CN109760107B - Monocular vision-based robot positioning accuracy evaluation method - Google Patents
Monocular vision-based robot positioning accuracy evaluation method Download PDFInfo
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- CN109760107B CN109760107B CN201910056723.6A CN201910056723A CN109760107B CN 109760107 B CN109760107 B CN 109760107B CN 201910056723 A CN201910056723 A CN 201910056723A CN 109760107 B CN109760107 B CN 109760107B
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Abstract
The invention discloses a monocular vision-based robot positioning accuracy evaluation method, wherein a calibration plate is arranged at any position in a robot movement space, a robot observes the same calibration plate for multiple times at different positions in the working environment in the movement process, the observation method is used for sampling for multiple times, and the quantitative comparison of the positioning accuracy of different positioning algorithms in the same working environment is realized according to all observation information acquired in the movement process and the data statistical characteristics of the observation information. The invention obtains the real pose of the robot without other precise instruments, does not need a large number of repeated experimental tests, saves the use cost and improves the working efficiency. In addition, a calibration plate with an unknown environment pose is adopted, so that the evaluation method is simpler and more convenient, and the motion environment and space of the robot are not limited. Finally, the accuracy of the robot positioning algorithm is described by expression of positive correlation to the positioning error, and the comparison of the positioning accuracy among different algorithms is realized.
Description
Technical Field
The invention relates to the technical field of robot vision, in particular to a monocular vision-based robot positioning accuracy evaluation method.
Background
The visual positioning of the mobile robot is widely applied to various aspects of the visual navigation of the mobile robot, and the positioning precision directly influences the navigation capability of the mobile robot. Therefore, it is important to evaluate the accuracy of positioning the mobile robot. The evaluation method which is common at present is mostly compared and analyzed by a motion acquisition device and a calibration board group with position correlation added in a motion environment.
However, the method of arranging the motion capture device and the calibration plate set has high application cost, great modification degree to the environment, and increased complexity of use, and such an evaluation method is difficult to be widely applied in practical applications. The invention provides a positioning accuracy evaluation method based on monocular vision, which realizes quantitative comparison of different positioning algorithm accuracy by acquiring sensing feedback of a mobile robot in the moving process and observing a calibration plate at any position in a motion space by using a monocular camera carried on a robot body.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a robot positioning accuracy evaluation method based on monocular vision. The method arranges the calibration plate at any position in the robot motion space without calibrating the posture (position and posture) between the calibration plates. In the moving process, the robot observes the same calibration plate for multiple times at different positions in the working environment of the robot, samples are obtained for multiple times by the observation method, and quantitative comparison of positioning accuracy of different positioning algorithms in the same working environment is realized according to all observation information acquired in the moving process of one time and the data statistical characteristics of the observation information.
In order to achieve the purpose, the technical scheme provided by the invention is as follows:
a robot positioning accuracy evaluation method based on monocular vision comprises the following steps:
s1: arranging a calibration plate in a visual range along the movement of the robot;
s2: the robot moves to a certain calibration plate in the motion space;
s3: when the robot approaches the calibration plate, recording the real-time pose information of the current algorithm of the robot and the relative pose information of the robot and the calibration plate;
s4: and fitting to obtain an error distribution curve, and evaluating the accuracy of the positioning algorithm.
Further, in step S3, the real-time pose information of the robot is obtained through a positioning algorithm.
Further, in step S3, the relative pose information between the robot and the calibration board is obtained by a multi-view geometric method, and the specific calculation steps are as follows:
converting an image plane coordinate system into an image pixel coordinate system:
wherein, uO0v is an image pixel coordinate system, and the unit is a pixel; xO1y is an image plane coordinate system and the unit is millimeter;
let the physical size of each pixel in the u-axis and v-axis directions be dxAnd dy；
In the above formula, dx，dy，u0，v0Are all assumed parameters;
camera coordinate system to world coordinate system:
in the above formula, (X)C,YC,ZC) Image points in a camera coordinate system, (X, Y, Z) image points in a world coordinate system; r is a 3 × 3 rotation matrix, t is a 3 × 1 translation matrix, and L is represented as a 4 × 4 matrix;
world coordinate system and pixel coordinate system:
wherein f is the camera focal length.
