Method and system for flight positioning of a drone in an underground pipeline

Abstract

The application discloses a method and system for measuring the flight speed of a UAV in an underground pipeline, comprising: acquiring first video data of an upper pipe wall in a lateral pipeline when the UAV is flying in the lateral pipeline through an upper optical flow sensor; acquiring a first optical flow speed of the UAV according to the first video data; acquiring a first estimated speed of the UAV through a physical model; and fusing the first optical flow speed and the first estimated speed through a Kalman data fusion algorithm to obtain a first estimated speed of the UAV. In the embodiment, the first video data of the upper pipe wall when the UAV is flying in the lateral pipeline is acquired, and a more accurate first optical flow speed can be acquired according to the first video data, thereby solving the problem that when the UAV estimates the flight speed by acquiring image data of a lower pipe wall in an underground pipeline, accumulated water in the underground pipeline affects the optical flow estimation accuracy of the UAV.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) control methods, and in particular to a method and system for measuring the flight speed of a UAV in an underground pipeline. Background Art

[0002] The health of underground drainage pipes is crucial to a city's drainage system. Therefore, sustainable municipal development depends on management personnel identifying and promptly addressing any problems within these pipes. The long-term operation of underground pipes can lead to numerous problems. If these pipes are not effectively maintained, sludge can accumulate, hindering the smooth flow of water. This is especially true on rainy days, where excessive silt can prevent timely drainage of rainwater, leading to urban flooding and disrupting public transportation and daily life. If silt within the pipes is not promptly transported to the sewage treatment plant for removal, sewage from downstream manholes can overflow onto the road, emitting an unpleasant odor and polluting the urban environment. The core challenge with drone-based underground pipeline inspection is positioning; inaccurate positioning prevents the drone from properly performing inspections within the pipes.

[0003] The conventional positioning method uses optical flow to estimate the horizontal velocity at the bottom of the drone to achieve the positioning effect. However, water accumulation in underground pipes is a very common working condition. The surface of the accumulated water is a mirror. Therefore, optical flow cannot accurately estimate the horizontal flight speed of the drone in a mirror environment. Summary of the Invention

[0004] The present invention discloses a method and system for measuring the flight speed of a drone in an underground pipeline, so as to solve the problem in the above-mentioned background technology that the lower end of the existing drone uses an optical flow to estimate the horizontal speed and thus achieve the positioning effect. However, water accumulation in underground pipelines is a very common working condition, and the surface of the accumulated water is a mirror. Therefore, the optical flow cannot accurately estimate the horizontal flight speed of the drone in a mirror environment.

[0005] In order to solve the above technical problems, the following technical solutions are proposed:

[0006] A method for measuring the flight speed of a drone in an underground pipeline, wherein the underground pipeline includes a transverse pipeline, the drone is controlled to fly horizontally along the transverse pipeline, and an upper optical flow sensor is provided above the drone, comprising:

[0007] Acquire, by means of an upper optical flow sensor, first video data of an upper pipe wall in the transverse pipe when the drone is flying in the transverse pipe; and acquire a first optical flow velocity of the drone based on the first video data;

[0008] Get the first estimated speed of the drone;

[0009] The first optical flow velocity and the first estimated velocity are fused using a Kalman data fusion algorithm to obtain a first estimated velocity of the drone.

[0010] Preferably, the method of obtaining the first estimated speed of the UAV includes: obtaining second video data of the lower pipe wall in the transverse pipe when the UAV flies in the transverse pipe through the lower optical flow sensor, and obtaining the second optical flow speed of the UAV based on the second video data; obtaining the second estimated speed of the UAV through the physical model of the UAV; and fusing the second optical flow speed and the second estimated speed through the Kalman data fusion algorithm to obtain the first estimated speed of the UAV.

[0011] Preferably, after the first estimated speed of the UAV is obtained for the first time, the first optical flow speed and the first estimated speed obtained for the first time are fused through the Kalman data fusion algorithm to obtain the first estimated speed of the UAV for the first time; the second estimated speed of the UAV is iteratively updated based on the first estimated speed of the UAV obtained for the first time to obtain the second estimated speed after iterative update; the second optical flow speed and the second estimated speed after iterative update are fused through the Kalman data fusion algorithm to continuously obtain the first estimated speed of the UAV.

