阅读说明：本技术 一种基于LiDAR的无人机桥底检测系统 (Unmanned aerial vehicle bridge bottom detection system based on LiDAR ) 是由 张晓明 蒋盛川 钟盛 于 2021-08-18 设计创作，主要内容包括：本发明涉及一种基于LiDAR的无人机桥底检测系统,包括：无人机、LiDAR采集及导航模块、图像采集模块、传输模块、地面控制中心,所述无人机通过搭载所述LiDAR采集及导航模块和图像采集模块进行现场信息的采集,并通过所述传输模块将采集到的信息传输至所述地面控制中心。本发明的桥底检测系统以无人机为平台,成本低、效率高、可靠性高、灵活性大,且检测时间不受限制,同时通过避障信息的检测,提高了无人机飞行的安全性。与传统检测相比,本发明所述设备可以获取更为全面的桥梁信息。(The invention relates to an unmanned aerial vehicle bridge bottom detection system based on LiDAR, comprising: unmanned aerial vehicle, LiDAR gathers and navigation module, image acquisition module, transmission module, ground control center, unmanned aerial vehicle is through carrying on LiDAR gathers and navigation module and image acquisition module carry out the collection of on-the-spot information, and pass through transmission module with the information transmission who gathers extremely ground control center. The bridge bottom detection system takes the unmanned aerial vehicle as a platform, has low cost, high efficiency, high reliability and great flexibility, is not limited in detection time, and improves the flight safety of the unmanned aerial vehicle by detecting the obstacle avoidance information. Compared with the traditional detection, the equipment can acquire more comprehensive bridge information.)

1. An unmanned aerial vehicle bridge bottom detection system based on LiDAR, comprising:

unmanned aerial vehicle: the system is used for completing flight function and providing a carrier for other modules;

LiDAR acquisition and navigation module: the system comprises a GPS unit and an inertia measurement unit, wherein the GPS unit and the inertia measurement unit are used for positioning the unmanned aerial vehicle and acquiring obstacle information and bridge three-dimensional information;

an image acquisition module: the system comprises a wide-angle camera and a memory, and is used for shooting and storing the condition of a detection area;

a transmission module: the system is used for transmitting the obstacle information, the dynamic bridge variation and the image information acquired by the image acquisition module;

a ground control center: the system is used for processing the received information, making a flight route, detecting an abnormal area and evaluating the abnormal area;

the unmanned aerial vehicle carries the LiDAR acquisition and navigation module and the image acquisition module to acquire field information, and transmits the acquired information to the ground control center through the transmission module.

2. The LiDAR-based drone bridge floor detection system according to claim 1, wherein the inertial measurement unit is used to scan three-dimensional point cloud information for areas including bridges, bridge floors, pedestals, and form three-dimensional point cloud data.

3. The LiDAR-based drone underbridge detection system of claim 2, wherein the LiDAR acquisition and navigation module further comprises a data processing unit for detecting three-dimensional point cloud data scanned by the inertial measurement unit.

4. The LiDAR-based drone underbridge detection system of claim 3, wherein the three-dimensional point cloud data is detected by a noise reduction method for locating the bridge length, width, depth, height, and position information to obtain the obstacle information and the bridge dynamic variation.

5. The LiDAR-based drone underbridge detection system according to claim 1, wherein the ground control center includes a processing unit and a detection and location unit and an anomaly evaluation unit;

the processing unit is used for correcting the received image and formulating a flight route of the unmanned aerial vehicle according to the received obstacle information;

the detection positioning unit is used for positioning the detected abnormal area;

the abnormity evaluation unit is used for evaluating the abnormity condition of the abnormity area.

6. The LiDAR-based unmanned aerial vehicle underbridge detection system of claim 5, wherein the processing unit corrects, deblurs, and image enhances the received images, and performs a stitching operation based on the captured images between two piers to form a large-scale high-definition image.

7. The LiDAR-based unmanned aerial vehicle bridge bottom detection system of claim 5, wherein the detection positioning unit is used for detecting and positioning pier defects, column shaft inclination, bridge bottom surface cracks and uneven telescopic surface problems in the large-scale high-definition image.

8. The LiDAR-based unmanned aerial vehicle bridge bottom detection system of claim 5, wherein the anomaly evaluation unit performs crack measurement and density analysis according to a problem occurring in the bridge, and finally obtains a fault type of the bridge.

Technical Field

The invention relates to the technical field of bridge rapid inspection, in particular to an unmanned aerial vehicle bridge bottom detection system based on LiDAR.

