CN115597498B - Unmanned aerial vehicle positioning and speed estimation method - Google Patents
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Abstract
The invention discloses an unmanned aerial vehicle positioning and speed estimating method, which comprises an unmanned aerial vehicle, wherein the unmanned aerial vehicle is provided with a camera and an intersection measuring system, the intersection measuring system comprises two area array CCDs which have the same performance and are symmetrically arranged on two sides of the camera, and the two area array CCDs use a main optical axis of the camera as a symmetry axis; the invention combines the optical flow and the camera automatic zooming technology, and can realize the autonomous positioning of the unmanned aerial vehicle and the measurement of the displacement in the horizontal direction and the vertical direction under the condition of no GPS; the method adopts the image edge characteristics obtained by wavelet transformation of the area array CCD image and the CCD intersection distance measurement principle, and utilizes the good multi-scale resolution capability of the wavelet transformation and the internal and external standard quantity characteristics of the edge image to better determine the target point in the area target.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle positioning, and particularly relates to an unmanned aerial vehicle positioning and speed estimation method.
Background
Usually, the unmanned aerial vehicle mainly depends on GPS inertial integrated navigation, and in environments such as jungles, urban buildings, indoor environments, etc., the positioning of the unmanned aerial vehicle is difficult or unstable due to interference or shielding of GPS signals.
Disclosure of Invention
The invention provides an unmanned aerial vehicle positioning and speed estimating method, and aims to solve the existing problems.
The invention is realized in this way, a method for positioning and estimating speed of an unmanned aerial vehicle, which comprises the unmanned aerial vehicle, wherein the unmanned aerial vehicle is provided with a camera and an intersection measurement system, the intersection measurement system comprises two area array CCDs which have the same performance and are symmetrically arranged on two sides of the camera, and the two area array CCDs take a main optical axis of the camera as a symmetry axis, and the method specifically comprises the following steps:
s1: randomly selecting a reference object for flying of the unmanned aerial vehicle, and acquiring two frames of images acquired by a camera of the unmanned aerial vehicle at different moments in the flying process of the unmanned aerial vehicle, wherein the two frames of images both comprise the reference object and have the same pixel gray;
s2: calculating the displacement of pixel points in the two frames of images by using an optical flow method to obtain an optical flow vector, and further calculating the horizontal displacement of the unmanned aerial vehicle;
s3: selecting the intersection point of the main optical axis of the camera and the ground as a target point;
s4: at the first moment, images on the area array CCDs on two sides are obtained, image edge features are extracted according to time-frequency localization features and multi-scale analysis of wavelet transformation, the position of a target point on the images is determined, and the actual height between the unmanned aerial vehicle and the target point is obtained through calculation;
s5: at the second moment, after the camera is automatically focused, images on the area array CCDs on two sides are obtained, according to time-frequency localization characteristics of wavelet transformation and multi-scale analysis, image edge characteristics are extracted, the position of a target point on the images is determined, and the actual height between the unmanned aerial vehicle and the target point is obtained through calculation;
s6: calculating the actual heights obtained twice to obtain the vertical displacement of the unmanned aerial vehicle;
s7: calculating the flying speed of the unmanned aerial vehicle according to the two relative position offsets of the unmanned aerial vehicle in unit time; according to the positioning data of the unmanned aerial vehicle at the previous moment, and the horizontal displacement and the vertical displacement of the unmanned aerial vehicle, the positioning data of the unmanned aerial vehicle at the current moment is calculated.
