Solve the system of two linear equations in two variables consisting of equations 4 and 5: make:

[0008]

[0009]

[0010]

[0011]

[0012] The above system of two linear equations in two variables simplifies to:

[0013]

[0014] Solve the above system of equations using Cramer's rule: The coefficient determinant is:

[0015] like If the three cameras are approximately collinear, or the ranging data is abnormal, the solution will fail. like The system of equations has a unique solution:

[0016] ; Step 3: Calculate the distance between the target and the pipeline. : A rectangular plane coordinate system is established with the pipe under test as the horizontal axis and the target as the vertical axis. To measure the distance between the target and the pipe ,Right now: ; Step 4: Obtain the calculated Substituting back into equations one, two, and three respectively, we obtain... , , The calculated value, if , , If the deviation between the calculated value and its actual measured value exceeds the threshold, it is determined that the lens is blocked or the ranging noise is too large, and the current frame data is discarded. like , , If the deviation between the calculated value and its respective measured value does not exceed the threshold, then for Perform Kalman filtering: initialization: , Iteration formula:

[0017]

[0018]

[0019]

[0020]

[0021] Where k = 1, 2, 3, ..., n.

[0022] Specifically, the optical centers of the three cameras form an isosceles triangle.

[0023] Specifically, each of the three cameras is equipped with a range sensor, which measures the real-time distance from the corresponding camera to the target, and serves as the measured distance value. , , The ranging sensor is a laser sensor, an ultrasonic sensor, or an infrared sensor.

[0024] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) This scheme abandons the traditional indirect estimation mode of "vehicle-pipeline". It uses a tri-camera to simultaneously acquire on-site images, and combines AI vision algorithms to identify construction targets such as excavators, pipe jacking machines, and construction fences, and outputs their image coordinates S1, S2, and S3; then, through multi-baseline stereo matching and geometric calculation, the actual geographic coordinates E(x) of the risk object are directly obtained. e ,y e This method completely eliminates the relative positional deviation between the risk object and the vehicle-mounted equipment, upgrading distance measurement from "indirect estimation" to "direct calculation," with significantly better ranging accuracy than the traditional GPS estimation method.

[0025] (2) Multi-level data verification mechanism ensures the reliability of the solution. The solution incorporates a data verification module after geometric calculation to screen the rationality, consistency, and stability of the coordinate solutions in real time. It automatically identifies and removes invalid data caused by image occlusion, abnormal lighting, or false target detection. When verification fails, the system automatically triggers a "re-acquisition" process to ensure that the distance data entering subsequent stages is accurate and reliable, thus preventing false or missed warnings caused by anomalies in a single measurement.

[0026] (3) Kalman filtering smooths noise reduction and improves the stability of distance output. To address the measurement noise and random fluctuations present in the raw calculated distance S4_raw, this solution introduces a one-dimensional Kalman filter module for dynamic smoothing. By establishing a recursive model for state prediction and observation update, high-frequency noise is effectively suppressed, resulting in a stable and continuous distance value S4. This provides a stable and reliable data foundation for subsequent graded threshold determination, avoiding frequent false triggering of warnings due to distance jumps.

[0027] (4) Tiered threshold early warning and back-end linkage improve control efficiency. Based on the stabilized and accurate distance S4, the system supports setting multiple levels of safety thresholds (such as safe zones, warning zones, and danger zones), and automatically triggers corresponding early warning actions according to different risk levels, while simultaneously pushing early warning information to the back-end management platform in real time. This mechanism not only reduces the burden of manual judgment on inspection personnel, but also realizes closed-loop management of "early detection, early warning, and early response," significantly improving the intelligence level and response efficiency of pipeline safety management. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a schematic diagram of the process of the present invention; Figure 2 This is a schematic diagram of the coordinate system of the present invention. Detailed Implementation

[0030] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0031] See Figure 1 , Figure 2 A pipeline distance determination method based on trinocular vision ranging technology is applied to the measurement of inspection vehicles. The inspection vehicle is equipped with three cameras arranged in a triangle. The method includes the following steps: Step 1: Establish a rectangular plane coordinate system with the pipe to be measured as the horizontal axis. The optical center coordinates of the three cameras are as follows: , , The coordinates of the target to be measured are: The measured real-time distances from the three cameras to the target were as follows: , , ; Based on the formula for the distance between two points, establish a system of equations; Formula 1: ; Formula 2: ; Formula 3: ; Subtracting equation two from equation one, we get equation four:

[0032] Subtracting equation 3 from equation 2, we get equation 5:

[0033] Step 2: Solve the system of two linear equations in two variables consisting of equations 4 and 5: make:

[0034]

[0035]

[0036]

[0037]

[0038] The above system of two linear equations in two variables simplifies to:

[0039]

[0040] Solve the above system of equations using Cramer's rule: The coefficient determinant is:

[0041] like If the three cameras are approximately collinear, or the ranging data is abnormal, the solution will fail. like The system of equations has a unique solution:

[0042] ; Step 3: Calculate the distance between the target and the pipeline. : A rectangular plane coordinate system is established with the pipe under test as the horizontal axis and the target as the vertical axis. To measure the distance between the target and the pipe ,Right now: ; Step 4: Obtain the calculated Substituting back into equations one, two, and three respectively, we obtain... , , The calculated value, if , , If the deviation between the calculated value and its actual measured value exceeds the threshold, it is determined that the lens is blocked or the ranging noise is too large, and the current frame data is discarded. like , , If the deviation between the calculated value and its respective measured value does not exceed the threshold, then for Perform Kalman filtering: initialization: , Iteration formula:

[0043]

[0044]

[0045]

[0046]

[0047] Where k = 1, 2, 3, ..., n.