Background

The monitoring and analysis of the bridge bottom condition is a basic task of bridge health monitoring. Currently, the bridge bottom detection technology mainly has two modes of manual detection and unmanned aerial vehicle detection. The manual detection is carried out by large-scale machinery such as a bridge detection vehicle to send bridge detection experts to the bottom of the bridge, cracks are searched by naked eyes, and the sizes of the cracks are measured by a crack observation instrument or a ruler. The manual detection method has low efficiency, high cost and potential safety hazard; the unmanned aerial vehicle detection is usually carried out by adopting a video image method, is greatly influenced by positioning precision and is difficult to identify microscopic damage. Unmanned aerial vehicle also has a great deal of problem when flying under the bridge, for example GPS signal loses, the location is inaccurate, remote control signal is lost because of sheltering from, the barrier avoids etc. this makes unmanned aerial vehicle need carry out self location with the help of other sensors, will improve autonomic flight ability simultaneously, just can avoid striking obstacle or crash when flying under the bridge.

Therefore, how to detect and locate the bridge bottom safely, accurately and efficiently is an urgent problem to be solved.

Disclosure of Invention

The invention aims to provide an improved unmanned aerial vehicle bridge bottom detection method based on LIDAR, which realizes intelligent and convenient bridge bottom rapid inspection.

In order to achieve the purpose, the invention provides the following scheme:

a LiDAR-based unmanned aerial vehicle underbridge detection system, comprising:

unmanned aerial vehicle: the system is used for completing flight function and providing a carrier for other modules;

LiDAR acquisition and navigation module: the system comprises a GPS unit and an inertia measurement unit, wherein the GPS unit and the inertia measurement unit are used for positioning the unmanned aerial vehicle and acquiring obstacle information and bridge three-dimensional information;

an image acquisition module: the system comprises a wide-angle camera and a memory, and is used for shooting and storing the condition of a detection area;

a transmission module: the system is used for transmitting the obstacle information, the dynamic bridge variation and the image information acquired by the image acquisition module;

a ground control center: the system is used for processing the received information, making a flight route, detecting an abnormal area and evaluating the abnormal area;

the unmanned aerial vehicle carries the LiDAR acquisition and navigation module and the image acquisition module to acquire field information, and transmits the acquired information to the ground control center through the transmission module.

Preferably, the inertial measurement unit is used for scanning three-dimensional point cloud information of an area including a bridge, a bridge bottom surface and a support and forming three-dimensional point cloud data.

Preferably, the LiDAR acquisition and navigation module further comprises a data processing unit, and the data processing unit is used for detecting the three-dimensional point cloud data scanned by the inertial measurement unit.

Preferably, the three-dimensional point cloud data is detected by a noise reduction method, and is used for positioning the length, width, depth, height and position information of the bridge to obtain the obstacle information and the dynamic variation of the bridge.

Preferably, the ground control center comprises a processing unit, a detection positioning unit and an abnormality evaluation unit;

the processing unit is used for correcting the received image and formulating a flight route of the unmanned aerial vehicle according to the received obstacle information;

the detection positioning unit is used for positioning the detected abnormal area;

the abnormity evaluation unit is used for evaluating the abnormity condition of the abnormity area.

Preferably, the processing unit corrects the received image, removes motion blur and enhances the image, and performs splicing operation according to the acquired image between two piers to form a large-scale high-definition image.

Preferably, the detection positioning unit is used for detecting the problems of pier stud defects, column body inclination, bridge bottom surface cracks and uneven telescopic surface in the large-scale high-definition image and positioning the pier stud defects, the column body inclination, the bridge bottom surface cracks and the telescopic surface.

Preferably, the abnormality evaluation unit performs crack measurement and density analysis according to the problems of the bridge, and finally obtains the fault type of the bridge.

The invention has the beneficial effects that:

(1) the bridge bottom detection system takes the unmanned aerial vehicle as a platform, has low cost, high efficiency, high reliability and high flexibility, is not limited in detection time, and improves the flight safety of the unmanned aerial vehicle by detecting the obstacle avoidance information;

(2) the system of the invention adopts laser to scan the three-dimensional information of the bridge to obtain the three-dimensional point cloud data, and can realize the multi-dimensional detection of the bridge and accurately position the specific position of the defect of the bridge by processing the three-dimensional point cloud data;

(3) the system can dynamically detect the bridge variation by comparing the collected data of the previous time and the next time, and effectively guide workers to carry out maintenance operation.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.

FIG. 1 is a block diagram of a system according to the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.