[0012] Preferably, obtaining the first optical flow velocity of the drone according to the first video data includes:

[0013] performing discretization processing on the first video data to convert it into multiple sets of continuous image data;

[0014] Extracting corner points from each set of image data using a corner detection algorithm;

[0015] The corner points in the two sets of image data of adjacent frames are brought into the optical flow algorithm to obtain the first optical flow velocity of the drone.

[0016] Preferably, fusing the first optical flow velocity and the first estimated velocity by a Kalman data fusion algorithm to obtain a second estimated velocity of the drone comprises:

[0017] Acquiring the number of corner points in the image data, and adjusting a noise parameter of the image data according to the number of corner points;

[0018] Calculate the Kalman gain according to the noise parameter;

[0019] A weighting coefficient of the first optical flow velocity and a weighting coefficient of the first estimated velocity are obtained according to the Kalman gain, and the first optical flow velocity and the first estimated velocity are multiplied by the weighting coefficients and then added to obtain a first estimated velocity of the UAV.

[0020] Also disclosed is a system for measuring the flight speed of a drone in an underground pipeline, comprising:

[0021] an upper optical flow sensor, disposed above the drone, for acquiring first video data of an upper pipe wall in the transverse pipe when the drone flies in the transverse pipe; and acquiring a first optical flow velocity of the drone based on the first video data;

[0022] A control module, configured to obtain a first estimated speed of the drone;

[0023] A Kalman filter is provided, wherein the upper optical flow sensor and the Kalman filter are both electrically connected to the control module, and the control module transmits the first optical flow velocity and the first estimated velocity to the Kalman filter. The Kalman filter fuses the first optical flow velocity and the first estimated velocity according to a Kalman data fusion algorithm to obtain a first estimated velocity of the UAV.

[0024] As an option, it also includes:

[0025] a lower optical flow sensor, disposed above the drone and electrically connected to the control module, for acquiring second video data of a lower pipe wall within the transverse pipe when the drone is flying within the transverse pipe; and acquiring a second optical flow velocity of the drone based on the second video data;

[0026] During the process of the control module acquiring the first estimated speed of the drone, the control module acquires the second estimated speed of the drone using a physical model of the drone;

[0027] The control module transmits the second optical flow velocity and the second estimated velocity to the Kalman filter, and the Kalman filter fuses the second optical flow velocity and the second estimated velocity according to a Kalman data fusion algorithm to obtain a first estimated velocity of the drone.

[0028] Preferably, after the first estimated speed of the UAV is obtained for the first time, the first optical flow speed and the first estimated speed obtained for the first time are fused through the Kalman filter Kalman data fusion algorithm to obtain the first estimated speed of the UAV for the first time; the second estimated speed of the UAV is iteratively updated based on the first estimated speed of the UAV obtained for the first time to obtain the second estimated speed after iterative update; the second optical flow speed and the second estimated speed after iterative update are fused through the Kalman data fusion algorithm to continuously obtain the first estimated speed of the UAV.

[0029] Preferably, the specific process of the upper optical flow sensor acquiring the first optical flow velocity of the drone according to the first video data includes:

[0030] The upper optical flow sensor discretizes the first video data to obtain a plurality of continuous sets of image data;

[0031] Extracting corner points from each set of image data using a corner detection algorithm;

[0032] The corner points in the two sets of image data of adjacent frames are brought into the optical flow algorithm to obtain the first optical flow velocity of the drone.

[0033] Preferably, the Kalman filter fuses the first optical flow velocity and the first estimated velocity through a Kalman data fusion algorithm to obtain a second estimated velocity of the drone, comprising:

[0034] The Kalman filter obtains the number of corner points in the image data and adjusts a noise parameter of the image data according to the number of corner points; and calculates a Kalman gain according to the noise parameter;

[0035] A weighting coefficient of the first optical flow velocity and a weighting coefficient of the first estimated velocity are obtained according to the Kalman gain, and the first optical flow velocity and the first estimated velocity are multiplied by the weighting coefficients and then added to obtain a second estimated velocity of the UAV.

