CN110319772A - Visual large-span distance measurement method based on unmanned aerial vehicle - Google Patents
Visual large-span distance measurement method based on unmanned aerial vehicle Download PDFInfo
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- G—PHYSICS
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- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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Abstract
The invention relates to a visual large-span distance measurement method based on an unmanned aerial vehicle, which can be used for large-scene distance measurement. Carrying a visual sensor by an aircraft, acquiring image data of an object scene to be measured and acquiring IMU information through flight control; transmitting the data to the ground station through graph transmission; tracking FAST characteristic points by an optical flow method and screening and extracting key frames; performing sensor data fusion through nonlinear optimization; carrying out three-dimensional modeling on a scene by utilizing a multi-stereoscopic vision technology; and selecting a measuring point in the three-dimensional model for ranging. The method reduces the number of redundant pictures through key frame and image ambiguity screening, reduces reconstruction time and improves reconstruction efficiency; compared with the three-dimensional reconstruction of pure pictures, the three-dimensional model with the real scale can be reconstructed, and the model precision is higher; compared with the GPS and image fusion, the three-dimensional reconstruction can be carried out in the environment without GPS signals (such as mountainous areas and indoors), and the robustness of the system is improved.
Description
Technical field
The present invention relates to a kind of surveying and mapping technology, in particular to a kind of vision large span distance measuring method based on unmanned plane.
Background technique
The Three Dimensional Reconfiguration of target scene is widely used in survey field.Three-dimensionalreconstruction can be by multi-angle of view
The two dimensional image of shooting is reconstructed into the threedimensional model of space object, carries out the measurement of dimension of object.It is illustrated with flower bed, people want
The diameter perimeter for measuring flower bed, can not directly measure since flower bed size is larger.It is taken photo by plane, can be reconstructed to flower bed by unmanned plane
The threedimensional model of flower bed out, reconnaissance can be obtained the full-size(d) of flower bed on model.There are two types of the modes of three-dimensional modeling at present.
1) three-dimensional modeling is carried out by image completely, but resulting threedimensional model lacks scale factor, needs conversion scale in a model
Ruler.2) by way of GPS and image co-registration, the threedimensional model of actual size is calculated.But this method, to GPS location precision
It is high.It can not be reconstructed under no GPS signal environment, such as mountain area, interior etc..
Summary of the invention
The present invention be directed to space object ranging there are the problem of, propose a kind of vision large span based on unmanned plane and survey
Away from method, the three-dimensionalreconstruction distance measuring method based on IMU (Inertial Measurement Unit) and image co-registration, the three of available true scale
Dimension module, while the robustness of reconstruct is increased, it also can be carried out three-dimensionalreconstruction under no GPS signal environment.
The technical solution of the present invention is as follows: a kind of vision large span distance measuring method based on unmanned plane, specifically includes following step
It is rapid:
1) using unmanned plane acquisition video data and IMU data, after being pre-processed to video data, by band after pretreatment
The video data and IMU data for having characteristic point and key frame are transferred to ground-based server；
2) key frame in mean parallax, tracking quality and image blur Picking video is utilized；
3) sensor fusion techniques are utilized, the rough pose of camera is obtained by the key frame of IMU pre-integration and selection, then
Nonlinear optimization is carried out by the characteristic point in video frame, obtains the accurate pose of camera, i.e. flight path；
4) finally carry out it is dense rebuild obtain physical size three-dimensionalreconstruction model, on the threedimensional model of foundation directly into
Row reconnaissance ranging.
Step 1) the concrete methods of realizing is as follows:
1.1) ginseng outside the interior participation of the camera and IMU of unmanned plane is demarcated using kalibr kit first, is led to
The parameter for crossing calibration carries out distortion correction to video captured by camera；
1.2) video data and IMU data, video data and IMU data packet by unmanned plane, after obtaining correction
Include the 3-axis acceleration and angular speed of camera, image information；
1.3) after obtaining video data and IMU data, extract first video frame FAST characteristic point and by optical flow method with
Track obtains the matched characteristic point of subsequent frame, how much carries out key frame screening according to characteristic point in image, then will have characteristic point
Video data and IMU data with key frame is by being wirelessly transmitted to ground-based server.