A LiDAR-based unmanned aerial vehicle underbridge detection system, as shown in figure 1, comprising:

unmanned aerial vehicle: the system is used for completing flight function and providing a carrier for other modules;

LiDAR acquisition and navigation module: the system comprises a GPS unit and an inertia measurement unit, wherein the GPS unit and the inertia measurement unit are used for positioning the unmanned aerial vehicle and acquiring obstacle information and bridge three-dimensional information; the LiDAR acquisition and navigation module is a solid state LiDAR device.

An image acquisition module: the system comprises a wide-angle camera and a memory, and is used for shooting and storing the condition of a detection area;

a transmission module: the system is used for transmitting the obstacle information, the dynamic bridge variation and the image information acquired by the image acquisition module;

a ground control center: the system is used for processing the received information, making a flight route, detecting an abnormal area and evaluating the abnormal area;

unmanned aerial vehicle carries out the collection of on-the-spot information through carrying on solid-state laser radar equipment and wide angle camera, and passes through transmission module will gather information transmission extremely ground control center.

The method comprises the steps of firstly installing a special fixing support for the wide-angle camera on the top of the unmanned aerial vehicle through a bolt, and then fixing the solid-state laser radar equipment on the support. Whether the scanning range of the solid-state laser radar equipment is sheltered by the unmanned aerial vehicle body or not needs to be detected, if sheltering occurs, the inclination angle of the solid-state laser radar equipment should be properly adjusted, or the equipment is enabled to integrally move outwards along the fixed support.

According to the further optimization scheme, an inertia measurement unit in the solid-state laser radar is used for scanning three-dimensional point cloud information of an area including a bridge, a bridge bottom surface and a support and forming three-dimensional point cloud data.

In a further optimized scheme, the LiDAR acquisition and navigation module further comprises a data processing unit, and the data processing unit is used for detecting the three-dimensional point cloud data scanned by the inertial measurement unit. And detecting the three-dimensional point cloud data through a noise reduction method, and positioning the length, width, depth, height and position information of the bridge to obtain the obstacle information and the dynamic variation of the bridge.

Whether the point cloud data obtained by scanning of the laser radar equipment meets the detection requirements or not is checked by two methods. Firstly, the self-vibration condition of the laser radar in the static state of the unmanned vehicle needs to be checked, the self-vibration of the instrument during operation is avoided, and the condition is enough to interfere with subsequent experimental data. If the vibration does not meet the requirement, a buffer material such as a rubber gasket can be added between the laser radar equipment and the fixed support to reduce the vibration influence. Secondly, the visual range and the point distance of the three-dimensional point cloud data need to be checked. The detection method is that the unmanned aerial vehicle moves forward a small distance to obtain a section of plane scanning data, so that the visual range of the laser radar equipment is judged. And the second mark is a mark of a handheld high-reflectivity material, the mark moves back and forth along line scanning, and the visual range of the point cloud data is judged by manually observing the position of the high-reflectivity point. Finally, the angle of a laser scanner in the laser radar needs to be adjusted, the scanning line is kept perpendicular to the central axis of the unmanned aerial vehicle body as much as possible, and the rolling angle of the scanning line is reset to zero as much as possible.

The total amount of point cloud data obtained by the method is too large, and the number of point cloud data of about 10m is more than 50 ten thousand points generally, so that the point cloud data needs to be processed by an efficient noise reduction method. The method adopts a statistical filter for each frame of point cloud data, and in comparison, the data of a single frame of point cloud data generally does not exceed 4500 points, and all the points are in the same plane, so that calculation can be performed according to the sequence of positions, and the calculation difficulty caused by the disorder of the point cloud data is avoided. The principle of the noise reduction method adopted by the invention is as follows: searching each point for the adjacent points of the specified number of the adjacent points, calculating the average value of the distances from each point to the adjacent points, and calculating the average value and the standard deviation of the distances of the average values. Because the distance between two points in the point cloud data generally conforms to Gaussian distribution, if the average distance between a certain point and an adjacent point is greater than the maximum distance, the point is considered as noise, and the noise is eliminated from the data. The maximum distance is calculated as:

L＝M+k*σ

wherein L is the allowable maximum distance between two points, M is the mean value of the distance average value of each point, k is the standard deviation amplification coefficient, and sigma represents the standard deviation of the distance average value of each point. In order to retain the real data as much as possible, the value of k in this embodiment is 3.