[0036] Beneficial Effects: The present invention discloses a method and system for measuring the flight speed of a drone in an underground pipeline. The underground pipeline includes a transverse pipeline. A drone is controlled to fly horizontally along the transverse pipeline. An upper optical flow sensor is disposed above the drone. The method comprises: obtaining first video data of the upper wall of the transverse pipeline when the drone flies in the transverse pipeline through the upper optical flow sensor; obtaining a first optical flow velocity of the drone based on the first video data; obtaining a first estimated velocity of the drone using a physical model; and fusing the first optical flow velocity and the first estimated velocity using a Kalman data fusion algorithm to obtain a first estimated velocity of the drone. In this embodiment, since the accumulated water in the transverse pipeline is located on the lower wall of the transverse pipeline, and the upper wall of the transverse pipeline is not affected by the accumulated water due to gravity, a more accurate first optical flow velocity can be obtained by obtaining first video data of the upper wall of the transverse pipeline when the drone flies in the transverse pipeline and based on the first video data. This solves the problem that the accumulated water in the underground pipeline affects the accuracy of the drone's optical flow estimation when the drone estimates its flight speed by obtaining image data of the lower wall of the underground pipeline. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a control flow chart of the present invention;

[0038] Figure 2 This is a diagram of the framework structure of the present invention. DETAILED DESCRIPTION

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0040] The following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort shall fall within the scope of protection of the present invention.

[0041] In the city's drainage system, the health of underground drainage pipes is very important. Therefore, only when managers discover problems in underground drainage pipes and propose solutions in a timely manner can the sustainable development of the city be achieved. Due to the long-term operation of underground pipes, many problems will arise. If the relevant staff fail to effectively maintain the pipes, a lot of sludge will appear in the drainage pipes, which will affect the smooth passage of pipe drainage. Especially on rainy days, if too much mud accumulates in the pipes, it will lead to the inability to drain rainwater in time, which can easily cause urban waterlogging and affect citizens' travel and life. If the silt in the pipes cannot be transported to the sewage treatment plant for cleaning in time, the sewage in the downstream inspection wells will overflow onto the road surface, emitting an unpleasant odor and polluting the urban environment. However, the underground drainage pipe environment is harsh and the network is complex. It is impossible to effectively maintain the underground drainage pipe network through manual inspections. Therefore, in the present invention, a method of remote sensing detection of pipes by remote operation of drones is adopted to conduct quick and efficient inspections of underground drainage pipes.

[0042] However, the underground drainage pipe network is very changeable. Therefore, when controlling the drone to fly horizontally along the horizontal pipe in the underground pipe, it is necessary to accurately locate the position of the drone to ensure that the drone can fly normally in the pipe. Inspection is the primary problem to be solved at present. The current conventional positioning method for drones is to use GPS or use an optical flow at the lower end of the drone to estimate the horizontal speed, thereby achieving the positioning effect. First, since the underground drainage pipe is buried deep underground, the GPS will cause inaccurate positioning signals due to occlusion, and thus cannot effectively control the drone to fly horizontally along the horizontal pipe in the underground pipe. Secondly, water accumulation in the underground pipe is a very common working condition. The surface of the accumulated water is a mirror. The lower end of the drone uses an optical flow to estimate the horizontal speed. Since the optical flow cannot estimate the accurate speed in the mirror environment, the optical flow cannot achieve a good positioning function. Therefore, in order to solve the above problems, the present invention discloses a method for measuring the flight speed of a drone in an underground pipe. Refer to the attached figure. Figure 1-2The underground pipeline includes a transverse pipeline, and a drone is controlled to fly horizontally along the transverse pipeline. An upper optical flow sensor is arranged above the drone, including: obtaining first video data of the upper pipe wall in the transverse pipeline when the drone flies in the transverse pipeline through the upper optical flow sensor; and obtaining a first optical flow velocity of the drone based on the first video data; the drone obtains a first estimated velocity of the drone through a physical model; and obtains a first estimated velocity of the drone by fusing the first optical flow velocity and the first estimated velocity through a Kalman data fusion algorithm. In this embodiment, since the accumulated water in the transverse pipe is located on the lower wall of the transverse pipe, and the upper wall of the transverse pipe is affected by gravity and will not be affected by the accumulated water, in this embodiment, by obtaining the first video data of the upper wall when the drone flies in the transverse pipe, and based on the first video data, a more accurate first optical flow velocity can be obtained, thereby solving the problem that when the drone estimates the flight speed of the drone by obtaining image data of the lower wall of the underground pipe, the accumulated water in the underground pipe will affect the accuracy of the drone's optical flow estimation, thereby greatly increasing the accuracy of the drone's positioning. Secondly, in this embodiment, the drone obtains the first estimated speed of the drone through a physical model; then the first optical flow velocity and the first estimated speed are fused through the Kalman data fusion algorithm to obtain a more accurate first estimated speed of the drone, further improving the accuracy of the drone's positioning.