Step 2) the concrete methods of realizing is as follows:
2.1) by calculating present frame and a upper key frame using perspective-n-point algorithm to matched characteristic point
Spin matrix, translation matrix；If the spin matrix tracked between present frame and a upper key frame, translation matrix, which exceeds, to be set
Determine threshold value, then present frame is considered as to new key frame；
2.2) by by tracking characteristics quantity lower than setting quantity key frame be also considered as new key frame, with this avoid with
Track feature is lost completely, improves tracking quality；
2.3) it in non-reference picture, after carrying out Laplace transform to obtained all key frames, then counts
The average gradient calculated between adjacent pixel is poor, fuzziness is measured with average gradient difference, numerical value is lower, and frame is fuzzyyer, works as average gradient
When difference is less than setting Grads threshold, then the key frame is rejected.
Step 3) the concrete methods of realizing is as follows:
3.1) position orientation relation of the key frame under world coordinate system is calculated by IMU pre-integration, calculates the rough of camera
Pose；By alignment IMU data and key frame data in the rough pose acquisition process of camera, using between key frame images
It matches to constrain the pre- score error of IMU；
3.2) by nonlinear algorithm, optimize the pose of characteristic point and camera, obtain the accurate pose of camera.
Step 4) the concrete methods of realizing is as follows:
4.1) polar curve search and block-matching technique are used in key frame picture, to choosing 3* on the polar curve of each pixel
3 block of pixels and other key frame pictures mutually one by one compared with similarity, obtain position of the pixel in each picture；
4.2) by using depth filtering skill to the three-dimensional mapping point of the same pixel in different key frames in space
Art, which repeatedly carries out triangulation, makes estimation of Depth converge to a stationary value；
4.3) the point cloud under each key frame picture visual angle rotated by camera pose, move to world coordinate system
Under, splicing obtains the threedimensional model of physical size, and reconnaissance ranging can be directly carried out on the threedimensional model of foundation.
The beneficial effects of the present invention are: the present invention is based on the vision large span distance measuring methods of unmanned plane, pass through key frame
It is screened with image blur and reduces redundancy picture number, mitigate reconstitution time, improve reconstruct efficiency；With the three-dimensionalreconstruction of pure picture
It compares, the threedimensional model of true scale can be reconstructed and model accuracy is higher；It, can be in nothing with GPS compared with image co-registration
(such as mountain area, interior) carries out three-dimensionalreconstruction under GPS signal environment, improves the robustness of system.
Detailed description of the invention
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is method flow block diagram of the invention；
Fig. 3 is IMU of embodiment of the present invention pre-integration schematic diagram；
Fig. 4 is the camera pose figure after sensor optimization of the embodiment of the present invention；
Fig. 5 is epipolar-line constraint of embodiment of the present invention schematic diagram.
Specific embodiment
Vision large span distance measuring method flow chart based on unmanned plane as shown in Figure 1, 2, to the threedimensional model of given object
Carry out ranging, the size including target object, the distance between multiple objects, hardware needed for ranging are as follows: unmanned plane, earth station's clothes
Business device, monocular cam, wireless router etc..Concrete methods of realizing is as follows:
1, video data and IMU data are acquired using unmanned plane, after carrying out image preprocessing to video data, will pre-processed
Image data and IMU data are transferred to ground-based server afterwards；
Step 1 implements step are as follows:
1.1 first demarcate ginseng outside the interior participation of the camera and IMU of unmanned plane using kalibr kit, pass through
The parameter of calibration carries out distortion correction to video captured by camera；
1.2 by unmanned plane, and video data and IMU data, video data and IMU data after obtaining correction include
The 3-axis acceleration and angular speed of camera, image information；
After 1.3 obtain video datas and IMU data, extract first video frame FAST characteristic point and by optical flow method with
Track obtains the matched characteristic point of subsequent frame, how much carries out key frame screening according to characteristic point in image, then will have characteristic point
Video data and IMU data with key frame is by being wirelessly transmitted to ground-based server.
2, key frame in mean parallax, tracking quality and image blur Picking video is utilized；
Implement step are as follows:
2.1 by calculating present frame and a upper key using PNP (perspective-n-point) algorithm to matched characteristic point
The spin matrix of frame, translation matrix.If the spin matrix tracked between present frame and a upper key frame, translation matrix is super
Present frame is then considered as new key frame by given threshold out；
2.2 in order to prevent in track following characteristic point it is very few cause tracking fail.By the way that tracking characteristics quantity is lower than
The key frame of setting quantity is also considered as new key frame, avoids tracking characteristics from losing completely with this, improves tracking quality.