When data processing is carried out through three-dimensional point cloud data, space measurement is carried out, the positioning comprises bridge length, width, depth, height and position information, and dynamic bridge variation is obtained by comparing laser three-dimensional scanning data acquired twice.

The transmission module transmits the barrier information, the dynamic bridge variation and the image information acquired by the image acquisition module; the transmission module transmits information to the ground control center through a wireless communication mode, such as 4G, 5G and wifi networks.

In a further optimization scheme, the ground control center comprises a processing unit, a detection positioning unit and an abnormality evaluation unit;

the processing unit is used for correcting the received image and formulating a flight route of the unmanned aerial vehicle according to the received obstacle information;

the detection positioning unit is used for positioning the detected abnormal area;

the abnormity evaluation unit is used for evaluating the abnormity condition of the abnormity area.

According to the further optimization scheme, the processing unit corrects the received image, removes motion blur and enhances the image, and performs splicing operation according to the collected image between the two piers to form a large-scale high-definition image.

According to the further optimization scheme, the detection positioning unit is used for detecting the problems of pier stud defects, column body inclination, bridge bottom surface cracks and uneven telescopic surface in the large-scale high-definition image and positioning the problems.

And further optimizing the scheme, wherein the abnormity evaluation unit performs crack measurement and density analysis according to the problems of the bridge, and finally obtains the fault type of the bridge.

Firstly, the ground control center plans the flight path of the unmanned aerial vehicle according to the received obstacle information, and the obstacle avoidance and path planning are carried out by acquiring the position and the current posture of the unmanned aerial vehicle and acquiring 360-degree obstacle point cloud data around the unmanned aerial vehicle by using the laser radar. For the obstacle-free area between two piers, the drone will cruise according to the planned path. If a complex obstacle is encountered, the laser radar is used for calculating the mileage to position the unmanned aerial vehicle, and under the condition of accurate positioning of the unmanned aerial vehicle, obstacle avoidance is realized and the optimal flight path is planned.

Secondly, in order to obtain the bridge bottom surface image of high resolution, the shooting distance should be maintained in the shooting process of recycling the wide-angle camera, so when the unmanned aerial vehicle flies, the unmanned aerial vehicle flies by adopting a horizontal plane with constant height, the phenomenon that the shot image is not clear or the unmanned aerial vehicle is unstable due to wind power change when the unmanned aerial vehicle is close to the bridge bottom surface is avoided, and the shooting distance of about 3 meters is adopted for shooting.

When the ground control center receives the shot image, preprocessing operation is carried out, mainly the image is corrected, enhanced and subjected to motion blur removal and uneven illumination elimination, so that a high-definition distortion-free bridge bottom image is obtained. Because the visual range of each image shot by the unmanned aerial vehicle is limited, a bridge bottom image with a large visual field needs to be acquired in an image splicing mode so as to detect faults.

And acquiring all image data between the two piers by using the relation between the point cloud data and the image data during shooting. The step mainly depends on the synchronous scanning relation between image shooting and laser point clouds, and a series of laser point clouds can be correspondingly arranged when one image is shot. The distance between the shot image and the bridge pier can be known through the point cloud. According to the set safe distance between the unmanned aerial vehicle and the bridge pier, all images between the unmanned aerial vehicle and the safe distance twice are all images between the two bridge piers, and a plurality of image sets are obtained.

After obtaining the complete bridge bottom image, the detection positioning unit is used for comprehensively detecting the bridge bottom image, and the problems comprise pier stud defects, column body inclination, bridge bottom surface cracks, uneven telescopic surface and the like. The scanned bridge can be reconstructed in three dimensions through the acquired point cloud data, and the detection positioning unit measures according to the reconstructed model and detects the defects on the bridge and the positions corresponding to the defects.

And finally, determining the type of the bridge fault through an abnormality evaluation unit according to the defect problems and the positioning information, classifying the potential safety hazards, and informing managers of timely repairing.

The invention has the following beneficial effects:

(1) the bridge bottom detection system takes the unmanned aerial vehicle as a platform, has low cost, high efficiency, high reliability and high flexibility, is not limited in detection time, and improves the flight safety of the unmanned aerial vehicle by detecting the obstacle avoidance information;

(2) the system of the invention scans the three-dimensional information of the bridge by adopting the laser to obtain the three-dimensional point cloud data, can realize the multi-dimensional detection of the bridge by processing the three-dimensional point cloud data, and can accurately position the specific position of the defect of the bridge.

(3) The system can dynamically detect the bridge variation by comparing the collected data of the previous time and the next time, and effectively guide workers to carry out maintenance operation.

The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.