WO2018103407A1 - 一种基于彩色立体标定物的无人机标定方法及系统 - Google Patents

一种基于彩色立体标定物的无人机标定方法及系统 Download PDF

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Publication number
WO2018103407A1
WO2018103407A1 PCT/CN2017/102218 CN2017102218W WO2018103407A1 WO 2018103407 A1 WO2018103407 A1 WO 2018103407A1 CN 2017102218 W CN2017102218 W CN 2017102218W WO 2018103407 A1 WO2018103407 A1 WO 2018103407A1
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color
checkerboard
calibration
stereo
camera
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PCT/CN2017/102218
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English (en)
French (fr)
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李熙莹
陈思嘉
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中山大学
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Priority to US16/098,425 priority Critical patent/US10867406B2/en
Publication of WO2018103407A1 publication Critical patent/WO2018103407A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/80—Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64C—AEROPLANES; HELICOPTERS
    • B64C39/00—Aircraft not otherwise provided for
    • B64C39/02—Aircraft not otherwise provided for characterised by special use
    • B64C39/024—Aircraft not otherwise provided for characterised by special use of the remote controlled vehicle type, i.e. RPV
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64D—EQUIPMENT FOR FITTING IN OR TO AIRCRAFT; FLIGHT SUITS; PARACHUTES; ARRANGEMENT OR MOUNTING OF POWER PLANTS OR PROPULSION TRANSMISSIONS IN AIRCRAFT
    • B64D47/00—Equipment not otherwise provided for
    • B64D47/08—Arrangements of cameras
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64F—GROUND OR AIRCRAFT-CARRIER-DECK INSTALLATIONS SPECIALLY ADAPTED FOR USE IN CONNECTION WITH AIRCRAFT; DESIGNING, MANUFACTURING, ASSEMBLING, CLEANING, MAINTAINING OR REPAIRING AIRCRAFT, NOT OTHERWISE PROVIDED FOR; HANDLING, TRANSPORTING, TESTING OR INSPECTING AIRCRAFT COMPONENTS, NOT OTHERWISE PROVIDED FOR
    • B64F5/00—Designing, manufacturing, assembling, cleaning, maintaining or repairing aircraft, not otherwise provided for; Handling, transporting, testing or inspecting aircraft components, not otherwise provided for
    • B64F5/60—Testing or inspecting aircraft components or systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
    • G01C11/04—Interpretation of pictures
    • G01C11/06—Interpretation of pictures by comparison of two or more pictures of the same area
    • G01C11/08—Interpretation of pictures by comparison of two or more pictures of the same area the pictures not being supported in the same relative position as when they were taken
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
    • G01C11/04—Interpretation of pictures
    • G01C11/30—Interpretation of pictures by triangulation
    • G01C11/34—Aerial triangulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/50—Depth or shape recovery
    • G06T7/536—Depth or shape recovery from perspective effects, e.g. by using vanishing points
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/22—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
    • G06V10/225—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition based on a marking or identifier characterising the area
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/13—Satellite images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/17—Terrestrial scenes taken from planes or by drones
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U20/00—Constructional aspects of UAVs
    • B64U20/80—Arrangement of on-board electronics, e.g. avionics systems or wiring
    • B64U20/87—Mounting of imaging devices, e.g. mounting of gimbals
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2101/00—UAVs specially adapted for particular uses or applications
    • B64U2101/30—UAVs specially adapted for particular uses or applications for imaging, photography or videography
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2201/00—UAVs characterised by their flight controls
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2201/00—UAVs characterised by their flight controls
    • B64U2201/10—UAVs characterised by their flight controls autonomous, i.e. by navigating independently from ground or air stations, e.g. by using inertial navigation systems [INS]
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06T2207/30252—Vehicle exterior; Vicinity of vehicle
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P70/00—Climate change mitigation technologies in the production process for final industrial or consumer products
    • Y02P70/50—Manufacturing or production processes characterised by the final manufactured product

Definitions

  • the invention relates to the field of computer vision, in particular to a method and system for calibration of a drone based on a color stereo calibration.
  • image-based 3D reconstruction is generally the inverse process from 2D image restoration to 3D scene, ie through computer analysis.
  • a two-dimensional image of a target scene or object at different viewing angles restores the three-dimensional geometric information of the scene or object.
  • 3D reconstruction has a wide range of applications and plays a huge role in a variety of scenarios, such as the restoration of precious historical relics and ancient buildings, the three-dimensional reconstruction of medical human organs, the automatic driving of unmanned vehicles, etc., using ordinary digital cameras to reconstruct scenes.
  • objects have many advantages such as simplicity, economy, high efficiency, etc., and have broad application prospects, which have far-reaching significance for the development of human society.
  • Unmanned Aerial Vehicle has many advantages such as simple structure, small size, light weight, low cost, convenient operation and flexible maneuverability. It can replace manned aircraft, ground vehicles and high-altitude workers to perform various tasks and greatly expand. The work area reduces the risk of work and improves work efficiency. Therefore, in recent years, unmanned aerial vehicles such as remote fixed wings, multi-rotors, and helicopters are being used more and more in aerial mapping and aerial imaging to realize large-area topographic maps, engineering projects, buildings, etc. Modular process.
  • the three-dimensional reconstruction method of scenes and objects based on motion cameras such as drones generally uses a Euler geometry reconstruction linear algorithm framework to calculate the three-dimensional space points and camera positions, using scaling.
  • Orthographic projection and perspective projection creating an affine camera that combines point, line, and conic features to capture image sequences and estimate the scene structure.
  • the sparse point cloud of the real scene can be obtained from the N-view two-dimensional image and used to reconstruct the model surface by using the principle of motion recovery structure (SFM).
  • SFM principle of motion recovery structure
  • the two-dimensional image is rich in surface texture and illumination.
  • Useful information for 3D reconstruction such as Shading and Silhouette.
  • Using the contour of the object a preliminary 3D model visible shell (Visual Hull) can be obtained. The accuracy of the visible shell depends mainly on the contour accuracy, the number of views, and the shooting. Multiple factors such as angle.
  • the three-dimensional reconstruction method of scenes based on image sequence of UAV mainly relies on image features and multi-view geometric constraints. Compared with traditional visual methods, the computational complexity is large, the computing power of equipment is high, and the research starts late. Among them, the solution of image features and multi-view geometric constraints, including determining the spatial point and camera position in the captured sequence image, measuring or calibrating the geometric size and proportional constraints, has always been the key to determine the reconstruction accuracy.
  • 3D reconstruction technology generally needs to capture the image of the target object through multiple angles of the camera, if the camera is fixed and shooting The distance of the object, the range of space that can be reconstructed is very limited.
  • the relative position of the camera and the target object cannot be fixed, it is usually manually selected some local features such as the site, and the feature points such as edges and corner points are extracted as landmarks or reference objects, and then combined with artificial
  • the actual measured dimensions and other information provide scale and proportional constraints, to solve the parameters of the affine transformation and other formulas to improve the accuracy of 3D reconstruction. Since the marker points and reference objects are uncertain, it is necessary to measure at any time, the measurement accuracy is not easy to guarantee, and the actual operation is cumbersome, and there are many uncertain factors.
  • the position of the camera is constantly changing when shooting the image sequence, and the surround flight shooting mode is often used.
  • the spatial position of the camera itself is determined. Therefore, the industry has put forward new requirements for the UAV calibration technology: on the one hand, it is easy to accurately measure the calibration object, the space position standard and the placement operation are convenient, on the other hand, it is necessary to target the imaging characteristics of the UAV as much as possible from the space. The position is photographed to the calibration, so that the geometric constraints and spatial position of the sequence of captured images are conveniently obtained.
  • the object of the present invention is to provide a UAV calibration method based on a color stereo calibration object which is easy to accurately measure, has high detection precision, is convenient to install, has high versatility and is convenient to use.
  • Another object of the present invention is to provide a UAV calibration system based on color stereo calibration, which is easy to accurately measure, has high detection precision, is convenient to install, has high versatility and is convenient to use.
  • a method for calibration of a drone based on a color stereo calibration comprising the following steps:
  • a color checkerboard stereo calibrator into the scene to be photographed, the color checkerboard stereo calibrator being a closed stereoscopic structure, the color checkerboard stereo calibrator comprising at least one top surface and one side, the color checkerboard
  • Each surface of the grid-shaped calibration object adopts a checkerboard image of color and white phase, color and black phase, different color phase or black and white, and the color combination of the checkerboard images of any two adjacent surfaces are different;
  • the vanishing point theory is used to linearly solve the parameters of the UAV camera
  • the coordinate projection transformation method is used to determine the spatial position and image geometric constraint relationship of the UAV camera.
  • the color checkerboard stereo calibration includes but is not limited to a cube, a cuboid, a hexagonal prism, a hemisphere, and cylinder.
