CN109460046B - Unmanned aerial vehicle natural landmark identification and autonomous landing method - Google Patents

Unmanned aerial vehicle natural landmark identification and autonomous landing method Download PDF

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CN109460046B
CN109460046B CN201811213147.3A CN201811213147A CN109460046B CN 109460046 B CN109460046 B CN 109460046B CN 201811213147 A CN201811213147 A CN 201811213147A CN 109460046 B CN109460046 B CN 109460046B
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朱航
裴思宇
李宏泽
黄钰
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Jilin University
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
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    • G05D1/0653—Rate of change of altitude or depth specially adapted for aircraft during a phase of take-off or landing
    • G05D1/0676—Rate of change of altitude or depth specially adapted for aircraft during a phase of take-off or landing specially adapted for landing
    • G—PHYSICS
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    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
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    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
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Abstract

A method for recognizing natural landmark and autonomously landing an unmanned aerial vehicle belongs to the technical field of machine vision navigation, and comprises the steps of determining a landing area on a satellite digital map according to a given pre-landing coordinate, shooting an aerial image by the unmanned aerial vehicle at the pre-landing coordinate, carrying out filtering, graying, binarization processing, edge feature extraction and Hough transformation on the satellite digital map and the aerial image, extracting a continuous geometric curve, matching the satellite digital map and the aerial image by adopting a weighted Hausdorff distance matching algorithm, calculating the coordinate of the centroid of the area in the aerial image of the unmanned aerial vehicle relative to the unmanned aerial vehicle according to the Green's theorem, calculating the space coordinate of the centroid of the area according to a projection relation, and guiding the unmanned aerial vehicle to autonomously land at the space coordinate of the centroid of the area. The method can ensure that the unmanned aerial vehicle autonomously identifies the optimal landing point within the designated range, accurately lands, can make up for the defect of large autonomous landing error under GPS navigation, and improves the safety and reliability of autonomous landing.