2.3 in non-reference picture, after first carrying out Laplace transform to obtained all key frames, then calculates phase
Average gradient between adjacent pixel is poor, and fuzziness is measured with average gradient difference, and numerical value is lower, and frame is fuzzyyer, when average gradient difference is small
When setting Grads threshold, then the key frame is rejected.
3, using sensor fusion techniques, the rough pose of camera is obtained by the key frame of IMU pre-integration and selection, then
Nonlinear optimization is carried out by the characteristic point in video frame, obtains the accurate pose of camera, i.e. flight path.
Implement step are as follows:
3.1 calculate position orientation relation of the key frame under world coordinate system by IMU pre-integration, calculate the rough position of camera
Appearance.I.e. under world coordinate system, IMU is respectively corresponded in the position at k moment, speed, rotation Such as Fig. 3 institute
Show, the position orientation relation between the key frame at i and j moment can be obtained by IMU pre-integration, they meet iterative.
In formula: subscript w represents world coordinate system；Subscript bkRepresent the k moment of IMU coordinate system；Subscript t represents t moment；Under
Mark bk+1Represent the k+1 moment of IMU coordinate system；For t moment, rotation of the IMU under world coordinate system,For t moment IMU's
Acceleration；For the biasing of t moment IMU acceleration；gwFor the gravity under world coordinate system；For the angle speed of t moment IMU
Degree；For the biasing of t moment IMU angular speed；
I.e. the state at current time can be acquired by last moment state come iteration.By one between adjacent two key frame
The IMU pre-integration of section time is got up, and the IMU relative motion between two frames is obtained.
By alignment IMU data and key frame data in the rough pose acquisition process of camera, using key frame images it
Between matching constrain the pre- score error of IMU.
3.2, by nonlinear algorithm, optimize the pose of characteristic point and camera, obtain the accurate pose of camera.Firstly, by position
Appearance, speed, the quantity of states such as Camera extrinsic are integrated into a state vector and carry out nonlinear optimization.State vector is as follows:
(4) are derived by (5) (6)
In formula: X is state vector；XkFor the state variable of IMU；bgFor the gravity biased of IMU；baIt is inclined for the acceleration of IMU
It sets；For the camera state variable opposite with IMU；Displacement between camera and IMU；Between camera and IMU
Rotation；λiIt is characterized a coordinate, i=1,2 ..., m；
Nonlinear optimization objective function is as follows:
In formula:For the displacement observation amount based on IMU；For the displacement observation amount based on image；rβFor the pass of IMU
It is function；rcFor the relation function of image；K is the moment, and β represents IMU, which characteristic point l represents, and j represents which frame, C generation
Table camera.
Camera pose optimum results are as shown in Figure 4.
4, finally carry out it is dense rebuild obtain physical size three-dimensionalreconstruction model, on the threedimensional model of foundation directly into
Row reconnaissance ranging.
Implement step are as follows:
4.1 use polar curve search and block-matching technique in key frame picture, to choosing 3*3 on the polar curve of each pixel
Block of pixels and other key frame pictures mutually one by one compared with similarity, obtain position of the pixel in each picture.Such as Fig. 5 institute
Show, d indicates the depth of three-dimensional point, o1、o2Indicate that the image center of the first picture and the second picture, R, T indicate 2 liang of frame figures
Spin matrix and translation matrix between piece, p1、p2Indicate the pixel on the first picture and the second picture, l2Indicate p1
Polar curve in the second picture.
4.2 by using depth filtering technology to the three-dimensional mapping point of the same pixel in different key frames in space
Repeatedly carrying out triangulation makes estimation of Depth converge to a stationary value.
Point cloud under each key frame picture visual angle is rotated by camera pose, moves to world coordinate system by 4.3
Under, splicing obtains the threedimensional model of physical size, and reconnaissance ranging can be directly carried out on the threedimensional model of foundation.
Experimental result has obtained threedimensional model by the vision large span distance measuring method based on unmanned plane, and can be repeatedly
The ranging of true scale is carried out on dimension module.Based on range-measurement system of the invention, mapping operations person can very easily be carried out
Mapping operations.