  • i 1,2,..., b ⁇ , b is the number of corner points of the color checkerboard stereo calibration, and b ⁇ e, e is the total number of faces of the color checkerboard stereo calibration, and Pi is the corresponding one of the corner points i of the color checkerboard stereo calibration.
  • maxPi is the maximum number of coplanar planes of the color checkerboard stereo calibration; if the color checkerboard stereo calibration is a regular cube without corners, the required number of colors is the same as the number of faces .
  • the top surface of the color checkerboard stereo calibration object adopts a red and white phase or a red and black checkerboard image
  • the side of the color checkerboard stereo calibration object adopts color other than red or white or black and white.
  • the bottom surface of the color checkerboard stereo calibration object is mounted with four adjustable footrests or a spherical table-shaped tray, and a bubble level is embedded in the top of the colored checkerboard stereoscopic calibration object.
  • the step of linearly solving the parameters of the UAV camera according to the image of the photographed color checkerboard stereo calibration using the vanishing point theory comprises:
  • the model of the UAV camera is a pinhole camera model
  • the spatial coordinates of any spatial point P are homogeneous coordinates M
  • the rotation matrix of the system, T is the translation vector of the world coordinate system to the camera coordinate system;
  • the inner parameter matrix of the drone camera is solved linearly by least square method and Zhang Zhengyou plane calibration method.
  • a UAV calibration system based on a color stereo calibration comprising:
  • a placement module for placing a color checkerboard stereo calibration into a scene to be photographed, the color checkerboard stereo calibration being a closed stereo structure, the colored checkerboard stereo calibration comprising at least one top surface and one side
  • Each color surface of the color checkerboard stereoscopic calibration uses a color combination of color and white, color and black, different color phase or black and white, and a color combination of the checkerboard images of any two adjacent surfaces. Are not the same;
  • a shooting module for taking an image of a color checkerboard stereo calibration from at least three different orientations using a drone
  • the internal parameter solving module is configured to linearly solve the parameters of the UAV camera according to the image of the photographed color checkerboard stereo calibration object by using vanishing point theory;
  • the coordinate projection transformation module is configured to determine the spatial position and image geometric constraint relationship of the UAV camera according to the coordinate projection transformation method of the parameters of the UAV camera.
  • i 1,2,..., b ⁇ , b is the number of corner points of the color checkerboard stereo calibration, and b ⁇ e, e is the total number of faces of the color checkerboard stereo calibration, and Pi is the corresponding one of the corner points i of the color checkerboard stereo calibration.
  • maxPi is the maximum number of coplanar planes of the color checkerboard stereo calibration; if the color checkerboard stereo calibration is a regular cube without corners, the required number of colors is the same as the number of faces .
  • the bottom surface of the color checkerboard stereo calibration object is mounted with four adjustable footrests or a spherical table-shaped tray, and a bubble level is embedded in the top of the colored checkerboard stereoscopic calibration object.
  • the internal parameter solving module includes:
  • the initialization unit is configured to determine a model of the UAV camera and a mapping relationship between the homogeneous coordinates of the spatial point and the homogeneous coordinates of the image point: if the model of the UAV camera is a pinhole camera model, the spatial point of any spatial point P
  • the rotation matrix of the coordinate system to the camera coordinate system, and T is the translation vector of the world coordinate system to the camera coordinate system;
  • the solving unit is configured to linearly solve the internal parameter matrix of the drone camera by using the least square method and the Zhang Zhengyou plane calibration method according to the image of the color checkerboard stereo calibration object and the constraint equation of the vanishing point theory.
  • the method of the present invention has the beneficial effects of: placing a color checkerboard stereo calibration object into the scene to be photographed, and taking an image of the color checkerboard stereo calibration object from at least three different orientations by the drone, according to the photographed color checkerboard.
  • the image of the stereo calibration object uses the vanishing point theory to linearly solve the parameters of the UAV camera and the coordinate projection transformation method based on the parameters of the UAV camera to determine the spatial position and image geometric constraint relationship of the UAV camera.
  • the color checkerboard stereo calibration is used for camera calibration, which can provide three-dimensional information and photograph the color checkerboard stereo calibration from any position or angle of the space, and pass the checkerboard image of any adjacent two surfaces in the color checkerboard stereo calibration.
  • the vanishing point theory obtains the parameters of the UAV camera to complete the calibration of the parameters in the camera, and then through the coordinate projection transformation method, the spatial position and image geometric constraint relationship of the UAV camera can be conveniently obtained, which is more convenient to use.
  • the system has the beneficial effects of including a placement module, a shooting module, an internal parameter solving module and a coordinate projection transformation module, and adopts a color checkerboard stereo calibration to perform camera calibration, and can provide three-dimensional information and any position or arbitrary space from the space.
  • FIG. 1 is an overall flow chart of a method for calibration of a drone based on a color stereo calibration object according to the present invention
  • FIG. 2 is a schematic diagram of two specific color matching schemes for the surface of a color checkerboard stereo calibration object
  • Figure 3 is a schematic view showing the structure of a cube calibration
  • Figure 4 is a schematic diagram of the fitting process of the vanishing point
  • Figure 5 is a calibration image taken by a drone when the color checkerboard stereo calibration is applied for actual 3D reconstruction.
  • a method for calibration of a drone based on a color stereo calibration includes the following steps:
  • a color checkerboard stereo calibrator into the scene to be photographed, the color checkerboard stereo calibrator being a closed stereoscopic structure, the color checkerboard stereo calibrator comprising at least one top surface and one side, the color checkerboard
  • Each surface of the grid-shaped calibration object adopts a checkerboard image of color and white phase, color and black phase, different color phase or black and white, and the color combination of the checkerboard images of any two adjacent surfaces are different;
  • the vanishing point theory is used to linearly solve the parameters of the UAV camera
  • the coordinate projection transformation method is used to determine the spatial position and image geometric constraint relationship of the UAV camera.
  • the color checkerboard stereo calibration object has two colors on each surface, that is, the checkerboard image of each surface is arranged by two different color checkerboards.
  • the color checkerboard stereo calibration includes but is not limited to a cube, a cuboid, Six prisms, hemispheres and cylinders.
  • the color combination manner of the checkerboard image on the surface of the color checkerboard stereo calibration object is that the surface of the color checkerboard stereo calibration object adopts a checkerboard image of color and white, and adjacent surfaces adopt different color phases.
  • i 1 , 2,...,b ⁇ ,m is the number of corner points of the color checkerboard stereo calibration, and b ⁇ e, b is the total number of faces of the color checkerboard stereo calibration, and Pi is any one of the color checkerboard stereo calibrations.
  • maxPi is the maximum number of coplanar planes of the color checkerboard stereo calibration; if the color checkerboard stereo calibration is a regular cube without corner points, the required color The number is the same as the number of its surface.
  • the top surface of the color checkerboard stereo calibration object adopts a red and white phase or a red and black checkerboard image
  • the side of the color checkerboard stereo calibration object adopts color and white other than red. Inter-parent or black and white checkerboard images.
  • the bottom surface of the color checkerboard stereo calibration object is provided with four adjustable footrests or a ball-shaped tray, and the top of the color checkerboard stereo calibration is embedded with a bubble level.
  • the step of linearly solving the parameters of the UAV camera according to the image of the photographed color checkerboard stereo calibration using the vanishing point theory includes:
  • the model of the UAV camera is a pinhole camera model
  • the spatial coordinates of any spatial point P are homogeneous coordinates M
  • the rotation matrix of the system, T is the translation vector of the world coordinate system to the camera coordinate system;
  • the inner parameter matrix of the drone camera is solved linearly by least square method and Zhang Zhengyou plane calibration method.
  • a UAV calibration system based on a color stereo calibration includes:
  • a placement module for placing a color checkerboard stereo calibration into a scene to be photographed, the color checkerboard stereo calibration being a closed stereo structure, the colored checkerboard stereo calibration comprising at least one top surface and one side
  • Each color surface of the color checkerboard stereoscopic calibration uses a color combination of color and white, color and black, different color phase or black and white, and a color combination of the checkerboard images of any two adjacent surfaces. Are not the same;
  • a shooting module for taking an image of a color checkerboard stereo calibration from at least three different orientations using a drone
  • the internal parameter solving module is configured to linearly solve the image according to the photographed color checkerboard stereo calibration object by using vanishing point theory Out the parameters of the drone camera;
  • the coordinate projection transformation module is configured to determine the spatial position and image geometric constraint relationship of the UAV camera according to the coordinate projection transformation method of the parameters of the UAV camera.
  • the color combination manner of the checkerboard image on the surface of the color checkerboard stereo calibration object is that the surface of the color checkerboard stereo calibration object adopts a checkerboard image of color and white, and adjacent surfaces adopt different color phases.