Further, the step S4 fits to obtain an error distribution curve, and the specific steps are as follows:
Pifor the true value, P, of the pose of the robot obtained in step S3i' calculating the difference between the pose containing the error under the positioning algorithm:
Pi’＝Δi·Pi；
assuming that the error of the positioning algorithm conforms to Gaussian distribution, by means of a calibration plate with unknown pose in a working environment, the pose P containing the error of the robot is recorded at different positions simultaneouslyi' and the pose T of the robot relative to the calibration plate to acquire the pose error delta of the roboti＝Pi’·Ti -1·r-1So as to obtain the expression of the error distribution curve of the robot under the current positioning algorithm:
Ω＝Δ1·Δ2 -1＝P1’·Ti -1·T2·P2 ’-1
from the above formula, the obtained error expression is positively related to the positioning error of the robot, so that the positioning error distribution of the estimated positioning algorithm can be plotted, and the accuracy of the positioning algorithm can be evaluated.
Compared with the prior art, the principle and the advantages of the scheme are as follows:
1. the real pose of the robot is obtained without other precise instruments, and a large number of repeated experimental tests are not needed, so that the use cost is saved, and the working efficiency is improved.
2. The calibration plate with unknown environment poses is adopted, pose information among a plurality of calibration plates does not need to be acquired in advance, the evaluation method is simpler and more convenient, and the motion environment and space of the robot are not limited.
3. The accuracy of the robot positioning algorithm is described by expression positively correlated to the positioning error, and the original true value error distribution expression is replaced, so that the comparison of the positioning accuracy among different algorithms is realized.
Drawings
FIG. 1 is a flow chart of a robot positioning accuracy evaluation method based on monocular vision according to the present invention;
FIG. 2 is a schematic diagram of error depiction in the monocular vision-based robot positioning accuracy evaluation method of the present invention;
FIG. 3 is a schematic diagram of an image plane coordinate system to an image pixel coordinate system;
fig. 4 is a schematic diagram of a world coordinate system transformed from a camera coordinate system.
Detailed Description
The invention will be further illustrated with reference to specific examples:
referring to fig. 1 and 2, the method for evaluating the positioning accuracy of the robot based on monocular vision according to the present embodiment includes the following steps:
s1: arranging a calibration plate in a visual range along the movement of the robot;
s2: the robot moves to a certain calibration plate in the motion space;
s3: when the robot approaches the calibration plate, recording the real-time pose information of the current algorithm of the robot and the relative pose information of the robot and the calibration plate;
in step S3, the real-time pose information of the robot is obtained by a positioning algorithm.
The relative pose information of the robot and the calibration plate is obtained by a camera calibration method, and the specific calculation steps are as follows:
the image plane coordinate system is converted into an image pixel coordinate system, as shown in fig. 3:
uO0v is an image pixel coordinate system, and the unit is a pixel; xO1y is an image plane coordinate system and the unit is millimeter;
let the physical size of each pixel in the u-axis and v-axis directions be dxAnd dy；
In the above formula, dx，dy，u0，v0Are all assumed parameters;
the camera coordinate system is transformed into a world coordinate system, as shown in fig. 4:
wherein (X)C,YC,ZC) Image points in a camera coordinate system, (X, Y, Z) image points in a world coordinate system; r is a 3 × 3 rotation matrix, t is a 3 × 1 translation matrix, and L is represented as a 4 × 4 matrix;
world coordinate system and pixel coordinate system:
wherein f is the camera focal length.