Description

Unmanned aerial vehicle natural landmark identification and autonomous landing method
Technical Field
The invention belongs to the technical field of machine vision navigation, and particularly relates to a natural landmark identification and autonomous landing method for an unmanned aerial vehicle.
Background
In recent years, with the development of micro inertial navigation systems, flight control systems, micro electromechanical systems and novel materials, the research on micro unmanned aerial vehicles has greatly progressed. The rotary wing type micro unmanned aerial vehicle has the advantages of being good in flexibility, compact in structure, low in cost, fast in data acquisition and the like, and the application range also covers various fields including but not limited to pesticide spraying, geological surveying, searching and rescuing, cargo transportation, mapping and the like. Due to the limitation of the response speed and the working efficiency of the people for acquiring the information, the tasks are automatically completed by the unmanned aerial vehicle as much as possible, the actions of automatic take-off and landing, path planning, obstacle avoidance, ground imitation flying and the like are realized through a set program or the automatic planning of the unmanned aerial vehicle, and the accuracy and the reliability of the operation process are ensured.
In the aspect of unmanned aerial vehicle autonomous landing, an autonomous landing mode based on GPS navigation is mostly used at present, namely, a GPS sensor carried by the unmanned aerial vehicle records the geographic coordinate of a take-off time body, or a certain geographic coordinate is specified artificially, and when the unmanned aerial vehicle lands, a GPS positioning system guides the unmanned aerial vehicle to hover over the recorded geographic coordinate and descend for landing. The GPS navigation has the defects of large interference by non-air media, low positioning accuracy and the like, so that the unmanned aerial vehicle has large autonomous landing error in remote areas or areas with a large number of shelters and cannot accurately complete a landing task.
An unmanned aerial vehicle autonomous landing method based on machine vision is one of approaches for solving inaccurate positioning of a GPS (global positioning system), and currently, an autonomous landing method based on artificial landmarks is more applied to a rotor unmanned aerial vehicle. And along with the application of unmanned aerial vehicles in various fields is more and more extensive, also higher and more high to unmanned aerial vehicle's environmental suitability requirement. Some specific tasks require the drone to land in places where artificial landmarks are not suitable, and even require the drone to autonomously find a suitable landing place in a specific area, which requires the drone to have the ability to recognize natural landmarks. Therefore, in order to provide accurate navigation information to the drone and complete a specific autonomous landing task, a natural landmark identification and autonomous landing method for the drone is urgently needed.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle natural landmark identification and autonomous landing method based on machine vision and a satellite digital map, which aims to solve the problems in the prior art.
The natural landmark identification and autonomous landing method for the unmanned aerial vehicle comprises the following steps:
1.1 according to the given Pre-landing coordinates (X)0,Y0,Z0) Determining a landing area P with a convex polygon outline on a satellite digital map, firstly carrying out filtering, graying and binarization processing on an image of the area P, then further carrying out edge feature extraction, removing part of miscellaneous points, reserving main edge features based on the area, and finally extracting a continuous geometric curve through Hough transformation to obtain an outline curve I and a reference image A of the area P on the satellite digital map, wherein the binarization processing adopts a maximum inter-class variance method;
1.2, flying an unmanned aerial vehicle to the air above a given pre-landing coordinate, carrying out filtering, graying and binarization processing on an aerial image, further carrying out edge feature extraction, removing part of miscellaneous points, reserving main edge features based on a region, and finally extracting a continuous geometric curve through Hough transformation to obtain a profile curve II and a measured image B of a region P on a satellite digital map, wherein the binarization processing also adopts a maximum inter-class variance method;
1.3, matching the reference image A obtained in the step 1.1 with the actual measurement image B obtained in the step 1.2, and confirming a landing area P in the aerial image of the unmanned aerial vehicle; the image matching adopts a weighted Hausdorff distance matching algorithm, and comprises the following steps:
1.3.1 in the reference image A and the actual measurement image B, the 3-4DT algorithm is adopted to carry out the distance conversion of the characteristic point set in the two-dimensional space, and an image distance conversion matrix J is obtainedAAnd JB；
1.3.2 extracting branch points in a reference image A and a measured image B, and respectively storing the branch points in matrixes A and B;
1.3.3 according to JA、JBAnd A and B calculate the weighted Hausdorff distance:
H(A,B)＝max(hWHD(A,B),hWHD(B,A))
Figure GDA0001893972810000021
Figure GDA0001893972810000022
wherein: A. b is two point sets; n is a radical ofaIs the total number of feature points in point set a; a is a feature point belonging to A; d (a, B) is the distance from the characteristic point a on the point set A to the point set B; h isWHD(A, B) represents the directed distance from point set A to point set B; h isWHD(B, A) represents the directed distance from point set B to point set A;
the point with the minimum Hausdorff distance is the final matching point, so that the preliminary positioning information is obtained;
1.3.4, utilizing a least square algorithm to carry out one-to-one correspondence on all matching point pairs to obtain more accurate position information;
1.4 establishing a two-dimensional plane rectangular coordinate system by taking the unmanned aerial vehicle camera as the origin of coordinates, and calculating the coordinate (x) of the centroid of the area P in the aerial image of the unmanned aerial vehicle relative to the unmanned aerial vehicle according to the Green's theoremc,yc)；
1.5 calculating the coordinates (X) of the P centroid of the region from the projection relationshipc,Yc,Zc) The method specifically comprises the following steps:
1.5.1 calculating ground resolution GSD:
Figure GDA0001893972810000023
wherein: GSD represents ground resolution (m); f is the focal length (mm) of the lens; p is the pixel size (mm) of the imaging sensor; h is the corresponding flight height (m) of the unmanned aerial vehicle;
1.5.2 calculating the actual ground distance of the image diagonal, and obtaining the ground distance L between the image diagonal according to the width w and the height h of the image:
Figure GDA0001893972810000024
wherein: GSD represents ground resolution (m); w is the image width; h is the image height;