  • i 1 , 2,...,b ⁇ ,b is the number of corner points of the color checkerboard stereo calibration, and b ⁇ e, e is the total number of faces of the color checkerboard stereo calibration, and Pi is any one of the color checkerboard stereo calibrations.
  • maxPi is the maximum number of coplanar planes of the color checkerboard stereo calibration; if the color checkerboard stereo calibration is a regular cube without corner points, the required color The number is the same as the number of its surface.
  • the bottom surface of the color checkerboard stereo calibration object is provided with four adjustable footrests or a ball-shaped tray, and the top of the color checkerboard stereo calibration is embedded with a bubble level.
  • the internal parameter solving module includes:
  • the initialization unit is configured to determine a model of the UAV camera and a mapping relationship between the homogeneous coordinates of the spatial point and the homogeneous coordinates of the image point: if the model of the UAV camera is a pinhole camera model, the spatial point of any spatial point P
  • the rotation matrix of the coordinate system to the camera coordinate system, and T is the translation vector of the world coordinate system to the camera coordinate system;
  • the solving unit is configured to linearly solve the internal parameter matrix of the drone camera by using the least square method and the Zhang Zhengyou plane calibration method according to the image of the color checkerboard stereo calibration object and the constraint equation of the vanishing point theory.
  • the calibration process of the UAV includes two aspects: one is to design a stereo calibration which is easy to measure accurately, standard and easy to operate, and the other is to give a method of camera parameter calibration and image geometric constraint. The contents of these two aspects are described in detail below.
  • the surface of the UAV calibration object must have a three-dimensional spatial structure relationship, and Conveniently distinguish between different space points.
  • the present invention designs a color checkerboard stereo calibration, which has the following characteristics:
  • the three-dimensional calibration object is a closed three-dimensional structure, and includes at least one top surface and one side surface, such as a regular polyhedron such as a cube, a rectangular parallelepiped, a hexagonal prism, and the like, a hemisphere, a cylinder, and the like.
  • the surface of the stereo calibration object is a checkerboard image of color and white, color and black, different color phase or black and white, and color the surface according to the following rules:
  • the adjacent surface of the stereo calibration object uses a checkerboard image of different color (and black) and white (and black), and the number of checkerboards is an integer. At the boundary between the two surfaces, try to keep the color (and black) and white (and black).
  • the total number of colors is determined by the number of planes of the polyhedral co-corner, as follows:
  • the required non-white color (ie, color and black, excluding white) number Q satisfies the condition: 3 ⁇ Q ⁇ maxPi.
  • the number of colors is the same as the number of surfaces.
  • the top surface of the general three-dimensional calibration object adopts a red, white (or black) phase checkerboard pattern, and the side uses a color pattern (and black) other than red and a white checkerboard pattern.
  • the complete rendering scheme is the top red and black checkerboard pattern, and the side greenish white and blue and white checkerboards are adjacent to each other; the simple rendering scheme is to construct red and white. , green white, blue and white checkerboard patterns as adjacent surfaces.
  • the stereo calibration has at least 4*4 squares on one surface, and the side length of each square is set. It is a fixed length (such as 10cm, 20cm, etc.).
  • the surface of the checkerboard is matt material to ensure that the surface of the calibration object when the drone is photographed at different angles will not be exposed due to reflection.
  • the invention adopts different color pattern rendering methods for different surfaces of the color checkerboard stereo calibration object, and effectively obtains the calibration value and the determined size ratio constraint of the spatial position of the shooting region in the image capturing of the three-dimensional reconstruction, and significantly enhances the reconstruction result. Accuracy and accuracy of 3D measurements.
  • the color checkerboard stereo calibration designed by the invention is small in size and convenient to use, and is especially suitable for scale calibration in outdoor aerial photography applications.
  • the classic algorithms include TSL calibration method proposed by Tsai, RAC calibration method proposed by Heikkila, and plane calibration method proposed by Zhang Zhengyou.
  • the invention provides a linear camera self-calibration method based on a color checkerboard stereo calibration, which only needs to capture the color checkerboard stereo calibration image placed in the scene to be photographed from three or more different orientations.
  • the vanishing point theory is used to linearly solve the parameters in the camera, and then the classical coordinate projection transformation method can be used to determine the spatial position of the camera and the geometric constraint relationship of the image.
  • the specific implementation process of this method is:
  • ⁇ is a given scale factor; It is the parameter matrix in the camera; R is the rotation matrix of the world coordinate system to the camera coordinate system; T is the translation vector of the world coordinate system to the camera coordinate system.
  • P 1 , P 2 and P 3 be the vanishing points of the straight lines AB, AA', and AD, respectively, and their coordinates are recorded as As shown in Fig. 4, the least square method is used to fit the intersection of the straight line AB and the straight line cluster parallel to AB to obtain the vanishing point. Similarly, the vanishing point P 2 corresponding to AA' and the vanishing point P 3 corresponding to AD can be obtained.
  • n is an integer greater than or equal to 3
  • 3n equations related to matrix C can be obtained, and then, f is obtained linearly by least squares method, and matrix C can be obtained.
  • the camera's internal parameter matrix K can be obtained linearly by using the classical Zhang Zhengyou plane calibration method, thus completing the calibration of the camera. This calibration process is simple and has good stability and accuracy, and is highly practical.
  • the coordinate projection transformation method is used to determine the spatial position of the camera and the geometric constraint relationship of the image.
  • the present invention After the calibration of the camera is completed, according to the imaging characteristics of the drone, the present invention also needs to determine the spatial position and image geometric constraint relationship of the camera. Taking the color checkerboard stereo calibration as the cube calibration, the present invention takes the top surface and other faces 1 to 3 diagonal points in the image of the cube calibration as the photos in the image sequence (ie, the image of the object to be photographed).
  • Constraint points, and the length of the square of each checkerboard of the color checkerboard stereo calibration is known (such as 10cm), so the classical perspective transformation method can be used to solve the world coordinates corresponding to each point in the image, thus the space
  • the distance in the measurement is measured, and the precision of the length, width, height direction is used in the reconstruction process by using the checkerboard size of the cube calibration.
  • the color checkerboard stereo calibrators can be used to easily distinguish different surfaces, thereby length limiting the size of any measurement direction. And after choosing two 3D points in space, you need to The two constraint points are corrected in at least three images, and the more images participating in the correction, the more accurate the constraint. At the same time, any known ground artificial measurements can be used as a size constraint in the scene to be photographed.
  • the invention utilizes a color checkerboard stereo calibration object, and can shoot a cube calibration object from any angle.
  • there is a high requirement for the placement or movement of the calibration object if the calibration object is placed above the shooting object, etc.
  • the color The checkerboard stereo calibration is more suitable for camera calibration when the drone is shooting in multiple angles in space, and the checkerboard of different colors is also beneficial for observation and recognition after scene reconstruction; in addition, the checkerboard in the color checkerboard stereo calibration
  • the standard side length also has a greater precision improvement effect on the spatial proportional constraint correction of the reconstructed model.
  • the invention provides a calibration object and a calibration parameter in the camera, which has the advantages of simple structure, high detection precision, convenient placement, strong versatility, low cost and convenient use, and can meet the requirements of the unmanned aerial vehicle shooting sequence image for three-dimensional reconstruction of the scene.
  • Method the method has the following advantages:
  • a new type of multi-color checkerboard for color checkerboard is used, which can be used for auxiliary calibration when the UAV surrounds multi-angle images for 3D reconstruction.
  • the cube calibration can be taken from any angle.
  • the checkerboard stereo calibration is more suitable for camera calibration when the drone is shooting in multiple angles in space, and the checkerboard of different colors is also beneficial for observation and recognition after scene reconstruction; in addition, the checkerboard in the color checkerboard stereo calibration
  • the standard side length also has a greater precision improvement effect on the spatial proportional constraint correction of the reconstructed model.