[0043] Another feasible implementation scheme is also provided in this embodiment. In this embodiment, a lower optical flow sensor is provided under the drone, and second video data of the lower pipe wall in the transverse pipe when the drone flies in the transverse pipe is obtained through the lower optical flow sensor; and a second optical flow velocity of the drone is obtained based on the second video data; a second estimated velocity of the drone is obtained based on the physical model of the drone; the second optical flow velocity and the second estimated velocity are fused through the Kalman data fusion algorithm to obtain a first estimated velocity of the drone, and the first estimated velocity calculated by the lower optical flow sensor is fused with the first optical flow velocity obtained by the upper optical flow sensor to obtain a more accurate first estimated velocity of the drone, thereby increasing the robustness of the drone and further improving the accuracy of drone positioning.

[0044] In this embodiment, when the drone flies in the transverse pipe, the drone needs to repeatedly perform the above steps to obtain the first estimated speed of the drone in real time. During the process of repeating the above steps, the drone will iteratively update the second estimated speed of the drone, thereby achieving a more accurate positioning effect. The specific method for the drone to iteratively update the second estimated speed of the drone is as follows:

[0045] After the first estimated speed of the UAV is obtained for the first time, the first optical flow speed and the first estimated speed obtained for the first time are fused through the Kalman data fusion algorithm to obtain the first estimated speed of the UAV for the first time; the second estimated speed of the UAV is iteratively updated based on the first estimated speed of the UAV obtained for the first time to obtain the second estimated speed after iterative update; the second optical flow speed and the second estimated speed after iterative update are fused through the Kalman data fusion algorithm to continuously obtain the first estimated speed of the UAV.

[0046] Specifically, take the drone obtaining the first estimated speed for the first time and obtaining the first estimated speed for the second time as an example.

[0047] When the drone initially obtains a first estimated speed, the drone obtains first video data of the upper wall of the transverse pipe when the drone flies in the transverse pipe through the upper optical flow sensor; and obtains the first optical flow speed of the drone based on the first video data; obtains second video data of the lower wall of the transverse pipe when the drone flies in the transverse pipe through the lower optical flow sensor; and obtains the second optical flow speed of the drone based on the second video data; obtains the second estimated speed of the drone based on the physical model of the drone; and obtains the first estimated speed of the drone by fusing the second optical flow speed and the second estimated speed through the Kalman data fusion algorithm. Finally, the drone obtains the first estimated speed of the drone by fusing the first optical flow speed and the first estimated speed through the Kalman data fusion algorithm.

[0048] When obtaining the first estimated speed for the second time, the drone obtains first video data of the upper pipe wall in the transverse pipe when the drone is flying in the transverse pipe through the upper optical flow sensor; and obtains the first optical flow speed of the drone based on the first video data;

[0049] obtaining, by means of a lower optical flow sensor, second video data of a lower pipe wall in the transverse pipe when the drone is flying in the transverse pipe; and obtaining a second optical flow velocity of the drone based on the second video data;

[0050] Using the first estimated speed obtained when the drone initially obtains the first estimated speed as the second estimated speed of the drone;

[0051] The second optical flow velocity and the second estimated velocity at this time are fused through the Kalman data fusion algorithm to obtain the first estimated velocity of the UAV. Finally, the first optical flow velocity and the first estimated velocity are fused through the Kalman data fusion algorithm to obtain the first estimated velocity of the UAV.

