CN117333553A - Feature-based detection robot camera offset error compensation method - Google Patents
Feature-based detection robot camera offset error compensation method Download PDFInfo
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- CN117333553A CN117333553A CN202311143967.0A CN202311143967A CN117333553A CN 117333553 A CN117333553 A CN 117333553A CN 202311143967 A CN202311143967 A CN 202311143967A CN 117333553 A CN117333553 A CN 117333553A
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- 238000001514 detection method Methods 0.000 title claims abstract description 16
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- 201000010099 disease Diseases 0.000 description 3
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 3
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/40—Correcting position, velocity or attitude
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
- G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
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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
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/695—Control of camera direction for changing a field of view, e.g. pan, tilt or based on tracking of objects
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- 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
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
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- Signal Processing (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The invention discloses a characteristic-based detection robot camera offset error compensation method, which comprises the following steps: s1: the robot collects n batches of images and GPS data in an experimental field; s2: positioning and directional correcting are carried out on each image according to GPS coordinates; s3: sequencing the images according to the batch numbers, extracting features for matching, and recording feature point coordinates successfully matched; s4: converting the feature point coordinates into GPS coordinates; s5: and establishing a cost function and solving the optimal camera offset compensation parameter. The beneficial effects of the invention are as follows: by utilizing the characteristic information of the images, a matching relation between the images is established, a cost function related to the offset error compensation parameter is constructed, and the optimal offset error compensation parameter is solved by minimizing the error, so that manual calibration is replaced, and the efficiency and accuracy are improved.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a characteristic-based detection robot camera offset error compensation method.
Background
At present, in highway and airport maintenance, road surface image data is automatically collected based on a road surface detection robot, images are spliced to manufacture a map, diseases are automatically identified, and a map of disease distribution is generated, so that the detection efficiency of road surface diseases can be greatly improved. The road surface detection robot is provided with a linear array camera to scan and image the road surface when collecting data, and uses GPS equipment to record positioning data in real time, so that in order to be capable of making a high-precision map, the installation position of the camera and the offset parameter of the positioning center of the GPS equipment need to be accurately calibrated to determine the accurate GPS coordinates of the camera center when collecting image data, however, in daily operation and maintenance processes, the installation position of the camera often changes, and offset errors to a certain extent occur.
At present, offset compensation parameters mainly depend on manual calibration, firstly, image data and GPS data are collected back and forth in an experiment field on the basis of known camera installation design parameters, then images are spliced and released into a map, the image dislocation distance is measured on the map, then the camera offset compensation parameters are adjusted, and the calibration is completed repeatedly until the map dislocation distance is smaller. However, the method depends on manual calibration, and needs to repeatedly adjust the offset compensation parameters of the camera, which is low in efficiency and inaccurate.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a characteristic-based offset error compensation method for a detection robot camera.
The aim of the invention is achieved by the following technical scheme: a feature-based detection robot camera offset error compensation method comprises the following steps:
s1: the detection robot collects n batches of images and GPS data in an experimental field;
s2: positioning and directional correcting are carried out on each image according to GPS coordinates;
s3: sequencing the images according to the batch numbers, extracting features for matching, and recording feature point coordinates successfully matched;
s4: converting the feature point coordinates into GPS coordinates;
s5: and establishing a cost function and solving the optimal camera offset compensation parameter.
Preferably, in step S1, the robot collects n batches back and forth in the experimental field, and the image deflection angles of different batches are 180 °.
Preferably, in step S2, the method further includes the steps of:
s21: calculating the declination of each image from successive GPS coordinates, wherein the lateral ground resolution gsd of the image h Is fixed, known as longitudinal ground resolution gsd v The GPS coordinates corresponding to the center points of the images are obtained, the GPS coordinates corresponding to the four corner points of the images are obtained, and the GPS coordinate ranges of the four corner points of the images are recorded;
s22: calculating homography transformation matrix according to GPS coordinates and pixel coordinates of four corner points, transforming the image to obtain corrected image, arranging m images, and marking the corresponding upper left corner geositting as
g i (x g ,y g )(i＝1,2,...,m)。
Preferably, in step S22, the corrected image longitudinal direction is the north direction.
Preferably, in step S3, the images are sorted according to the lot numbers from small to large, and the image feature points are extracted by the SIFT feature extraction algorithm.