S4: fitting to obtain an error distribution curve, and evaluating the accuracy of the positioning algorithm; the method comprises the following specific steps: piFor the true value, P, of the pose of the robot obtained in step S3i' calculating the difference between the pose containing the error under the positioning algorithm:
Pi’＝Δi·Pi；
assuming that the error of the positioning algorithm conforms to Gaussian distribution, by means of a calibration plate with unknown pose in a working environment, the pose P containing the error of the robot is recorded at different positions simultaneouslyi' and the pose T of the robot relative to the calibration plate to acquire the pose error delta of the roboti＝Pi’·Ti -1·r-1So as to obtain the expression of the error distribution curve of the robot under the current positioning algorithm:
Ω＝Δ1·Δ2 -1＝P1’·Ti -1·T2·P2’-1
from the above formula, the obtained error expression is positively related to the positioning error of the robot, so that the positioning error distribution of the estimated positioning algorithm can be plotted, and the accuracy of the positioning algorithm can be evaluated.
The embodiment does not need other precise instruments to obtain the real pose of the robot, and a large number of repeated experimental tests are not needed, so that the use cost is saved, and the working efficiency is improved. In addition, the calibration plate with the unknown environment pose is adopted, the pose information among a plurality of calibration plates does not need to be acquired in advance, the evaluation method is simpler and more convenient, and the motion environment and the space of the robot are not limited. And finally, describing the accuracy of the robot positioning algorithm by expression positively correlated to the positioning error, replacing the original true value error distribution expression, and realizing the comparison of the positioning accuracy among different algorithms.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, so that variations based on the shape and principle of the present invention should be covered within the scope of the present invention.
Claims (2)
1. A robot positioning accuracy evaluation method based on monocular vision is characterized by comprising the following steps:
s1: arranging a calibration plate in a visual range along the movement of the robot;
s2: the robot moves to a certain calibration plate in the motion space;
s3: when the robot approaches the calibration plate, recording the real-time pose information of the current algorithm of the robot and the relative pose information of the robot and the calibration plate;
s4: fitting to obtain an error distribution curve, and evaluating the accuracy of the positioning algorithm;
in step S3, the relative pose information between the robot and the calibration board is obtained by a camera calibration method, and the specific calculation steps are as follows:
converting an image plane coordinate system into an image pixel coordinate system:
u and v are image pixel coordinate systems, and the unit is a pixel; x and y are image half-plane coordinate systems with the unit of millimeter;
assuming that the physical dimensions of each pixel in the u-axis and v-axis directions are dx and dy;
in the above formula, dx, dy, u0，v0Are all assumed parameters;
camera coordinate system to world coordinate system:
wherein (X)C，YC，ZC) Image points in a camera coordinate system, (X, Y, Z) image points in a world coordinate system; r is a 3 × 3 rotation matrix, t is a 3 × 1 translation matrix, and L is represented as a 4 × 4 matrix;
world coordinate system and pixel coordinate system:
wherein f is the focal length of the camera;
the step S4 is to obtain an error distribution curve by fitting, and the specific steps are as follows:
Pifor the true value, P, of the pose of the robot obtained in step S3i' calculating the difference between the pose containing the error under the positioning algorithm:
Pi′＝Δi·Pi；
assuming that the error of the positioning algorithm conforms to Gaussian distribution, by means of a calibration plate with unknown pose in a working environment, the pose of the robot with the error is recorded at different positions simultaneouslyPi' and the pose T of the robot relative to the calibration plate to acquire the pose error delta of the roboti＝Pi′·Ti -1·r-1So as to obtain the expression of the error distribution curve of the robot under the current positioning algorithm:
Ω＝Δ1·Δ2 -1＝P1′·Ti -1·T2·P2′-1，
from the above formula, the obtained error expression is positively related to the positioning error of the robot, so that the positioning error distribution of the estimated positioning algorithm can be plotted, and the accuracy of the positioning algorithm can be evaluated.
2. The monocular vision based robot positioning accuracy evaluation method of claim i, wherein in step S3, the real-time pose information of the robot is obtained through a positioning algorithm.
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| CN111678521B (en) * | 2020-06-18 | 2021-12-28 | 上海大学 | Method and system for evaluating positioning accuracy of mobile robot |
| CN111896032B (en) * | 2020-09-29 | 2021-09-03 | 北京清微智能科技有限公司 | Calibration system and method for monocular speckle projector position |
| CN112781498B (en) * | 2021-02-09 | 2022-07-08 | 南京景曜智能科技有限公司 | Robot actuator pose repetition precision measuring method and device |
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