[0052] In this embodiment, in the process of obtaining the first optical flow velocity of the drone based on the first video data, the drone discretizes the first video data and converts it into continuous image data; the corner points in each set of image data are extracted through a corner detection algorithm; the corner points in two sets of image data of adjacent frames are brought into the optical flow algorithm to obtain the first optical flow velocity of the drone, wherein the corner points are points in the image where the brightness changes significantly. In this application, by obtaining the corner points in the image data to measure the first optical flow velocity of the drone, the accuracy of measuring the optical flow can be greatly improved.

[0053] In this embodiment, in obtaining the first estimated speed of the UAV by fusing the first optical flow speed and the first estimated speed through the Kalman data fusion algorithm: the number of corner points in the image data is obtained, and the noise parameter of the image data is adjusted according to the number of corner points; the Kalman gain is calculated according to the noise parameter; the weighting coefficient of the first optical flow speed and the weighting coefficient of the first estimated speed are obtained according to the Kalman gain, the first optical flow speed and the first estimated speed are multiplied by the weighting coefficient and then added to obtain the first estimated speed of the UAV. In this embodiment, after the first optical flow speed and the first estimated speed are weighted and superimposed according to the Kalman gain to obtain the first estimated speed of the UAV, the accuracy of the UAV state estimation is greatly improved and the accuracy of the UAV positioning is further improved.

[0054] The present invention also discloses a system for measuring the flight speed of a drone in an underground pipeline, comprising an upper optical flow sensor, a control module, and a Kalman filter. The upper optical flow sensor is arranged above the drone, and the upper optical flow sensor and the Kalman filter are both electrically connected to the control module. The upper optical flow sensor is used to obtain first video data of the upper wall of the transverse pipeline when the drone flies in the transverse pipeline; and obtain a first optical flow velocity of the drone based on the first video data; after obtaining a first estimated speed of the drone, the control module transmits the first optical flow velocity and the first estimated speed to the Kalman filter, and the Kalman filter fuses the first optical flow velocity and the first estimated speed according to the Kalman data fusion algorithm to obtain a first estimated speed of the drone. In this embodiment, since the accumulated water in the transverse pipeline is located on the lower wall of the transverse pipeline, and the upper wall of the transverse pipeline is affected by gravity and will not be affected by the accumulated water, in this embodiment, by obtaining the first video data of the upper wall of the drone when flying in the transverse pipeline, a more accurate first optical flow velocity can be obtained based on the first video data, thereby greatly increasing the accuracy of the drone positioning.

[0055] In this embodiment, the system for measuring the flight speed of a drone in an underground pipeline also includes a lower optical flow sensor, which is arranged above the drone and electrically connected to the control module. The lower optical flow sensor is used to obtain second video data of the lower pipe wall in the transverse pipeline when the drone flies in the transverse pipeline; and obtain the second optical flow velocity of the drone based on the second video data; in the process of the control module obtaining the first estimated speed of the drone, the control module obtains the second estimated speed of the drone through the physical model of the drone; the control module transmits the second optical flow velocity and the second estimated speed to the Kalman filter, and the Kalman filter fuses the second optical flow velocity and the second estimated speed according to the Kalman data fusion algorithm to obtain the first estimated speed of the drone. In this embodiment, the first estimated speed calculated by the lower optical flow sensor is fused with the first optical flow velocity obtained by the upper optical flow sensor to obtain a more accurate first estimated speed of the drone, thereby increasing the robustness of the drone and further improving the accuracy of drone positioning.

[0056] In this embodiment, when the drone flies in the transverse pipe, the control module of the drone needs to obtain the first estimated speed of the drone in real time. In the process of obtaining the first estimated speed of the drone in real time, the control module of the drone will iteratively update the second estimated speed of the drone, thereby achieving a more accurate positioning effect.