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Abstract

一种基于彩色立体标定物的无人机标定方法及系统，方法包括：将彩色棋盘格立体标定物放置到待拍摄场景内；采用无人机至少从3个不同的方位拍摄彩色棋盘格立体标定物的图像；根据拍摄的彩色棋盘格立体标定物的图像采用灭点理论线性求解出无人机摄像机内参数；根据无人机摄像机内参数采用坐标投影变换方法确定无人机摄像机的空间位置和图像几何约束关系。采用彩色棋盘格立体标定物来进行摄像机标定，易于准确测量、检测精度高、便于安放和通用性强；只需至少从3个不同的方位拍摄彩色棋盘格立体标定物的图像并结合灭点理论得到无人机摄像机内参数来完成摄像机内参数的标定，使用起来更方便，可广泛应用于计算机视觉领域。

Description

一种基于彩色立体标定物的无人机标定方法及系统 技术领域
本发明涉及计算机视觉领域，尤其是一种基于彩色立体标定物的无人机标定方法及系统。
背景技术
计算机视觉研究领域的最终目标是为了使计算机与人类一样具备感知三维环境的能力，在这个领域中，基于图像的三维重建过程一般是从二维图像还原到三维场景的逆过程，即通过计算机分析目标场景或物体在不同视角下的二维图像，还原出场景或物体的三维空间几何信息。三维重建在多种场景中有着广泛的应用并发挥着巨大的作用，比如珍贵历史文物和古建筑的还原展示、医学人体器官的三维重建、无人汽车自动驾驶等等，利用普通数码相机重建场景和物体具有简易、经济、高效等诸多优点，应用前景广泛，对人类社会的发展有着深远的意义。
无人机(Unmanned Aerial Vehicle)具有结构简单、尺寸小、重量轻、成本低、操作便捷、灵活机动等多种优势，能够替代有人航空器、地面车辆、高空作业人员执行多种任务，极大地扩展了作业区域，降低了作业风险，提高了作业效率。因此近年来，遥控固定翼、多旋翼、直升飞机等无人飞行器正在越来越多地被应用于航空测绘、航空影像中，以实现大面积的地形图、工程项目、建筑物等测绘建模过程。
作为计算机视觉领域的重要分支，无人机这类基于运动相机对场景和物体的三维重建方式一般通过一个基于欧拉几何结构重建线性算法框架，来进行三维空间点以及相机位置的计算，利用缩放正投影和透视投影，建立一个结合点、线、圆锥曲线特征的仿射相机，从而获取图像序列并估算出场景结构。而真实场景的稀疏点云可以利用运动中恢复结构SFM(Structure From Motion)原理从N-视角二维图像中获得并用来进行模型表面的重建，二维图像中富含表面纹理(Texture)、光照亮度(Shading)、物体轮廓(Silhouette)等三维重建需要的有用信息，利用物体轮廓可以得到初步的三维模型可视壳(Visual Hull)，可视壳的精度主要依赖于轮廓精度、视图数量、拍摄角度等多重因素。
无人机这类基于图像序列的场景三维重建方式主要依赖于图像特征和多视图几何约束的求解，相比传统的视觉方法算法计算量大、设备计算能力要求高、研究起步较晚。其中，图像特征和多视图几何约束的求解，包括确定拍摄序列图像中空间点和相机的位置，测量或标定出几何尺寸和比例约束，一直是决定重建精度的关键。
三维重建技术一般需要通过摄像机多角度拍摄目标物体的图像，如果固定摄像机与拍摄 物体的距离，则可重建的空间范围非常有限。对于室外场景，若无法固定摄像机与拍摄目标物体的相对位置，则通常会手工选择一些现场有特色的建筑物局部等，提取其边缘、角点等特征点作为标志点或者参照物，再结合人工实际测量的尺寸等信息，提供尺度和比例约束，以解算仿射变换等公式的参数，来提高三维重建精度。由于标志点、参照物不确定，需要随时测绘，测量精度不易保证，且实际操作繁琐，存在很多不确定因素，如果要达到高精度三维重建，有必要设置标定物。若简单采用现有地面近景摄影勘查方法中的平面型黑白棋盘格标定物，则难以提供三维信息；若采用两个棋盘格标定平面的组合，则无法满足空间任意位置观察、标定的需求；而如果将黑白双色棋盘格在立方体的多个表面同时使用时，对拍摄者和照片使用者而言都存在一定的视觉识别困难和较大误差。
另外，对于无人机来说，其在拍摄图像序列时摄像机的位置不断变化，且经常采用环绕飞行拍摄方式，除了标定摄像机(即相机)内参数外，更要确定出摄像机自身的空间位置。因此，业内对无人机标定技术提出了新的要求：一方面要求标定物易于准确测量、空间位置标准和放置操作便利，另一方面要针对无人机的成像特点，尽可能可以从空间任意位置拍摄到标定物，从而方便地得到拍摄图像序列的几何约束和空间位置。
发明内容
为解决上述技术问题，本发明的目的在于：提供一种易于准确测量、检测精度高、便于安放、通用性强和使用方便的，基于彩色立体标定物的无人机标定方法。
本发明的另一目的在于：提供一种易于准确测量、检测精度高、便于安放、通用性强和使用方便的，基于彩色立体标定物的无人机标定系统。
本发明所采取的技术方案是：
一种基于彩色立体标定物的无人机标定方法，包括以下步骤：
将彩色棋盘格立体标定物放置到待拍摄场景内，所述彩色棋盘格立体标定物为封闭式立体结构，所述彩色棋盘格立体标定物包含有至少一个顶面和一个侧面，所述彩色棋盘格立体标定物的每个表面采用彩色与白色相间、彩色与黑色相间、不同彩色相间或黑色与白色相间的棋盘格图像且任意相邻两个表面的棋盘格图像的颜色组合均不相同；
采用无人机至少从3个不同的方位拍摄彩色棋盘格立体标定物的图像；
根据拍摄的彩色棋盘格立体标定物的图像采用灭点理论线性求解出无人机摄像机内参数；
根据无人机摄像机内参数采用坐标投影变换方法确定无人机摄像机的空间位置和图像几何约束关系。
进一步，所述彩色棋盘格立体标定物包括但不限于正方体、长方体、六棱柱、半球体和 圆柱。
进一步，所述彩色棋盘格立体标定物表面的棋盘格图像的颜色组合方式为彩色棋盘格立体标定物的表面采用彩色与白色相间的棋盘格图像，相邻表面采用不同彩色相间的棋盘格图像；若彩色棋盘格立体标定物为规则多面体构成的立体标定物，则其需要的非白色颜色总数Q满足条件：3≤Q≤maxPi，其中，maxPi＝max{Pi|i＝1，2，…，b}，b为彩色棋盘格立体标定物的角点数，且b＜e，e为彩色棋盘格立体标定物的总面数，Pi为彩色棋盘格立体标定物中的任意一个角点i对应的包含该角点的面数，maxPi为彩色棋盘格立体标定物的最多共角点平面数；若彩色棋盘格立体标定物为没有角点的规则立方体，则其所需的颜色数与其表面数相同。
进一步，所述彩色棋盘格立体标定物的顶面采用红色与白色相间或红色与黑色相间的棋盘格图像，所述彩色棋盘格立体标定物的侧面采用红色以外的彩色与白色相间或黑色与白色相间的棋盘格图像。
进一步，所述彩色棋盘格立体标定物的底面安装有四个可调节衬脚或者球台形托盘，所述彩色棋盘格立体标定物的顶部嵌入有气泡水平仪。
进一步，所述根据拍摄的彩色棋盘格立体标定物的图像采用灭点理论线性求解出无人机摄像机内参数这一步骤，其包括：
确定无人机摄像机的模型以及空间点齐次坐标与图像点齐次坐标的映射关系：若无人机摄像机的模型为针孔相机模型，则任一空间点P的空间点齐次坐标M与图像点齐次坐标m的映射关系表达式为：λm＝K[RT]M，其中，λ为给定的比例因子，K为无人机摄像机的内参数矩阵，R为世界坐标系到摄像机坐标系的旋转矩阵，T为世界坐标系到摄像机坐标系的平移向量；
根据拍摄的彩色棋盘格立体标定物的图像和灭点理论的约束方程，采用最小二乘法和张正友平面标定法线性求解出无人机摄像机的内参数矩阵。
本发明所采取的另一技术方案是：
一种基于彩色立体标定物的无人机标定系统，包括：
放置模块，用于将彩色棋盘格立体标定物放置到待拍摄场景内，所述彩色棋盘格立体标定物为封闭式立体结构，所述彩色棋盘格立体标定物包含有至少一个顶面和一个侧面，所述彩色棋盘格立体标定物的每个表面采用彩色与白色相间、彩色与黑色相间、不同彩色相间或黑色与白色相间的棋盘格图像且任意相邻两个表面的棋盘格图像的颜色组合均不相同；
拍摄模块，用于采用无人机至少从3个不同的方位拍摄彩色棋盘格立体标定物的图像；
内参数求解模块，用于根据拍摄的彩色棋盘格立体标定物的图像采用灭点理论线性求解出无人机摄像机内参数；
坐标投影变换模块，用于根据无人机摄像机内参数采用坐标投影变换方法确定无人机摄像机的空间位置和图像几何约束关系。
进一步，所述彩色棋盘格立体标定物表面的棋盘格图像的颜色组合方式为彩色棋盘格立体标定物的表面采用彩色与白色相间的棋盘格图像，相邻表面采用不同彩色相间的棋盘格图像；若彩色棋盘格立体标定物为规则多面体构成的立体标定物，则其需要的非白色颜色总数Q满足条件：3≤Q≤maxPi，其中，maxPi＝max{Pi|i＝1，2，…，b}，b为彩色棋盘格立体标定物的角点数，且b＜e，e为彩色棋盘格立体标定物的总面数，Pi为彩色棋盘格立体标定物中的任意一个角点i对应的包含该角点的面数，maxPi为彩色棋盘格立体标定物的最多共角点平面数；若彩色棋盘格立体标定物为没有角点的规则立方体，则其所需的颜色数与其表面数相同。
进一步，所述彩色棋盘格立体标定物的底面安装有四个可调节衬脚或者球台形托盘，所述彩色棋盘格立体标定物的顶部嵌入有气泡水平仪。
进一步，所述内参数求解模块包括：