Preferably, in step S4, the method further includes the steps of:
s41: a pair of successfully matched images is img1 and img2, and the corresponding characteristic point is p (x 1 ,y 1 ) And p' (x) 2 ,y 2 ) Corresponding upper left corner GPS coordinate is g 1 (x g ,y g ) And g 2 (x g ,y g ) The corresponding deflection angle is y 1 And y 2 P (x) 1 ,y 1 ) Conversion of pixel coordinates to GPS coordinatesThe formula of (2) is:
s42: calculation by step S41
Preferably, in step S5, the method further comprises the steps of:
s51: after the feature points on the image img1 are converted into GPS coordinates, the coordinates are corrected by offset compensation parametersThe calculation formula is as follows:
s52: calculating GPS coordinates of the feature points on img2 after correction by adopting the step S51
S53: establishing a cost function between characteristic point pairs:
wherein M is j For the j (j=1, 2,) k pairs of matched feature points, k is the number of pairs of feature points that match successfully, and e is the error sum.
The invention has the following advantages: according to the invention, the matching relation between the images is established by utilizing the characteristic information of the images, the cost function related to the offset error compensation parameter is constructed, and the optimal offset error compensation parameter is solved by minimizing the error, so that manual calibration is replaced, and the efficiency and accuracy are improved.
Drawings
FIG. 1 is a schematic diagram of a flow of a method for detecting offset error compensation of a robot camera;
fig. 2 is a schematic diagram of the structure of image distribution of different batches.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, based on the embodiments of the invention, which are apparent to those of ordinary skill in the art without inventive faculty, are intended to be within the scope of the invention.
In addition, the embodiments of the present invention and the features of the embodiments may be combined with each other without collision.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the description of the present invention, it should be noted that, directions or positional relationships indicated by terms such as "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., are directions or positional relationships based on those shown in the drawings, or are directions or positional relationships conventionally put in use of the inventive product, or are directions or positional relationships conventionally understood by those skilled in the art, are merely for convenience of describing the present invention and for simplifying the description, and are not to indicate or imply that the apparatus or element to be referred to must have a specific direction, be constructed and operated in a specific direction, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
In the description of the present invention, it should also be noted that, unless explicitly specified and limited otherwise, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
In this embodiment, as shown in fig. 1, a method for compensating an offset error of a feature-based detection robot camera includes the following steps:
s1: the detection robot collects n batches of images and GPS data in an experimental field;
s2: positioning and directional correcting are carried out on each image according to GPS coordinates;
s3: sequencing the images according to the batch numbers, extracting features for matching, and recording feature point coordinates successfully matched;
s4: converting the feature point coordinates into GPS coordinates;
s5: and establishing a cost function and solving the optimal camera offset compensation parameter. By utilizing the characteristic information of the images, a matching relation between the images is established, a cost function related to the offset error compensation parameter is constructed, and the optimal offset error compensation parameter is solved by minimizing the error, so that manual calibration is replaced, and the efficiency and accuracy are improved.
Further, as shown in fig. 2, in step S1, the robot collects n batches back and forth in the experimental field, and the image deflection angles of different batches are 180 °. Specifically, b1, b2 and b3 are images of different batches, each image records a batch number, wherein the image drift angles of different batches differ by 180 °, the image drift angles of the same batch are the same, the dashed line box is an image, and the arrow is the robot travelling direction.
Still further, in step S2, the method further includes the following steps:
s21: calculating the declination of each image from successive GPS coordinates, wherein the lateral ground resolution gsd of the image h Is fixed, known as longitudinal ground resolution gsd v The GPS coordinates corresponding to the center points of the images are obtained, the GPS coordinates corresponding to the four corner points of the images are obtained, and the GPS coordinate ranges of the four corner points of the images are recorded;
s22: calculating homography transformation matrix according to GPS coordinates and pixel coordinates of four corner points, transforming the image to obtain corrected image, arranging m images, and marking the corresponding upper left corner geographic sitting as g i (x g ,y g ) (i=1, 2,) m. Preferably, in step S22, the corrected image longitudinal direction is the north direction.
In this embodiment, in step S3, the images are sorted according to the lot numbers from small to large, and the image feature points are extracted by the SIFT feature extraction algorithm. Specifically, the images are ordered according to the batch numbers from small to large, k images are randomly selected from the images in the same batch, the main effect is to reduce the calculation amount, the SIFT feature extraction algorithm is used for extracting the feature points of the images, the images adjacent to the SIFT feature extraction algorithm are inquired according to the GPS coordinate range of each image, feature matching is carried out one by one, and feature point coordinates successfully matched are recorded. In this embodiment, the setting can be made according to the actual situation.