[0057] When the control module first obtains the first estimated speed, it calculates the current second estimated speed of the drone through the physical model of the drone. The drone fuses the second optical flow speed and the second estimated speed through the Kalman data fusion algorithm to obtain the first estimated speed of the drone. The first estimated speed calculated by the lower optical flow sensor is fused with the first optical flow speed obtained by the upper optical flow sensor to obtain a more accurate first estimated speed of the drone.

[0058] When the control module is not acquiring the first estimated speed for the first time, it uses the first estimated speed acquired last time as the second estimated speed required for acquiring the first estimated speed this time, and calculates a more accurate first estimated speed based on the second estimated speed required for acquiring the first estimated speed this time, thereby greatly improving the accuracy of measuring optical flow.

[0059] In this embodiment, the upper optical flow sensor obtains the first optical flow speed of the drone based on the first video data: the upper optical flow sensor discretizes the first video data and converts it into continuous image data; the corner points in each group of image data are extracted through the corner detection algorithm; the upper optical flow sensor brings the corner points in the two groups of image data of adjacent frames into the optical flow algorithm to obtain the first optical flow speed of the drone. In this application, the accuracy of measuring the optical flow can be greatly improved by obtaining the corner points in the image data.

[0060] In this embodiment, the Kalman filter fuses the first optical flow velocity and the first estimated velocity through the Kalman data fusion algorithm to obtain the second estimated velocity of the UAV: ​​the Kalman filter obtains the number of corner points in the image data, and adjusts the noise parameter of the image data according to the number of corner points; the Kalman gain is calculated according to the noise parameter; the weighting coefficient of the first optical flow velocity and the weighting coefficient of the first estimated velocity are obtained according to the Kalman gain, the first optical flow velocity and the first estimated velocity are multiplied by the weighting coefficient and then added to obtain the second estimated velocity of the UAV. In this embodiment, after the first optical flow velocity and the first estimated velocity are weighted and superimposed according to the Kalman gain to obtain the first estimated velocity of the UAV, the accuracy of the UAV state estimation is greatly improved, and the accuracy of the UAV positioning is further improved.

[0061] The above disclosures are only several specific embodiments of the present invention, but the present invention is not limited thereto. Any changes that can be conceived by those skilled in the art should fall within the scope of protection of the present invention.

Claims

1. A method for measuring the flight speed of a drone in an underground pipeline, wherein the underground pipeline includes a transverse pipeline, the drone is controlled to fly horizontally along the transverse pipeline, and an upper optical flow sensor is provided above the drone, characterized in that: include: Acquiring first video data of an upper pipe wall in the transverse pipe when the drone flies in the transverse pipe by an upper optical flow sensor; and obtaining a first optical flow velocity of the drone based on the first video data; Get the first estimated speed of the drone; fusing the first optical flow velocity and the first estimated velocity using a Kalman data fusion algorithm to obtain a first estimated velocity of the drone; The obtaining of the first optical flow velocity of the drone according to the first video data includes: performing discretization processing on the first video data to convert it into multiple sets of continuous image data; Extracting corner points from each set of image data using a corner detection algorithm; Substituting the corner points in the two sets of image data of adjacent frames into the optical flow algorithm to obtain a first optical flow velocity of the drone; The step of fusing the first optical flow velocity and the first estimated velocity using a Kalman data fusion algorithm to obtain a first estimated velocity of the drone includes: Acquiring the number of corner points in the image data, and adjusting a noise parameter of the image data according to the number of corner points; Calculate the Kalman gain according to the noise parameter; A weighting coefficient of the first optical flow velocity and a weighting coefficient of the first estimated velocity are obtained according to the Kalman gain, and the first optical flow velocity and the first estimated velocity are multiplied by the weighting coefficients and then added to obtain a first estimated velocity of the UAV.