Claims (5)
1. a kind of vision large span distance measuring method based on unmanned plane, which is characterized in that specifically comprise the following steps:
1) using unmanned plane acquisition video data and IMU data, after pre-processing to video data, spy will be had after pretreatment
The video data and IMU data of sign point and key frame are transferred to ground-based server；
2) key frame in mean parallax, tracking quality and image blur Picking video is utilized；
3) sensor fusion techniques are utilized, the rough pose of camera are obtained by the key frame of IMU pre-integration and selection, then pass through
Characteristic point in video frame carries out nonlinear optimization, obtains the accurate pose of camera, i.e. flight path；
4) the dense three-dimensionalreconstruction model rebuild and obtain physical size is finally carried out, is directly selected on the threedimensional model of foundation
Point ranging.
2. the vision large span distance measuring method based on unmanned plane according to claim 1, which is characterized in that the step 1) tool
Body implementation method is as follows:
1.1) ginseng outside the interior participation of the camera and IMU of unmanned plane is demarcated using kalibr kit first, passes through mark
Fixed parameter carries out distortion correction to video captured by camera；
1.2) by unmanned plane, video data and IMU data after obtaining correction, video data and IMU data include phase
The 3-axis acceleration and angular speed of machine, image information；
1.3) it after obtaining video data and IMU data, extracts first video frame FAST characteristic point and is tracked by optical flow method, obtained
To the matched characteristic point of subsequent frame, how much key frame screening is carried out according to characteristic point in image, then will have characteristic point and pass
The video data and IMU data of key frame are by being wirelessly transmitted to ground-based server.
3. the vision large span distance measuring method based on unmanned plane according to claim 1, which is characterized in that the step 2) tool
Body implementation method is as follows:
2.1) pass through the rotation to matched characteristic point using perspective-n-point algorithm calculating present frame and a upper key frame
Matrix, translation matrix；If the spin matrix tracked between present frame and a upper key frame, translation matrix is beyond setting threshold
Present frame, then is considered as new key frame by value；
2.2) new key frame is also considered as by the key frame by tracking characteristics quantity lower than setting quantity, avoids tracking special with this
Sign is lost completely, improves tracking quality；
2.3) in non-reference picture, after carrying out Laplace transform to obtained all key frames, then phase is calculated
Average gradient between adjacent pixel is poor, and fuzziness is measured with average gradient difference, and numerical value is lower, and frame is fuzzyyer, when average gradient difference is small
When setting Grads threshold, then the key frame is rejected.
4. according to claim 1 to the vision large span distance measuring method described in any one of 3 based on unmanned plane, feature exists
In the step 3) concrete methods of realizing is as follows:
3.1) position orientation relation of the key frame under world coordinate system is calculated by IMU pre-integration, calculates the rough pose of camera；
By alignment IMU data and key frame data in the rough pose acquisition process of camera, the matching between key frame images is used
To constrain the pre- score error of IMU；
3.2) by nonlinear algorithm, optimize the pose of characteristic point and camera, obtain the accurate pose of camera.
5. according to claim 1 to the vision large span distance measuring method described in any one of 3 based on unmanned plane, feature exists
In the step 4) concrete methods of realizing is as follows:
4.1) polar curve search and block-matching technique are used in key frame picture, to selection 3*3 on the polar curve of each pixel
Block of pixels and other key frame pictures mutually one by one compared with similarity, obtain position of the pixel in each picture；
4.2) by more using depth filtering technology to the three-dimensional mapping point of the same pixel in different key frames in space
Secondary progress triangulation makes estimation of Depth converge to a stationary value；
4.3) the point cloud under each key frame picture visual angle rotated by camera pose, moved under world coordinate system,
Splicing obtains the threedimensional model of physical size, and reconnaissance ranging can be directly carried out on the threedimensional model of foundation.
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| CN112505065A (en) * | 2020-12-28 | 2021-03-16 | 上海工程技术大学 | Method for detecting surface defects of large part by indoor unmanned aerial vehicle |
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| CN113139949A (en) * | 2021-04-30 | 2021-07-20 | 逻腾（杭州）科技有限公司 | Robot image ambiguity detection method |
| CN115272494A (en) * | 2022-09-29 | 2022-11-01 | 腾讯科技（深圳）有限公司 | Calibration method and device for camera and inertial measurement unit and computer equipment |
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