Further, in step S2, since the gray levels of the pixels of the two frames of images are the same, the following results are obtained:
wherein, I (x, y, t) represents that the pixel point moves to the position of the second frame image (x + dx, y + dy) after time dt, and both sides are expanded by taylor series to eliminate the same term, so as to obtain the following equation:
wherein:
，
the above is the optical flow equation, f x And f y Representing the gradient of the image, f t Represents a temporal gradient;
because all points in the reference object have the same motion, 9 points in the 3X3 neighborhood have the same motion to obtain 9 optical flow equations, and a least square method is adopted for fitting solution to obtain (u, v) as follows:
further, in step S4, one side of the CCD lens node O is used 1 As the origin of coordinates, connecting line O of CCD lens nodes at two sides 1 O 2 Establishing a coordinate system for the x axis, wherein the node distance of the two CCD lenses is d, the inclination angles of the two optical axes are alpha and beta, and the imaging distances of the target point A on the two CCD surfaces through the lenses are h respectively 1 、h 2 I.e. image height; the corresponding sign rule is: taking the y axis as a starting point, clockwise rotation is positive, and anticlockwise rotation is negative; image height h 1 、h 2 The left side is negative, the right side is positive, the focal lengths of the two CCD lenses are the same and are f, and the coordinates (x, y) of the target point A are as follows:
the target point is the intersection point h of the main optical axis of the camera and the ground 1 =h 2 = h, i.e. the target point y coordinate, i.e. the camera shooting distance:
further, in step S4, extracting image edge features according to the best time-frequency localization features and multi-scale analysis capability of the wavelet transform, specifically including:
let the real function of the two-dimensional signal output by CCD be f (x, z), and take the Gaussian function
As a smoothing function, its first derivative is:
in the dimension 2 j Under the conditions, the wavelet transform in the x-direction and the y-direction can be expressed as:
in the formula:
order to
Since the modulus maximum point of the wavelet transform corresponds to the edge of the image, the threshold value is uniformly taken as m 0 If is greater or greater>
Namely, the binary image is used as an edge point, and a binary image with an edge part of 1 and the rest of 0 is obtained; determining the central area and the edge characteristics of the central point image in the image, comparing the two sides of the CCD image to obtain the image height, and calculating the actual height between the unmanned aerial vehicle and the target point.
Further, in step S4, removing the false target in the binarized image includes:
removing the false target in the binary image by using the area as an inner scalar parameter and using a radius vector taking the centroid of a closed curve as a center as an outer scalar parameter, wherein the centroid coordinate (x) of the closed curve 1 ,z 1 ) Comprises the following steps:
where k is the number of sampling points of the closed curve, x i (i)、z i (i) Is the coordinate of the first sampling point;
the radius vector of the first sample point is:
compared with the prior art, the invention has the beneficial effects that: the invention discloses a method for positioning and estimating the speed of an unmanned aerial vehicle, which combines an optical flow technology and an automatic camera zooming technology, and can realize the autonomous positioning of the unmanned aerial vehicle and the measurement of the displacement in the horizontal direction and the vertical direction under the condition of no GPS; the method comprises the steps of adopting image edge characteristics obtained by wavelet transformation of an area array CCD image and a CCD intersection distance measurement principle, and utilizing good multi-scale resolution capability of the wavelet transformation and internal and external standard quantity characteristics of the edge image to better determine a target point in an area target; the camera shooting distance can be accurately determined by utilizing the characteristics of the internal scalar and the external scalar of the edge image after wavelet transformation, and the automatic focusing of the camera can be realized by means of a feedback circuit.
Drawings
FIG. 1 is a schematic diagram of the CCD convergence distance measurement of the present invention;
FIG. 2 is a diagram showing the relative mounting positions of the CCD and the camera according to the present invention;
FIG. 3 is a block diagram of the system design of the present invention;
fig. 4 is a two-dimensional code layout of the experimental example of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the description of the present invention, it is to be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate orientations or positional relationships based on those shown in the drawings, and are merely for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention. Further, in the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
Examples
Referring to fig. 1-3, in the optical flow theory, the gray scale of the pixel between two frames of images collected by the camera is not changed and the adjacent two frames of pixels have relative motion, then the following relationship holds:
wherein I (x, y, t) represents that the pixel point moves to the position of the second frame image (x + dx, y + dy) after time dt, and the following equation can be obtained by expanding both sides by taylor series and eliminating the same term:
wherein:
the above is the optical flow equation, where fx and fy represent the gradient of the image and ft represents the time gradient, but the above method cannot obtain (u, v) because one equation cannot solve two unknowns, and to solve this problem, the classical lucas-Kanade method can be used.
In the lucas-Kanade method, since all points in the neighborhood of the target point have similar motions, which is the core of the lucas-Kanade method, 9 optical flow equations are obtained by using 9 points in a 3X3 neighborhood to have the same motions, and then the least squares are adopted to perform fitting solution, and finally (u, v) are obtained as follows:
the method for calculating the moving speed of the pixel points by the optical flow method only needs to track some points in the image, the optical flow vector can be calculated by adopting the method, the attitude control of the unmanned aerial vehicle can be further optimized according to the obtained optical flow vector, and more accurate control is realized.