初始化单元，用于确定无人机摄像机的模型以及空间点齐次坐标与图像点齐次坐标的映射关系：若无人机摄像机的模型为针孔相机模型，则任一空间点P的空间点齐次坐标M与图像点齐次坐标m的映射关系表达式为：λm＝K[RT]M，其中，λ为给定的比例因子，K为无人机摄像机的内参数矩阵，R为世界坐标系到摄像机坐标系的旋转矩阵，T为世界坐标系到摄像机坐标系的平移向量；
求解单元，用于根据拍摄的彩色棋盘格立体标定物的图像和灭点理论的约束方程，采用最小二乘法和张正友平面标定法线性求解出无人机摄像机的内参数矩阵。
本发明的方法的有益效果是：包括将彩色棋盘格立体标定物放置到待拍摄场景内，采用无人机至少从3个不同的方位拍摄彩色棋盘格立体标定物的图像，根据拍摄的彩色棋盘格立体标定物的图像采用灭点理论线性求解出无人机摄像机内参数和根据无人机摄像机内参数采用坐标投影变换方法确定无人机摄像机的空间位置和图像几何约束关系的步骤，采用了彩色棋盘格立体标定物来进行摄像机标定，能提供三维信息和从空间任意位置或任意角度拍摄彩色棋盘格立体标定物，并通过在彩色棋盘格立体标定物任意相邻两个表面的棋盘格图像中设置不同的颜色组合来方便拍摄者和照片使用者进行视觉识别，易于准确测量、检测精度高、便于安放和通用性强；只需至少从3个不同的方位拍摄彩色棋盘格立体标定物的图像并结合 灭点理论得到无人机摄像机内参数来完成摄像机内参数的标定，并再通过坐标投影变换方法就能方便地得出无人机摄像机的空间位置和图像几何约束关系，使用起来更方便。
本发明的系统的有益效果是：包括放置模块、拍摄模块、内参数求解模块和坐标投影变换模块，采用了彩色棋盘格立体标定物来进行摄像机标定，能提供三维信息和从空间任意位置或任意角度拍摄彩色棋盘格立体标定物，并通过在彩色棋盘格立体标定物任意相邻两个表面的棋盘格图像中设置不同的颜色组合来方便拍摄者和照片使用者进行视觉识别，易于准确测量、检测精度高、便于安放和通用性强；只需至少从3个不同的方位拍摄彩色棋盘格立体标定物的图像并结合灭点理论得到无人机摄像机内参数来完成摄像机内参数的标定，并再通过坐标投影变换方法就能方便地得出无人机摄像机的空间位置和图像几何约束关系，使用起来更方便。
附图说明
图1为本发明一种基于彩色立体标定物的无人机标定方法的整体流程图；
图2为彩色棋盘格立体标定物表面的两种具体配色方案示意图；
图3为立方体标定物的模型结构示意图；
图4为灭点的拟合过程示意图；
图5为将彩色棋盘格立体标定物应用来进行实际三维重建时无人机所拍摄的一幅标定物图像。
具体实施方式
参照图1，一种基于彩色立体标定物的无人机标定方法，包括以下步骤：
将彩色棋盘格立体标定物放置到待拍摄场景内，所述彩色棋盘格立体标定物为封闭式立体结构，所述彩色棋盘格立体标定物包含有至少一个顶面和一个侧面，所述彩色棋盘格立体标定物的每个表面采用彩色与白色相间、彩色与黑色相间、不同彩色相间或黑色与白色相间的棋盘格图像且任意相邻两个表面的棋盘格图像的颜色组合均不相同；
采用无人机至少从3个不同的方位拍摄彩色棋盘格立体标定物的图像；
根据拍摄的彩色棋盘格立体标定物的图像采用灭点理论线性求解出无人机摄像机内参数；
根据无人机摄像机内参数采用坐标投影变换方法确定无人机摄像机的空间位置和图像几何约束关系。
其中，彩色棋盘格立体标定物每个表面包含有2种颜色，即每个表面的棋盘格图像由2种不同颜色的棋盘格相间排列而成。
进一步作为优选的实施方式，所述彩色棋盘格立体标定物包括但不限于正方体、长方体、 六棱柱、半球体和圆柱。
进一步作为优选的实施方式，所述彩色棋盘格立体标定物表面的棋盘格图像的颜色组合方式为彩色棋盘格立体标定物的表面采用彩色与白色相间的棋盘格图像，相邻表面采用不同彩色相间的棋盘格图像；若彩色棋盘格立体标定物为规则多面体构成的立体标定物，则其需要的非白色颜色总数Q满足条件：3≤Q≤maxPi，其中，maxPi＝max{Pi|i＝1，2，…，b}，m为彩色棋盘格立体标定物的角点数，且b＜e，b为彩色棋盘格立体标定物的总面数，Pi为彩色棋盘格立体标定物中的任意一个角点i对应的包含该角点的面数，maxPi为彩色棋盘格立体标定物的最多共角点平面数；若彩色棋盘格立体标定物为没有角点的规则立方体，则其所需的颜色数与其表面数相同。
进一步作为优选的实施方式，所述彩色棋盘格立体标定物的顶面采用红色与白色相间或红色与黑色相间的棋盘格图像，所述彩色棋盘格立体标定物的侧面采用红色以外的彩色与白色相间或黑色与白色相间的棋盘格图像。
进一步作为优选的实施方式，所述彩色棋盘格立体标定物的底面安装有四个可调节衬脚或者球台形托盘，所述彩色棋盘格立体标定物的顶部嵌入有气泡水平仪。
进一步作为优选的实施方式，所述根据拍摄的彩色棋盘格立体标定物的图像采用灭点理论线性求解出无人机摄像机内参数这一步骤，其包括：
确定无人机摄像机的模型以及空间点齐次坐标与图像点齐次坐标的映射关系：若无人机摄像机的模型为针孔相机模型，则任一空间点P的空间点齐次坐标M与图像点齐次坐标m的映射关系表达式为：λm＝K[RT]M，其中，λ为给定的比例因子，K为无人机摄像机的内参数矩阵，R为世界坐标系到摄像机坐标系的旋转矩阵，T为世界坐标系到摄像机坐标系的平移向量；
根据拍摄的彩色棋盘格立体标定物的图像和灭点理论的约束方程，采用最小二乘法和张正友平面标定法线性求解出无人机摄像机的内参数矩阵。
参照图1，一种基于彩色立体标定物的无人机标定系统，包括：
放置模块，用于将彩色棋盘格立体标定物放置到待拍摄场景内，所述彩色棋盘格立体标定物为封闭式立体结构，所述彩色棋盘格立体标定物包含有至少一个顶面和一个侧面，所述彩色棋盘格立体标定物的每个表面采用彩色与白色相间、彩色与黑色相间、不同彩色相间或黑色与白色相间的棋盘格图像且任意相邻两个表面的棋盘格图像的颜色组合均不相同；
拍摄模块，用于采用无人机至少从3个不同的方位拍摄彩色棋盘格立体标定物的图像；
内参数求解模块，用于根据拍摄的彩色棋盘格立体标定物的图像采用灭点理论线性求解 出无人机摄像机内参数；
坐标投影变换模块，用于根据无人机摄像机内参数采用坐标投影变换方法确定无人机摄像机的空间位置和图像几何约束关系。
进一步作为优选的实施方式，所述彩色棋盘格立体标定物表面的棋盘格图像的颜色组合方式为彩色棋盘格立体标定物的表面采用彩色与白色相间的棋盘格图像，相邻表面采用不同彩色相间的棋盘格图像；若彩色棋盘格立体标定物为规则多面体构成的立体标定物，则其需要的非白色颜色总数Q满足条件：3≤Q≤maxPi，其中，maxPi＝max{Pi|i＝1，2，…，b}，b为彩色棋盘格立体标定物的角点数，且b＜e，e为彩色棋盘格立体标定物的总面数，Pi为彩色棋盘格立体标定物中的任意一个角点i对应的包含该角点的面数，maxPi为彩色棋盘格立体标定物的最多共角点平面数；若彩色棋盘格立体标定物为没有角点的规则立方体，则其所需的颜色数与其表面数相同。
进一步作为优选的实施方式，所述彩色棋盘格立体标定物的底面安装有四个可调节衬脚或者球台形托盘，所述彩色棋盘格立体标定物的顶部嵌入有气泡水平仪。
进一步作为优选的实施方式，所述内参数求解模块包括：
初始化单元，用于确定无人机摄像机的模型以及空间点齐次坐标与图像点齐次坐标的映射关系：若无人机摄像机的模型为针孔相机模型，则任一空间点P的空间点齐次坐标M与图像点齐次坐标m的映射关系表达式为：λm＝K[RT]M，其中，λ为给定的比例因子，K为无人机摄像机的内参数矩阵，R为世界坐标系到摄像机坐标系的旋转矩阵，T为世界坐标系到摄像机坐标系的平移向量；
求解单元，用于根据拍摄的彩色棋盘格立体标定物的图像和灭点理论的约束方程，采用最小二乘法和张正友平面标定法线性求解出无人机摄像机的内参数矩阵。
下面结合说明书附图和具体实施例对本发明作进一步解释和说明。
实施例一
针对现有技术难以准确测量、检测精度低、不便于安放、通用性弱和使用不方便的问题，本发明提出了一种全新的无人机标定方法及系统。本发明对无人机的标定过程包括两个方面：一是设计易于准确测量、标准和易操作的立体标定物，二是给出摄像机参数标定和图像几何约束的方法。下面对这两个方面的内容进行详细说明。
(一)设计立体标定物
为了便于拍摄作业时操作者的观察与后期导出照片处理时的识别，以及三维建模后在建模场景中进行不同角度比例约束设置，无人机标定物表面必须存在三维空间结构关系，且能 方便地区分不同空间点。为此，本发明设计了彩色棋盘格立体标定物，该立体标定物有以下特点：
(1)立体标定物为封闭式立体结构，包含有至少一个顶面和一个侧面，如正方体、长方体、六棱柱等规则多面体，以及半球体、圆柱等。
(2)立体标定物的表面采用彩色与白色相间、彩色与黑色相间、不同彩色相间或黑色与白色相间的棋盘格图像，并根据以下规则对其表面进行颜色渲染：
1)立体标定物的相邻表面采用不同彩色(及黑色)与白色(及黑色)相间的棋盘格图像，棋盘格数目为整数。两表面边界处，尽量保持彩色(及黑色)与白色(及黑色)相间。
2)完整渲染方案：立体标定物的相邻表面采用不同彩色(及黑色)与白色相间的棋盘格图像，两表面边界处，保持彩色与白色(及黑色)相间，即任意两个相邻的表面图案展开为一个平面时，保持彩色与白色(及黑色)相间的棋盘格样式，不会出现类似白色块与白色块、彩色块与彩色块相邻的现象。如图2(a)所示，规则立方体顶面为红黑棋盘格图像，侧面是绿白、蓝白棋盘格图像。