1.5.3 according to the longitude and latitude of the central point of the image, the distance and the direction angle of the area P centroid relative to the central point, the geographical coordinate of the area P centroid is obtained:
Figure GDA0001893972810000025
Figure GDA0001893972810000031
wherein: theta0∈(0,2π)；LonaLongitude of the image center point; lataThe latitude of the central point of the image; ri6378137m is taken as the equatorial radius; rjTaking 6356725m as the extreme radius;
1.5.4 converting geographic coordinates into spatial coordinates to obtain spatial coordinates (X) of P centroid of regionc,Yc,Zc)：
Figure GDA0001893972810000032
Wherein: n is the curvature radius; lon is longitude; lat is latitude; h is elevation;
1.6 unmanned aerial vehicle flies to space coordinate (X)c,Yc,Zc) And (5) landing in the vertical direction in the air.
The method can ensure that the unmanned aerial vehicle autonomously identifies the optimal landing point within the designated range, accurately lands, can make up for the defect of large autonomous landing error under GPS navigation, and improves the safety and reliability of autonomous landing.
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FIG. 1 is a flowchart of a method for identifying natural landmarks and autonomously landing for unmanned aerial vehicles
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below.
Step one, according to the given pre-landing coordinate (X)0,Y0,Z0) Determining a proper landing area P (requiring the outline of the area to be a convex polygon) on a satellite digital map, firstly carrying out filtering processing, graying processing and binarization processing on the image of the area P, further carrying out edge feature extraction, removing partial miscellaneous points, reserving main edge features based on the area, finally extracting a continuous geometric curve through Hough transformation to obtain an outline curve of the area P on the satellite digital map, and obtaining a reference image A, wherein the binarization processing selects a maximum inter-class variance method, supposing that T is a selected global threshold, pixels of all pixel points of the image are divided into a foreground and a background according to T as a boundary, and omega is a global threshold1And ω2Respectively representing the proportion of pixels belonging to the background and the foreground in the whole image, then:
Figure GDA0001893972810000033
Figure GDA0001893972810000034
wherein: p (i) represents the probability of the pixel with the pixel value i appearing in the image.
μ0And mu1Respectively representing the average value of the pixels of the background pixel and the foreground pixel, and if mu is the average pixel value of all the pixels, then:
Figure GDA0001893972810000035
Figure GDA0001893972810000036
Figure GDA0001893972810000037
the variance σ between classes corresponding to the threshold2(T) is defined as:
σ2(T)＝ω0(T)[μ0(T)-μ(T)]2+ω1(T)[μ1(T)-μ(T)]2＝ω0(T)ω1(T)[μ0(T)-μ1(T)]2
and traversing each gray value, and finding out the threshold T corresponding to the maximum inter-class variance, namely the threshold.
And secondly, flying the unmanned aerial vehicle to the vicinity of the upper space of the given pre-landing coordinate, carrying out filtering processing, graying processing and binarization processing on the aerial image, further carrying out edge feature extraction, removing part of miscellaneous points, reserving main edge features based on the area, and finally extracting a continuous geometric curve through Hough transformation to obtain a contour curve of the area P on the satellite digital map so as to obtain a real-measurement image B. The binarization processing also selects a maximum inter-class variance method.
And step three, matching the reference image A with the actual measurement image B, and confirming the landing area P in the aerial image of the unmanned aerial vehicle. The image matching adopts a weighted Hausdorff distance matching algorithm, and comprises the following specific steps:
(1) in the reference image A and the actual measurement image B, the distance conversion of the feature point set in the two-dimensional space is carried out by adopting a 3-4DT algorithm to obtain an image distance conversion matrix JAAnd JB；
(2) Extracting branch points in the reference image A and the measured image B, and respectively storing the branch points in the matrixes A and the matrix B;
(3) according to JA、JBAnd A and B calculate the weighted Hausdorff distance:
H(A,B)＝max(hWHD(A,B),hWHD(B,A))
Figure GDA0001893972810000041
Figure GDA0001893972810000042
wherein: A. b is two sets of points, NaIs the total number of feature points in the point set A, a is a feature point belonging to A, d (a, B) is the distance from the feature point a to the point set B on the point set A, hWHD(A,B)、hWHD(B, A) represent the directional distances from point set A to point set B and from point set B to point set A, respectively.
The point with the minimum Hausdorff distance is the final matching point to obtain preliminary positioning information.
(4) And performing one-to-one correspondence on all the matching point pairs by using a least square algorithm to acquire more accurate position information.
Establishing a two-dimensional plane rectangular coordinate system by taking the unmanned aerial vehicle camera as a coordinate origin, and calculating the coordinate (x) of the centroid of the region P in the aerial image of the unmanned aerial vehicle relative to the unmanned aerial vehiclec,yc)。
According to green's theorem, the closed contour along region P integrates:
Figure GDA0001893972810000043
Figure GDA0001893972810000044
after discretization, the above formula translates to:
Figure GDA0001893972810000051
Figure GDA0001893972810000052
step five, calculating the coordinates (X) of the P centroid of the region according to the projection relationc,Yc,Zc):
(1) Calculating the ground resolution:
Figure GDA0001893972810000053
wherein: GSD represents ground resolution (m), f is lens focal length (mm), P is imaging sensor's pixel size (mm), and H is the corresponding flight height (m) of unmanned aerial vehicle.
(2) Calculating the actual ground distance of the image diagonal, and obtaining the ground distance between the image diagonal according to the width w and the height h of the image:
Figure GDA0001893972810000054
(3) according to the longitude and latitude of the central point of the image, the distance and the direction angle of the area P centroid relative to the central point, the geographic coordinate of the area P centroid is obtained:
Figure GDA0001893972810000055
Figure GDA0001893972810000056
wherein: theta0∈(0,2π),Lona、LataIs the longitude and latitude of the center point of the image, RiTaking 6378137m, R as the equatorial radiusj6356725m was taken for the polar radius.
(4) Conversion between a geographical coordinate to an inter-spatial coordinate system
Figure GDA0001893972810000057
Wherein: n is curvature radius, Lon, Lat and H are longitude, latitude and elevation respectively, and space coordinates (X) of the centroid of the area P is obtainedc,Yc,Zc)。
Step six, the unmanned plane flies to a space coordinate (X)c,Yc,Zc) Go to hang in the upper airLanding in a straight direction.