FIG. 1 is a schematic diagram of an area array CCD measurement installed on two sides of a camera, wherein the two CCD performance parameters are completely the same, and the optical axis of the camera is taken as a symmetry axis, and a CCD lens node O on one side is taken as a CCD lens node O 1 As the origin of coordinates, connecting line O of CCD lens nodes at two sides 1 O 2 Establishing a coordinate system for the x axis, wherein the node distance of the two CCD lenses is d, the inclination angles of the two optical axes are alpha and beta, and the imaging distances of the target point A on the two CCD surfaces through the lenses are h respectively 1 、h 2 I.e. image height;
the corresponding sign rule is: taking the y axis as a starting point, clockwise rotation is positive, and anticlockwise rotation is negative; image height h 1 、h 2 The left side is negative, the right side is positive, the focal lengths of the two CCD lenses are the same and are f, and the coordinates (x, y) of the target point A are as follows:
the conditions of the intersection system in optimizing the structural layout are as follows:
and to ensure distance measurementThe system is convenient to calculate when the distance is actually measured, and the intersection point of the main optical axis of the camera and the ground, namely h, is selected as a target point S 1 =h 2 = h, under this condition, the y coordinate of the target point, i.e., the camera photographing distance:
from the above analysis, the key to measuring camera height with a convergence system is how to determine the image of a point on the CCD plane located on the camera's principal optical axis in the target.
And extracting image edge characteristics according to the optimal time-frequency localization characteristics and multi-scale analysis capability of the wavelet transform, and judging the position of the target point on the basis of the image edge characteristics.
Let the real function of the two-dimensional signal output by CCD be f (x, z), and take the Gaussian function
Is a smoothing function having a first derivative of
Then in dimension 2 j Under the condition, the wavelet transform in the x-direction and the y-direction can be expressed as
In the formula:
order to
Theoretically, the modulus maximum point of wavelet transform corresponds to the edge of the image, and for simplifying operation, the threshold value is uniformly selected to be m 0 If is>
I.e. as edge points, which do not affect the inherent edge characteristics of the image.
In order to improve the calculation speed, edge detection is carried out from the x direction and the z direction to obtain a binary image with an edge part of 1 and the rest of 0.
To reduce the positioning error, the relative installation positions of the area array CCD and the camera are shown in fig. 2.
At the moment, the output of the CCD1 is only used as a reference image for determining the image point of the main optical axis, the position of the image point in the CCD2, namely the image height, is determined by comparing the image point with the image of the CCD2 according to the central area and the edge characteristic of the image point of the central point defined by the CCD1, and then the height of the target point is automatically calculated according to a coordinate formula.
Two-dimensional image shape description technologies can be divided into two categories, namely scalar and spatial domain, wherein the scalar technology refers to the fact that one scalar parameter and one group of scalar parameters are used for describing the shape of an object and is divided into two categories, namely an inner scalar technology and an outer scalar technology; spatial domain techniques are structural and relational properties that describe the shape of an object.
After the two-dimensional image is subjected to wavelet transformation and binarization, the area is used as a simple internal scalar parameter, most of non-related targets in the image can be removed, but pseudo targets with the same area and different targets also exist, and the radius vector taking the centroid of a closed curve as a center is used as an external scalar parameter, so that the pseudo targets can be well removed. Closed curve centroid coordinates
Is given by the formula
In the formula, k is the number of sampling points of the closed curve,
，
the first sample point coordinate.
Then the radius vector of the ith sample point is
According to the invention, the displacement of the unmanned aerial vehicle in the horizontal direction can be measured by utilizing optical flow positioning, so that the speed is estimated. When the height of the unmanned aerial vehicle changes, namely the speed exists in the vertical direction, targets are imaged on the two planar array CCDs through respective objective lenses, the two planar array CCDs are subjected to quick A/D conversion and then stored in a large-capacity memory, after a computer detects that two images are completely stored with signals, data in the memory are automatically read according to the sequence of the first CCD1 and the second CCD2, image edge binarization processing, target point determination and distance calculation are completed, results are provided for digital-to-analog conversion, a lens adjusting motor is driven to work, the adjusting process is fed back to the computer, when the lens reaches a proper position, the output of the computer is interrupted, and the motor stops working.