3)简单渲染方案：立体标定物的表面采用彩色与白色相间的棋盘格图像，相邻表面采用不同彩色相间的棋盘格图像。
4)对于规则多面体构成的立体标定物，其彩色总数由多面体共角点的平面数决定，具体如下：
设规则立方体标定物的角点数为b，总面数为e，对任意一个角点i，包含该角点的面数为Pi，则该立方体标定物最多共角点平面数为maxPi＝max{Pi|i＝1，2，…，b}；
而需要的非白色颜色(即彩色及黑色，不包括白色)数Q满足条件：3≤Q≤maxPi。
对于没有角点的规则立方体标定物，其颜色数与表面数相同。
5)一般立体标定物的顶面采用红色、白色(或黑色)相间的棋盘格图案，侧面采用红色以外的彩色(及黑色)与白色相间的棋盘格图案。
6)当颜色数为3时，彩色首选红、绿、蓝，此时完整渲染方案为顶面红黑棋盘格图案，侧面绿白、蓝白棋盘格依次相邻；简单渲染方案即构建红白、绿白、蓝白棋盘格图样作为相邻表面。
(3)为了保证至少有2*2个边缘和节点清晰的方格(棋盘格的基本单元)，立体标定物的一个表面上至少有4*4个方格，每个方格的边长设置为固定长度(如10cm、20cm等)。
(4)棋盘格表面为哑光材质，以保证无人机以不同角度拍摄时标定物表面时不会因反光出现照片过曝的问题。
(5)为了保持立方体标定物放置时能保持水平，可以在立方体标定物的底座加装四个可调节衬脚或者球台形托盘，并在顶部嵌入气泡水平仪。
本发明通过对彩色棋盘立体标定物不同表面采用不同的颜色图案渲染方式，在三维重建的图像拍摄中，有效给出了拍摄区域空间位置的标定值和确定尺寸比例约束，显著增强了重建结果的精度和三维测量的准确性。本发明设计的彩色棋盘立体标定物体积小，使用方便，尤其适用于户外各种航拍应用中的尺度标定。
(二)摄像机参数标定和确定图像几何约束的方法。
目前摄像机标定方法的研究很多，其中经典的算法有Tsai提出的DLT标定法、Heikkila提出的RAC标定法、张正友提出的基于平面标定法等。本发明提出了一种基于彩色棋盘格立体标定物的线性摄像机自标定方法，仅需从三个或更多个不同的方位摄取放置在待拍摄场景内的彩色棋盘格立体标定物图像，就可利用灭点理论线性地解出摄像机内参数，再利用经典的坐标投影变换方法，即可确定摄像机空间位置和图像几何约束关系。该方法的具体实现过程为：
(1)摄像机内参数标定。
假设摄像机模型为经典针孔相机模型，设P为任意空间点，其空间点齐次坐标为M＝(xw,yw,zw,1)T，图像点齐次坐标为m＝(u,v,1)T。由透视投影几何关系，可得M与m之间的关系如下：
λm＝K[RT]M   (1)
其中，λ为给定的比例因子；
Figure PCTCN2017102218-appb-000001
为摄像机内参数矩阵；R为世界坐标系到摄像机坐标系的旋转矩阵；T为世界坐标系到摄像机坐标系的平移向量。
(2)采用彩色棋盘格立体标定物标定摄像机的内参数矩阵。
彩色棋盘格立体标定物的模型如图3所示：
设P1，P2，P3分别为直线AB，AA’，AD的灭点，其坐标分别记为
Figure PCTCN2017102218-appb-000002
Figure PCTCN2017102218-appb-000003
如图4所示，利用最小二乘法由直线AB和平行于AB的直线簇的交点拟合可得到灭点
Figure PCTCN2017102218-appb-000004
同理，可以得到AA’对应的灭点P2、AD对应的灭点P3。
根据灭点理论，有以下约束方程：
Figure PCTCN2017102218-appb-000005
令
Figure PCTCN2017102218-appb-000006
由(2)式可得：
Figure PCTCN2017102218-appb-000007
令
Figure PCTCN2017102218-appb-000008
Figure PCTCN2017102218-appb-000009
则式(3)可表示为：Af＝0。
从不同方位拍摄n(n为大于等于3的整数)幅彩色棋盘格立体标定物的图像，即可得到3n个有关矩阵C的方程，接着，利用最小二乘法线性解出f，可得矩阵C；最后利用经典的张正友平面标定法即可线性求出摄像机内参数矩阵K，从而完成摄像机的标定。此标定过程简单且有较好的稳定性和准确性，实用性强。
(3)采用坐标投影变换方法确定摄像机的空间位置和图像几何约束关系。
完成摄像机的标定后，根据无人机的成像特点，本发明还需要确定在确定摄像机的空间位置和图像几何约束关系。以彩色棋盘格立体标定物为立方体标定物为例，本发明在立方体标定物的图像中取到顶面和其他面1～3对角点作为图像序列中的照片(即待拍摄物体的图像)标注约束点，且已知彩色棋盘格立体标定物的每个棋盘格的方块边长(如10cm)，故即可采用经典的透视变换方法，解算图像中各点对应的世界坐标，从而对空间中的距离进行测量，并在重建过程中利用立方体标定物的棋盘格尺寸进行长宽高方向的精准约束。
如图5所示，在三维重建的场景中，可以利用彩色棋盘格立体标定物容易地区分不同表面，从而对任意测量方向的尺寸进行长度约束。而在选择空间中的两个三维点之后，需要在 至少三幅图像中对这两个约束点进行校正，参与校正的图像越多，则约束越准确。同时，任何已知的地面人工测量值都可作为待拍摄场景中的尺寸约束。
本发明利用彩色棋盘格立体标定物，可以从任意角度拍摄立方体标定物，其他方法中对标定物的摆放或运动有较高要求(如需要标定物放在拍摄物体的上方等)，而彩色棋盘格立体标定物则更加适用于无人机在空间中多角度拍摄时的相机标定，并且不同色彩的棋盘格也有利于在场景重建后进行观察识别；此外彩色棋盘格立体标定物中棋盘格的标准边长对于重建模型的空间比例约束校正也有较大的精度提升作用。
本发明提供了一种结构简单、检测精度高、便于安放、通用性强、成本较低和使用方便的，可满足无人飞行器拍摄序列图像进行场景三维重建的标定物和摄像机内参数标定要求的方法，该方法具有以下优点：
1)采用了新型的彩色棋盘格立体标定物，可以用于无人机环绕多角度拍摄图像进行三维重建时的辅助标定。
2)提出了针对彩色棋盘格立体标定物表面的新颜色渲染方案，可以有效区分不同表面，以便于在三维重建时进行准确的空间位置标定。
3)提出了一种基于彩色棋盘格立体标定物的线性摄像机内参数自标定方法，仅需从三个或更多个不同的方位摄取彩色棋盘格立体标定物的图像，即可利用灭点理论线性地解出摄像机内参数，更加快捷。
4)能通过坐标投影变换从彩色棋盘格立体标定物推算无人飞行器摄像机的空间位置并进行三维重建时几何约束，使用起来十分方便。
5)利用彩色棋盘格立体标定物，可以从任意角度拍摄立方体标定物，其他方法中对标定物的摆放或运动有较高要求(如需要标定物放在拍摄物体的上方等)，而彩色棋盘格立体标定物则更加适用于无人机在空间中多角度拍摄时的相机标定，并且不同色彩的棋盘格也有利于在场景重建后进行观察识别；此外彩色棋盘格立体标定物中棋盘格的标准边长对于重建模型的空间比例约束校正也有较大的精度提升作用。
以上是对本发明的较佳实施进行了具体说明，但本发明并不限于所述实施例，熟悉本领域的技术人员在不违背本发明精神的前提下还可做作出种种的等同变形或替换，这些等同的变形或替换均包含在本申请权利要求所限定的范围内。

Claims (10)

  1. 一种基于彩色立体标定物的无人机标定方法，其特征在于：包括以下步骤：
    将彩色棋盘格立体标定物放置到待拍摄场景内，所述彩色棋盘格立体标定物为封闭式立体结构，所述彩色棋盘格立体标定物包含有至少一个顶面和一个侧面，所述彩色棋盘格立体标定物的每个表面采用彩色与白色相间、彩色与黑色相间、不同彩色相间或黑色与白色相间的棋盘格图像且任意相邻两个表面的棋盘格图像的颜色组合均不相同；
    采用无人机至少从3个不同的方位拍摄彩色棋盘格立体标定物的图像；
    根据拍摄的彩色棋盘格立体标定物的图像采用灭点理论线性求解出无人机摄像机内参数；
    根据无人机摄像机内参数采用坐标投影变换方法确定无人机摄像机的空间位置和图像几何约束关系。
  2. 根据权利要求1所述的一种基于彩色立体标定物的无人机标定方法，其特征在于：所述彩色棋盘格立体标定物包括但不限于正方体、长方体、六棱柱、半球体和圆柱。
  3. 根据权利要求1所述的一种基于彩色立体标定物的无人机标定方法，其特征在于：所述彩色棋盘格立体标定物表面的棋盘格图像的颜色组合方式为彩色棋盘格立体标定物的表面采用彩色与白色相间的棋盘格图像，相邻表面采用不同彩色相间的棋盘格图像；若彩色棋盘格立体标定物为规则多面体构成的立体标定物，则其需要的非白色颜色总数Q满足条件：3≤Q≤maxPi，其中，maxPi＝max{Pi|i＝1，2，…，b}，b为彩色棋盘格立体标定物的角点数，且b＜e，e为彩色棋盘格立体标定物的总面数，Pi为彩色棋盘格立体标定物中的任意一个角点i对应的包含该角点的面数，maxPi为彩色棋盘格立体标定物的最多共角点平面数；若彩色棋盘格立体标定物为没有角点的规则立方体，则其所需的颜色数与其表面数相同。
  4. 根据权利要求1所述的一种基于彩色立体标定物的无人机标定方法，其特征在于：所述彩色棋盘格立体标定物的顶面采用红色与白色相间或红色与黑色相间的棋盘格图像，所述彩色棋盘格立体标定物的侧面采用红色以外的彩色与白色相间或黑色与白色相间的棋盘格图像。