Claims (1)

1. A natural landmark identification and autonomous landing method for an unmanned aerial vehicle is characterized by comprising the following steps:
1.1 Pre-landing X according to given0,Y0,Z0Determining a landing area P with a convex polygon outline on a satellite digital map, performing filtering, graying and binarization processing on an image of the area P, further performing edge feature extraction, removing part of miscellaneous points, reserving main edge features based on the area, and finally extracting a continuous geometric curve through Hough transformation to obtain an outline curve I and a reference image A of the area P on the satellite digital map, wherein the binarization processing adopts a maximum inter-class variance method;
1.2, flying an unmanned aerial vehicle to the air above a given pre-landing coordinate, carrying out filtering, graying and binarization processing on an aerial image, further carrying out edge feature extraction, removing part of miscellaneous points, reserving main edge features based on a region, and finally extracting a continuous geometric curve through Hough transformation to obtain a profile curve II and a measured image B of a region P on a satellite digital map, wherein the binarization processing also adopts a maximum inter-class variance method;
1.3, matching the reference image A obtained in the step 1.1 with the actual measurement image B obtained in the step 1.2, and confirming a landing area P in the aerial image of the unmanned aerial vehicle; the image matching adopts a weighted Hausdorff distance matching algorithm, and comprises the following steps:
1.3.1 in the reference image A and the actual measurement image B, the 3-4DT algorithm is adopted to carry out the distance conversion of the characteristic point set in the two-dimensional space, and an image distance conversion matrix J is obtainedAAnd JB；
1.3.2 extracting branch points in a reference image A and a measured image B, and respectively storing the branch points in matrixes A and B;
1.3.3 according to JA、JBAnd A and B calculate the weighted Hausdorff distance:
H(A,B)＝max(hWHD(A,B),hWHD(B,A))
Figure FDA0001831586700000011
Figure FDA0001831586700000012
wherein: A. b is two point sets; n is a radical ofaThe total number of the characteristic points in the point set A is; a is a feature point belonging to A; d (a, B) is the distance from the characteristic point a on the point set A to the point set B; h isWHD(A, B) represents the directed distance from point set A to point set B; h isWHD(B, A) represents the directed distance from point set B to point set A;
the point with the minimum Hausdorff distance is the final matching point, so that the preliminary positioning information is obtained;
1.3.4, utilizing a least square algorithm to carry out one-to-one correspondence on all matching point pairs to obtain more accurate position information;
1.4 use unmanned aerial vehicle camera as the origin of coordinates to establish two-dimensional plane rectangular coordinate system, calculate according to Green's theorem that the centroid of regional P is for unmanned aerial vehicle's x in the unmanned aerial vehicle image of taking photo by planec,ycCoordinates;
1.5 calculating X of P centroid of region according to projection relationc,Yc,ZcThe coordinates specifically include:
1.5.1 calculating ground resolution GSD:
Figure FDA0001831586700000013
wherein: GSD represents ground resolution (m); f is the focal length (mm) of the lens; p is the pixel size (mm) of the imaging sensor; h is the corresponding flight height (m) of the unmanned aerial vehicle;
1.5.2 calculating the actual ground distance of the image diagonal, and obtaining the ground distance L between the image diagonals according to the width w and the height h of the image:
Figure FDA0001831586700000021
wherein: GSD represents ground resolution (m); w is the image width; h is the image height;
1.5.3 according to the longitude and latitude of the central point of the image, the distance and the direction angle of the area P centroid relative to the central point, the geographical coordinate of the area P centroid is obtained:
Figure FDA0001831586700000022
Figure FDA0001831586700000023
wherein: theta0∈(0,2π)；LonaLongitude of the image center point; lataThe latitude of the central point of the image; ri6378137m is taken as the equatorial radius; rjTaking 6356725m as the extreme radius;
1.5.4 converting geographic coordinates into spatial coordinates to obtain spatial coordinates X of P centroid of regionc,Yc,Zc：
Figure FDA0001831586700000024
Wherein: n is the curvature radius; lon is longitude; lat is latitude; h is elevation;
1.6 unmanned aerial vehicle flies to space coordinate Xc,Yc,ZcAnd (5) landing in the vertical direction in the air.
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