[0048] Specifically, the optical centers of the three cameras form an isosceles triangle.

[0049] Specifically, each of the three cameras is equipped with a range sensor, which measures the real-time distance from the corresponding camera to the target, and serves as the measured distance value. , , The ranging sensor is a laser sensor, an ultrasonic sensor, or an infrared sensor.

[0050] The partition corresponding to the final stable distance of the Kalman filter output

[0051] Example 1 Original distance: S4raw=30.05

[0052]

[0053]

[0054]

[0055]

[0056] Judgment: S4=30.05>30, safe range, no warning triggered; only distance and on-site captured images are stored.

[0057] Example 2 (Continued from Example 1) Original distance: S4raw=20.85

[0058]

[0059]

[0060]

[0061]

[0062] Judgment: S4=25.92, (15<25.92≤30), Level 1 alert warning; the background blue ledger records and pushes a message: the excavator at this point is 25.92m away from the pipeline and has entered the control zone, and will be continuously monitored remotely.

[0063] The beneficial effects of this invention are: (1) Achieve direct and accurate distance measurement from the risk object to the pipeline. This solution abandons the traditional indirect "vehicle-pipeline" estimation model. It simultaneously acquires on-site images using a tri-camera system, combines this with AI vision algorithms to identify construction targets such as excavators, pipe jacking machines, and construction barriers, and outputs their image coordinates S1, S2, and S3. Then, through multi-baseline stereo matching and geometric calculation, the actual geographic coordinates E(x) of the risk object are directly obtained. e ,y eThis method completely eliminates the relative positional deviation between the risk object and the vehicle-mounted equipment, upgrading distance measurement from "indirect estimation" to "direct calculation," with significantly better ranging accuracy than the traditional GPS estimation method.

[0064] (2) Multi-level data verification mechanism ensures the reliability of the solution. The solution incorporates a data verification module after geometric calculation to screen the rationality, consistency, and stability of the coordinate solutions in real time. It automatically identifies and removes invalid data caused by image occlusion, abnormal lighting, or false target detection. When verification fails, the system automatically triggers a "re-acquisition" process to ensure that the distance data entering subsequent stages is accurate and reliable, thus preventing false or missed warnings caused by anomalies in a single measurement.

[0065] (3) Kalman filtering smooths noise reduction and improves the stability of distance output. To address the measurement noise and random fluctuations present in the raw calculated distance S4_raw, this solution introduces a one-dimensional Kalman filter module for dynamic smoothing. By establishing a recursive model for state prediction and observation update, high-frequency noise is effectively suppressed, resulting in a stable and continuous distance value S4. This provides a stable and reliable data foundation for subsequent graded threshold determination, avoiding frequent false triggering of warnings due to distance jumps.

[0066] (4) Tiered threshold early warning and back-end linkage improve control efficiency. Based on the stabilized and accurate distance S4, the system supports setting multiple levels of safety thresholds (such as safe zones, warning zones, and danger zones), and automatically triggers corresponding early warning actions according to different risk levels, while simultaneously pushing early warning information to the back-end management platform in real time. This mechanism not only reduces the burden of manual judgment on inspection personnel, but also realizes closed-loop management of "early detection, early warning, and early response," significantly improving the intelligence level and response efficiency of pipeline safety management.

[0067] In summary, this solution significantly improves upon existing technologies in terms of ranging accuracy, data reliability, system stability, and engineering practicality. It can effectively meet the actual needs of precise risk management for third-party construction in pipeline scenarios such as oil and gas and chemical industries, and provide strong technical support for the construction of smart pipeline networks.

[0068] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A pipeline distance judgment method based on trinocular vision ranging technology, applied to inspection vehicle measurement. The inspection vehicle is equipped with three cameras arranged in a triangle. Its characteristic is that... Includes the following steps: Step 1: Establish a rectangular plane coordinate system with the pipe to be measured as the horizontal axis. The optical center coordinates of the three cameras are as follows: , , The coordinates of the target to be measured are: The measured real-time distances from the three cameras to the target were as follows: , , ; Based on the formula for the distance between two points, establish a system of equations; Formula 1: ; Formula 2: ; Formula 3: ; Subtracting equation two from equation one, we get equation four: Subtracting equation 3 from equation 2, we get equation 5: Step 2: Solve the system of two linear equations in two variables consisting of equations 4 and 5: make: The above system of two linear equations in two variables simplifies to: Solve the above system of equations using Cramer's rule: The coefficient determinant is: like If the three cameras are approximately collinear, or the ranging data is abnormal, the solution will fail. like The system of equations has a unique solution: ； Step 3: Calculate the distance between the target and the pipeline. : A rectangular plane coordinate system is established with the pipe under test as the horizontal axis and the target as the vertical axis. To measure the distance between the target and the pipe ,Right now: ； Step 4: Obtain the calculated Substituting back into equations one, two, and three respectively, we obtain... , , The calculated value, if , , If the deviation between the calculated value and its actual measured value exceeds the threshold, it is determined that the lens is blocked or the ranging noise is too large, and the current frame data is discarded. like , , If the deviation between the calculated value and its respective measured value does not exceed the threshold, then for Perform Kalman filtering: initialization: ， Iteration formula: Where k = 1, 2, 3, ..., n.

2. The pipeline distance determination method based on trinocular vision ranging technology according to claim 1, characterized in that: The optical centers of the three cameras form an isosceles triangle.

3. The pipeline distance determination method based on trinocular vision ranging technology according to claim 1, characterized in that: Each of the three cameras is equipped with a range sensor, which measures the real-time distance from the corresponding camera to the target, and serves as the measured distance value. , , The ranging sensor is a laser sensor, an ultrasonic sensor, or an infrared sensor.

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