  5. 根据权利要求1所述的一种基于彩色立体标定物的无人机标定方法，其特征在于：所述彩色棋盘格立体标定物的底面安装有四个可调节衬脚或者球台形托盘，所述彩色棋盘格立体标定物的顶部嵌入有气泡水平仪。
  6. 根据权利要求1-5任一项所述的一种基于彩色立体标定物的无人机标定方法，其特征在于：所述根据拍摄的彩色棋盘格立体标定物的图像采用灭点理论线性求解出无人机摄像机内参数这一步骤，其包括：
    确定无人机摄像机的模型以及空间点齐次坐标与图像点齐次坐标的映射关系：若无人机 摄像机的模型为针孔相机模型，则任一空间点P的空间点齐次坐标M与图像点齐次坐标m的映射关系表达式为：λm＝K[RT]M，其中，λ为给定的比例因子，K为无人机摄像机的内参数矩阵，R为世界坐标系到摄像机坐标系的旋转矩阵，T为世界坐标系到摄像机坐标系的平移向量；
    根据拍摄的彩色棋盘格立体标定物的图像和灭点理论的约束方程，采用最小二乘法和张正友平面标定法线性求解出无人机摄像机的内参数矩阵。
  7. 一种基于彩色立体标定物的无人机标定系统，其特征在于：包括：
    放置模块，用于将彩色棋盘格立体标定物放置到待拍摄场景内，所述彩色棋盘格立体标定物为封闭式立体结构，所述彩色棋盘格立体标定物包含有至少一个顶面和一个侧面，所述彩色棋盘格立体标定物的每个表面采用彩色与白色相间、彩色与黑色相间、不同彩色相间或黑色与白色相间的棋盘格图像且任意相邻两个表面的棋盘格图像的颜色组合均不相同；
    拍摄模块，用于采用无人机至少从3个不同的方位拍摄彩色棋盘格立体标定物的图像；
    内参数求解模块，用于根据拍摄的彩色棋盘格立体标定物的图像采用灭点理论线性求解出无人机摄像机内参数；
    坐标投影变换模块，用于根据无人机摄像机内参数采用坐标投影变换方法确定无人机摄像机的空间位置和图像几何约束关系。
  8. 根据权利要求7所述的一种基于彩色立体标定物的无人机标定系统，其特征在于：所述彩色棋盘格立体标定物表面的棋盘格图像的颜色组合方式为彩色棋盘格立体标定物的表面采用彩色与白色相间的棋盘格图像，相邻表面采用不同彩色相间的棋盘格图像；若彩色棋盘格立体标定物为规则多面体构成的立体标定物，则其需要的非白色颜色总数Q满足条件：3≤Q≤maxPi，其中，maxPi＝max{Pi|i＝1，2，…，b}，b为彩色棋盘格立体标定物的角点数，且b＜e，e为彩色棋盘格立体标定物的总面数，Pi为彩色棋盘格立体标定物中的任意一个角点i对应的包含该角点的面数，maxPi为彩色棋盘格立体标定物的最多共角点平面数；若彩色棋盘格立体标定物为没有角点的规则立方体，则其所需的颜色数与其表面数相同。
  9. 根据权利要求7所述的一种基于彩色立体标定物的无人机标定系统，其特征在于：所述彩色棋盘格立体标定物的底面安装有四个可调节衬脚或者球台形托盘，所述彩色棋盘格立体标定物的顶部嵌入有气泡水平仪。
  10. 根据权利要求7、8或9所述的一种基于彩色立体标定物的无人机标定系统，其特征在于：所述内参数求解模块包括：
    初始化单元，用于确定无人机摄像机的模型以及空间点齐次坐标与图像点齐次坐标的映 射关系：若无人机摄像机的模型为针孔相机模型，则任一空间点P的空间点齐次坐标M与图像点齐次坐标m的映射关系表达式为：λm＝K[RT]M，其中，λ为给定的比例因子，K为无人机摄像机的内参数矩阵，R为世界坐标系到摄像机坐标系的旋转矩阵，T为世界坐标系到摄像机坐标系的平移向量；
    求解单元，用于根据拍摄的彩色棋盘格立体标定物的图像和灭点理论的约束方程，采用最小二乘法和张正友平面标定法线性求解出无人机摄像机的内参数矩阵。
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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190082156A1 (en) * 2017-09-11 2019-03-14 TuSimple Corner point extraction system and method for image guided stereo camera optical axes alignment
CN110390697A (zh) * 2019-07-11 2019-10-29 浙江大学 一种基于lm算法的毫米波雷达与摄像头联合标定方法
CN111199576A (zh) * 2019-12-25 2020-05-26 中国人民解放军军事科学院国防科技创新研究院 一种基于移动平台的室外大范围人体姿态重建方法
CN112785570A (zh) * 2021-01-19 2021-05-11 浙江商汤科技开发有限公司 图像注册正确性的检测方法、装置、设备及存储介质
US11158088B2 (en) 2017-09-11 2021-10-26 Tusimple, Inc. Vanishing point computation and online alignment system and method for image guided stereo camera optical axes alignment
CN114543840A (zh) * 2022-02-25 2022-05-27 四川大学 环境因素对空三摄影影响实验装置和测评方法
US11600185B2 (en) * 2020-05-01 2023-03-07 Honeywell International Inc. Systems and methods for flight planning for conducting surveys by autonomous aerial vehicles

Families Citing this family (30)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106651961B (zh) 2016-12-09 2019-10-11 中山大学 一种基于彩色立体标定物的无人机标定方法及系统
CN107274453A (zh) * 2017-06-12 2017-10-20 哈尔滨理工大学 一种结合标定与校正的摄像机三维测量装置、系统及方法
CN113822939A (zh) 2017-07-06 2021-12-21 华为技术有限公司 车载传感器的外部参数标定的方法和设备
US10635116B2 (en) * 2017-10-19 2020-04-28 Ford Global Technologies, Llc Video calibration with illumination invariant image
US10636137B1 (en) * 2017-10-23 2020-04-28 Amazon Technologies, Inc. System for determining object orientations in an image
CN108090934B (zh) * 2017-12-15 2021-06-08 云南大学 根据球截面的几何特性标定抛物折反射摄像机的方法
CN110246185B (zh) * 2018-03-07 2023-10-27 阿里巴巴集团控股有限公司 图像处理方法、装置、系统、存储介质和标定系统
CN108765494B (zh) * 2018-05-21 2021-12-24 南昌航空大学 一种基于圆柱体标定物的多相机标定方法
CN108765298A (zh) * 2018-06-15 2018-11-06 中国科学院遥感与数字地球研究所 基于三维重建的无人机图像拼接方法和系统
US11282225B2 (en) * 2018-09-10 2022-03-22 Mapbox, Inc. Calibration for vision in navigation systems
CN109870118B (zh) * 2018-11-07 2020-09-11 南京林业大学 一种面向绿色植物时序模型的点云采集方法
CN111192318B (zh) * 2018-11-15 2023-09-01 杭州海康威视数字技术股份有限公司 确定无人机位置和飞行方向的方法、装置及无人机
US11010641B2 (en) 2019-03-14 2021-05-18 Mapbox, Inc. Low power consumption deep neural network for simultaneous object detection and semantic segmentation in images on a mobile computing device
US11145087B2 (en) * 2019-04-04 2021-10-12 Airpro Diagnostics, Llc Optimized forward-facing camera calibration system with portable mounting apparatus and adaptable electronic calibration target display