In this embodiment, in step S4, the following steps are further included:
s41: a pair of successfully matched images is img1 and img2, and the corresponding characteristic point is p (x 1 ,y 1 ) And p' (x) 2 ,y 2 ) The corresponding upper left corner GPS coordinate is g 1 (x g ,y g ) And g 2 (x g ,y g ) The corresponding deflection angle is y 1 And y 2 P (x) 1 ,y 1 ) Conversion of pixel coordinates to GPS coordinatesThe formula of (2) is:
s42: calculation by step S41
In this embodiment, in step S5, the following steps are further included:
s51: after the feature points on the image img1 are converted into GPS coordinates, the coordinates are corrected by offset compensation parametersThe calculation formula is as follows:
s52: calculating GPS coordinates of the feature points on img2 after correction by adopting the step S51
S53: establishing a cost function between characteristic point pairs:
wherein M is j For the j (j=1, 2,) k pairs of matched feature points, k is the number of pairs of feature points that match successfully, and e is the error sum. Specifically, due to errors in camera offset parameters, it results inAnd->Inequality, therefore, by establishing a cost function for the camera offset compensation parameters, the optimal camera offset compensation parameters O (x) o ,y o ) Camera offset compensation parameter O (x o ,y o ) The camera offset compensation parameter O (x o ,y o ) Is 0.
Although the present invention has been described with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements and changes may be made without departing from the spirit and principles of the present invention.
Claims (7)
1. A characteristic-based detection robot camera offset error compensation method is characterized in that: the method comprises the following steps:
s1: the detection robot collects n batches of images and GPS data in an experimental field;
s2: positioning and directional correcting are carried out on each image according to GPS coordinates;
s3: sequencing the images according to the batch numbers, extracting features for matching, and recording feature point coordinates successfully matched;
s4: converting the feature point coordinates into GPS coordinates;
s5: and establishing a cost function and solving the optimal camera offset compensation parameter.
2. The feature-based detection robot camera offset error compensation method of claim 1, wherein: in the step S1, the robot collects n batches back and forth in an experimental field, and the image deflection angles of different batches are 180 degrees different.
3. The feature-based detection robot camera offset error compensation method of claim 2, wherein: in the step S2, the method further includes the following steps:
s21: calculating the declination of each image from successive GPS coordinates, wherein the lateral ground resolution gsd of the image h Is fixed, known as longitudinal ground resolution gsd v The GPS coordinates corresponding to the center points of the images are obtained, the GPS coordinates corresponding to the four corner points of the images are obtained, and the GPS coordinate ranges of the four corner points of the images are recorded;
s22: calculating homography transformation matrix according to GPS coordinates and pixel coordinates of four corner points, transforming the image to obtain corrected image, arranging m images, and marking the corresponding upper left corner geositting as
g i (x g ，y g )(i＝1，2，...，m)。
4. A method for compensating for camera offset errors of a feature-based inspection robot in accordance with claim 3, wherein: in the step S22, the corrected image longitudinal direction is the north direction.
5. The method for compensating for camera offset errors of a feature-based inspection robot of claim 4, wherein: in the step S3, the images are ordered according to the batch numbers from small to large, and the image feature points are extracted by the SIFT feature extraction algorithm.
6. The method for compensating for camera offset errors of a feature-based inspection robot of claim 5, wherein: in the step S4, the method further includes the following steps:
s41: a pair of successfully matched images is img1 and img2, and the corresponding characteristic point is p (x 1 ，y 1 ) And p' (x) 2 ，y 2 ) The corresponding upper left corner GPS coordinate is g 1 (x g ，y g ) And g 2 (x g ，y g ) The corresponding deflection angle is y 1 And y 2 P (x) 1 ，y 1 ) Conversion of pixel coordinates to GPS coordinatesThe formula of (2) is:
s42: calculation using the step S41
7. The method for compensating for camera offset errors of a feature-based inspection robot of claim 6, wherein: in the step S5, the method further includes the following steps:
S51：after the feature points on the image img1 are converted into GPS coordinates, the coordinates are corrected by offset compensation parametersThe calculation formula is as follows:
s52: calculating GPS coordinates of the feature points on img2 after correction by adopting the step S51
S53: establishing a cost function between characteristic point pairs:
wherein M is j For the j (j=1, 2,) k pairs of matched feature points, k is the number of pairs of feature points that match successfully, and e is the error sum.
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