Test example 1
The method comprises the steps of testing on a p450 series unmanned aerial vehicle by using an optical flow positioning sensor, selecting one surface of a protective net as a reference object (the length is about 5 m) for testing optical flow positioning, pasting a two-dimensional code at intervals of 1m as an image for identifying and positioning by an optical flow technology, and setting the position of a first two-dimensional code to be 0m. The unmanned aerial vehicle flies at fixed points and passes through two-dimensional codes each time (the distance between the unmanned aerial vehicle and the protective net is kept unchanged in the test process), and test data are shown in table 1.
TABLE 1
Test example 2
The two planar array CCDs of the measurement system both adopt Tc215 type full-frame image sensors produced by TEXAS INSTRUMENTS, and the lens adopts a double-gauss photographic objective lens with the focal length of 18-200 mm. The test site was photographed on every other floor at 15m high floors (3 m high floor for each floor) from the ground with the test data shown in table 2.
TABLE 2
Test example 3
The two-area array CCD adopts a Tc215 full-frame image sensor produced by TEXAS INSTRUMENTS, the lens adopts a double-gauss photographic objective lens with the focal length of 18-200mm, and a p450 series unmanned plane test is adopted. The test site is selected as a 6m multiplied by 6m long and wide area protected by a protective net, the height of the test site is 3.5m, two-dimensional codes are selected as identification objects, the unmanned aerial vehicle sequentially passes through each two-dimensional code from different heights, and the position and the speed of the unmanned aerial vehicle are estimated. Record the change of focal length of the camera, the two-dimensional code array is placed on the ground as shown in fig. 4, the height is recorded as 0, and the test data is shown in table 3, wherein the coordinates are expressed as (x, y).
TABLE 3
According to the unmanned aerial vehicle positioning and speed estimating method disclosed by the invention, the unmanned aerial vehicle can be autonomously positioned and the displacement in the horizontal direction and the vertical direction can be measured by utilizing the optical flow positioning and area array CCD technology under the condition of not depending on positioning equipment such as a GPS and the like and under the condition of no GPS; under most circumstances, the data deviation is not higher than 5%, has realized that unmanned aerial vehicle's location is stable.
The present invention is not limited to the above preferred embodiments, and any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. The utility model provides an unmanned aerial vehicle location and speed estimation method, includes unmanned aerial vehicle, unmanned aerial vehicle is last to be installed the camera and to intersect the measurement system, the measurement system that intersects includes two the same performance and symmetry install the area array CCD of camera both sides, two area array CCD uses the camera principal optical axis as the symmetry axis, its characterized in that specifically includes following step:
s1: randomly selecting a reference object for flying of the unmanned aerial vehicle, and acquiring two frames of images acquired by a camera of the unmanned aerial vehicle at different moments in the flying process of the unmanned aerial vehicle, wherein the two frames of images both comprise the reference object and have the same pixel gray;
s2: calculating the displacement of pixel points in the two frames of images by using an optical flow method to obtain an optical flow vector, and further calculating the horizontal displacement of the unmanned aerial vehicle;
s3: selecting the intersection point of the main optical axis of the camera and the ground as a target point;
s4: at the first moment, images on the area array CCDs on two sides are obtained, image edge features are extracted according to time-frequency localization features and multi-scale analysis of wavelet transformation, the position of a target point on the images is determined, and the actual height between the unmanned aerial vehicle and the target point is obtained through calculation;
s5: at the second moment, after the camera is automatically focused, images on the area array CCDs on two sides are obtained, according to time-frequency localization characteristics of wavelet transformation and multi-scale analysis, image edge characteristics are extracted, the position of a target point on the images is determined, and the actual height between the unmanned aerial vehicle and the target point is obtained through calculation;
s6: calculating the actual heights obtained twice to obtain the vertical displacement of the unmanned aerial vehicle;