CN111833404B (zh) * 2019-04-23 2023-10-31 富联精密电子(天津)有限公司 摄像机校正系统及摄像机校正方法
CN110827359A (zh) * 2019-10-29 2020-02-21 武汉大学 基于棋盘格三面体的相机与激光器外参检校方法及装置
CN110992429B (zh) * 2019-11-12 2022-04-22 武汉华中天经通视科技有限公司 一种单一场景大视场相机外参数标定方法
CN112319846B (zh) * 2020-09-25 2022-07-19 中国航空工业集团公司上海航空测控技术研究所 一种用于飞机发动机参数采集设备的参数标定方法
CN112033379B (zh) * 2020-11-03 2021-01-29 腾讯科技（深圳）有限公司 一种对象检测方法、装置以及计算机可读存储介质
CN112362034B (zh) * 2020-11-11 2022-07-08 上海电器科学研究所（集团）有限公司 基于双目视觉的固体发动机多节筒段对接引导测量方法
CN113077521B (zh) * 2021-03-19 2022-11-01 浙江华睿科技股份有限公司 一种相机标定方法及装置
CN113074635B (zh) * 2021-03-29 2022-10-28 哈尔滨市科佳通用机电股份有限公司 一种标定螺栓及利用该标定螺栓检测螺栓松脱的方法
CN113066086B (zh) * 2021-04-26 2022-08-30 深圳市商汤科技有限公司 道路病害检测方法及装置、电子设备和存储介质
CN113409402B (zh) * 2021-06-29 2022-07-26 湖南泽塔科技有限公司 相机标定板及其使用方法、相机标定的特征点提取方法
CN113393538B (zh) * 2021-07-14 2024-05-28 南京苏润科技发展有限公司 一种基于双棋盘格标定的闸门门缝检测方法
CN114267181B (zh) * 2022-03-03 2022-06-03 深圳市旗扬特种装备技术工程有限公司 一种城市道路路口的行人预警方法、装置及电子设备
CN114505860A (zh) * 2022-03-03 2022-05-17 深圳市启灵图像科技有限公司 3d手眼标定方法及系统
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CN114882124B (zh) * 2022-07-06 2022-09-30 季华实验室 标定器具和标定方法、装置、电子设备和存储介质
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1490765A (zh) * 2002-10-18 2004-04-21 �Ϻ���ͨ��ѧ 用于相机标定的方法及所用的彩色参照物
CN101504287A (zh) * 2009-01-22 2009-08-12 浙江大学 基于视觉信息的无人飞行器自主着陆的姿态参数估算方法
CN101576379A (zh) * 2009-05-12 2009-11-11 四川大学 基于二维彩色标靶的主动投影三维测量系统快速标定方法
US20130070094A1 (en) * 2011-09-20 2013-03-21 The Regents Of The University Of California, A California Corporation Automatic registration of multi-projector dome images
CN106651961A (zh) * 2016-12-09 2017-05-10 中山大学 一种基于彩色立体标定物的无人机标定方法及系统

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2465792A (en) * 2008-11-28 2010-06-02 Sony Corp Illumination Direction Estimation using Reference Object
GB2465793A (en) * 2008-11-28 2010-06-02 Sony Corp Estimating camera angle using extrapolated corner locations from a calibration pattern
CN103983961A (zh) * 2014-05-20 2014-08-13 南京理工大学 一种3d激光雷达和摄像机联合标定立体标定靶
CN105981074B (zh) * 2014-11-04 2018-02-02 深圳市大疆创新科技有限公司 用于标定成像装置的系统、方法和装置
CN104376572A (zh) * 2014-12-02 2015-02-25 成都理工大学 移动终端相机标定方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1490765A (zh) * 2002-10-18 2004-04-21 �Ϻ���ͨ��ѧ 用于相机标定的方法及所用的彩色参照物
CN101504287A (zh) * 2009-01-22 2009-08-12 浙江大学 基于视觉信息的无人飞行器自主着陆的姿态参数估算方法
CN101576379A (zh) * 2009-05-12 2009-11-11 四川大学 基于二维彩色标靶的主动投影三维测量系统快速标定方法
US20130070094A1 (en) * 2011-09-20 2013-03-21 The Regents Of The University Of California, A California Corporation Automatic registration of multi-projector dome images
CN106651961A (zh) * 2016-12-09 2017-05-10 中山大学 一种基于彩色立体标定物的无人机标定方法及系统

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190082156A1 (en) * 2017-09-11 2019-03-14 TuSimple Corner point extraction system and method for image guided stereo camera optical axes alignment
US11089288B2 (en) * 2017-09-11 2021-08-10 Tusimple, Inc. Corner point extraction system and method for image guided stereo camera optical axes alignment
US11158088B2 (en) 2017-09-11 2021-10-26 Tusimple, Inc. Vanishing point computation and online alignment system and method for image guided stereo camera optical axes alignment
CN110390697A (zh) * 2019-07-11 2019-10-29 浙江大学 一种基于lm算法的毫米波雷达与摄像头联合标定方法
CN110390697B (zh) * 2019-07-11 2021-11-05 浙江大学 一种基于lm算法的毫米波雷达与摄像头联合标定方法
CN111199576A (zh) * 2019-12-25 2020-05-26 中国人民解放军军事科学院国防科技创新研究院 一种基于移动平台的室外大范围人体姿态重建方法
CN111199576B (zh) * 2019-12-25 2023-08-18 中国人民解放军军事科学院国防科技创新研究院 一种基于移动平台的室外大范围人体姿态重建方法
US11600185B2 (en) * 2020-05-01 2023-03-07 Honeywell International Inc. Systems and methods for flight planning for conducting surveys by autonomous aerial vehicles
CN112785570A (zh) * 2021-01-19 2021-05-11 浙江商汤科技开发有限公司 图像注册正确性的检测方法、装置、设备及存储介质
CN114543840A (zh) * 2022-02-25 2022-05-27 四川大学 环境因素对空三摄影影响实验装置和测评方法

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