2. The method for measuring the flight speed of a drone in an underground pipeline according to claim 1, wherein a lower optical flow sensor is provided below the drone, and wherein: Obtaining the first estimated speed of the drone includes: obtaining second video data of the lower pipe wall in the transverse pipe when the drone flies in the transverse pipe through the lower optical flow sensor, and obtaining a second optical flow speed of the drone based on the second video data; obtaining the second estimated speed of the drone through a physical model of the drone; and fusing the second optical flow speed and the second estimated speed through a Kalman data fusion algorithm to obtain the first estimated speed of the drone.

3. The method for measuring the flight speed of a drone in an underground pipeline according to claim 2, wherein a lower optical flow sensor is provided below the drone, and wherein: After first obtaining the first estimated speed of the UAV, fusing the first optical flow speed and the first estimated speed obtained for the first time through a Kalman data fusion algorithm to obtain a first estimated speed of the UAV for the first time; Iteratively updating the second estimated speed of the drone based on the first estimated speed of the drone to obtain an iteratively updated second estimated speed; The second optical flow velocity and the iteratively updated second estimated velocity are fused through a Kalman data fusion algorithm to continuously obtain a first estimated velocity of the drone.

4. A system for measuring the flight speed of a drone in an underground pipeline, characterized in that: include: an upper optical flow sensor, disposed above the UAV, for acquiring first video data of an upper pipe wall in the transverse pipe when the UAV flies in the transverse pipe; and obtaining a first optical flow velocity of the drone based on the first video data; A control module, configured to obtain a first estimated speed of the drone; A Kalman filter, wherein the upper optical flow sensor and the Kalman filter are both electrically connected to the control module, the control module transmits the first optical flow velocity and the first estimated velocity to the Kalman filter, and the Kalman filter fuses the first optical flow velocity and the first estimated velocity according to a Kalman data fusion algorithm to obtain a first estimated velocity of the UAV; The specific process of the upper optical flow sensor acquiring the first optical flow velocity of the drone according to the first video data includes: The upper optical flow sensor discretizes the first video data to obtain a plurality of continuous sets of image data; Extracting corner points from each set of image data using a corner detection algorithm; Substituting the corner points in the two sets of image data of adjacent frames into the optical flow algorithm to obtain a first optical flow velocity of the drone; The Kalman filter fuses the first optical flow velocity and the first estimated velocity using a Kalman data fusion algorithm to obtain a first estimated velocity of the drone, comprising: The Kalman filter obtains the number of corner points in the image data and adjusts a noise parameter of the image data according to the number of corner points; and calculates a Kalman gain according to the noise parameter; A weighting coefficient of the first optical flow velocity and a weighting coefficient of the first estimated velocity are obtained according to the Kalman gain, and the first optical flow velocity and the first estimated velocity are multiplied by the weighting coefficients and then added to obtain a first estimated velocity of the UAV.

5. The system for measuring the flight speed of a drone in an underground pipeline according to claim 4, characterized in that: Also includes: a lower optical flow sensor, disposed below the drone and electrically connected to the control module, the lower optical flow sensor being configured to acquire second video data of a lower pipe wall within the transverse pipe when the drone is flying within the transverse pipe; and to acquire a second optical flow velocity of the drone based on the second video data; During the process of the control module acquiring the first estimated speed of the drone, the control module acquires the second estimated speed of the drone using a physical model of the drone; The control module transmits the second optical flow velocity and the second estimated velocity to the Kalman filter, and the Kalman filter fuses the second optical flow velocity and the second estimated velocity according to a Kalman data fusion algorithm to obtain a first estimated velocity of the UAV.

6. The system for measuring the flight speed of a drone in an underground pipeline according to claim 5, characterized in that: After first obtaining a first estimated speed of the drone, fusing the first optical flow speed and the first estimated speed obtained for the first time using the Kalman filter according to a Kalman data fusion algorithm to obtain a first estimated speed of the drone for the first time; Iteratively updating the second estimated speed of the drone based on the first estimated speed of the drone to obtain an iteratively updated second estimated speed; The second optical flow velocity and the iteratively updated second estimated velocity are fused through a Kalman data fusion algorithm to continuously obtain a first estimated velocity of the drone.

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