s7: calculating the flight speed of the unmanned aerial vehicle according to the two relative position offsets of the unmanned aerial vehicle in unit time; calculating the positioning data of the unmanned aerial vehicle at the current moment according to the positioning data of the unmanned aerial vehicle at the previous moment and the horizontal displacement and the vertical displacement of the unmanned aerial vehicle;
in step S4, one side of CCD lens node O 1 As the origin of coordinates, connecting line O of CCD lens nodes at two sides 1 O 2 Establishing a coordinate system for the x axis, wherein the node distance of the two CCD lenses is d, the inclination angles of the two optical axes are alpha and beta, and the imaging distances of the target point A on the two CCD surfaces through the lenses are h respectively 1 、h 2 I.e. image height; phase (C)The sign rules should be: taking the y axis as a starting point, clockwise rotation is positive, and anticlockwise rotation is negative; image height h 1 、h 2 The left side is negative, the right side is positive, the focal lengths of the two CCD lenses are the same and are f, and the coordinates (x, y) of the target point A are as follows:
the target point is the intersection point h of the main optical axis of the camera and the ground 1 ＝h 2 = h, i.e. the target point y coordinate, i.e. the camera shooting distance:
y＝-d(hsinα+fcosα)/[2hfcos2α+(h 2 -f 2 )sin2α]。
2. the method of claim 1, wherein in step S2, since the two frames of images have the same pixel gray level, the following results are obtained:
I(x，y，z)＝I(x+dx，y+dy，t+dt)
wherein, I (x, y, t) represents that the pixel point moves to the position of the second frame image (x + dx, y + dy) after time dt, and both sides are expanded by taylor series to eliminate the same term, so as to obtain the following equation:
f x u+f y v+f t ＝0
wherein:
above is the optical flow equation, f x And f y Representing the gradient of the image, f t Represents a temporal gradient;
because all points in the reference object have the same motion, 9 points in the 3X3 neighborhood have the same motion to obtain 9 optical flow equations, and a least square method is adopted for fitting solution to obtain (u, v) as follows:
3. the unmanned aerial vehicle positioning and speed estimating method according to claim 1, wherein in step S4, the image edge feature is extracted according to the best time-frequency localization feature and multi-scale analysis capability of wavelet transform, and specifically comprises:
let the real function of the two-dimensional signal output by CCD be f (x, z), take Gaussian function
For a smoothing function, its first derivative is:
in the dimension 2 j Under the conditions, the wavelet transform in the x-direction and the y-direction can be expressed as:
in the formula:
order to
Since the modulus maximum point of wavelet transform corresponds to the edge of image, uniformly taking the threshold value as m 0 If m is 2j f (x, z) > m0, namely the binary image is used as an edge point, and a binary image with an edge part of 1 and the rest of 0 is obtained; determining the central area and the edge characteristics of the central point image in the image, comparing the two sides of the CCD image to obtain the image height, and calculating the actual height between the unmanned aerial vehicle and the target point.
4. The unmanned aerial vehicle positioning and speed estimating method according to claim 3, wherein in step S4, removing the false target in the binarized image specifically comprises:
removing the false target in the binary image by using the area as an inner scalar parameter and using a radius vector taking the centroid of a closed curve as a center as an outer scalar parameter, wherein the centroid coordinate (x) of the closed curve 1 ,z 1 ) Comprises the following steps:
where k is the number of sampling points of the closed curve, x i (i)、z i (i) Is the coordinate of the first sampling point;
the radius vector of the first sample point is:
R i ＝[(x i (i)-x 1 ) 2 +(z i (i)-z 1 ) 2 ]1/2，i＝1，2，...，k。
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| CN111024066B (en) * | 2019-12-10 | 2023-08-01 | 中国航空无线电电子研究所 | Unmanned aerial vehicle vision-inertia fusion indoor positioning method |
| JP2024038527A (en) * | 2021-01-12 | 2024-03-21 | ソニーセミコンダクタソリューションズ株式会社 | Signal processing device, signal processing method, and signal processing system |
| CN216246319U (en) * | 2021-06-29 | 2022-04-08 | 广州南洋理工职业学院 | Unmanned aerial vehicle's light stream positioning system |
| CN114459467B (en) * | 2021-12-30 | 2024-05-03 | 北京理工大学 | VI-SLAM-based target positioning method in unknown rescue environment |
| CN114690802A (en) * | 2022-04-02 | 2022-07-01 | 深圳慧源创新科技有限公司 | Unmanned aerial vehicle binocular light stream obstacle avoidance method and device, unmanned aerial vehicle and storage medium |
| CN115371673A (en) * | 2022-07-14 | 2022-11-22 | 北京理工大学 | Binocular camera target positioning method based on Bundle Adjustment in unknown environment |
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