CN112258577B - Method and system for evaluating confidence of monocular vision mapping measurement at vehicle end - Google Patents
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Abstract
The invention relates to a method and a system for evaluating the confidence of monocular vision mapping measurement at a vehicle end, wherein the method comprises the following steps: initializing each module covariance matrix of the vehicle-end monocular vision system, wherein the covariance matrix comprises a vehicle-end pose error covariance matrix omegaGAnd image feature point matching pixel error covariance matrix omegaP(ii) a Calculating a position covariance matrix of the target point after monocular vision mapping; and calculating the corresponding measurement confidence coefficient under the designated error radius according to the position covariance matrix based on a confidence ellipse theory. And calculating the confidence of the measurement result based on the specified error confidence interval to solve the problem of quantitative evaluation of uncertainty of the measurement result of the vehicle-end monocular mapping, directly provide the error distribution interval of the current measurement result for the user, provide the reliability evaluation basis of the visual measurement result in the applications of visual positioning, visual navigation, visual mapping and the like, and further guide the improvement of the visual mapping measurement precision.
Description
Technical Field
The invention relates to the field of computer vision positioning and mapping, in particular to a method and a system for evaluating confidence of vehicle-end monocular vision mapping measurement.
Background
Monocular vision has the advantages of low price, convenient installation, simple calibration process and the like, and is gradually applied to the vision positioning and image building process in a large scale in recent years. The visual positioning and mapping refers to recovering motion information of a camera from adjacent image frames through a mutual correlation relationship between visual images, and estimating a target position and establishing a point cloud map of a surrounding environment by combining an initial position prior value. The visual positioning and mapping provides target space position information for a plurality of applications such as visual obstacle avoidance and visual navigation, and the reliability of the measurement result provides safety coefficient measurement for upper-layer applications.
Currently, confidence evaluation of a visual positioning measurement result mainly uses a measurement information matrix to calculate an artificially defined algebraic value, and the method mainly has two problems: (1) the confidence coefficient is artificially defined and lacks physical effectiveness and rationality; (2) the confidence result cannot directly measure the error range of the current measurement value, and lacks guidance significance in practical application.
Disclosure of Invention
The invention provides a method and a system for evaluating the confidence of monocular vision mapping measurement at a vehicle end, aiming at the technical problems in the prior art, and solving the problems in the prior art.
The technical scheme for solving the technical problems is as follows: a method for evaluating confidence of monocular vision mapping measurement at a vehicle end comprises the following steps:
step 1, initializing each module error covariance matrix of a vehicle-end monocular vision system, wherein the covariance matrix comprises a vehicle-end position and attitude error covariance matrix omegaGAnd the image characteristic point matching pixel error covariance matrix omegaP；
Step 2, calculating a position covariance matrix of the target point after monocular vision mapping;
and 3, calculating the corresponding confidence coefficient under the specified error radius according to the position covariance matrix based on a confidence ellipse theory.
A vehicle-end monocular vision mapping measurement confidence evaluation system comprises: the device comprises an initialization module, a position covariance matrix estimation module and a confidence coefficient calculation module;
an initialization module for initializing each error covariance matrix of the vehicle-end monocular vision system, wherein the covariance matrix comprises a vehicle-end position and attitude error covariance matrix omegaGAnd the image characteristic point matching pixel error covariance matrix omegaP；
The position covariance matrix estimation module is used for calculating a position covariance matrix of the target point after monocular vision mapping;
and the confidence coefficient calculation module is used for calculating the corresponding confidence coefficient under the appointed error radius according to the position covariance matrix based on a confidence ellipse theory.
The invention has the beneficial effects that: the invention provides a method for evaluating the confidence of a vehicle-end monocular vision-based mapping measurement, which is used for calculating the confidence of a measurement result based on a specified error confidence interval so as to solve the problem of quantitative evaluation of uncertainty of the vehicle-end monocular measurement result, directly providing the error uncertainty of the current measurement result for a user, providing a reliability evaluation basis of the vision measurement result in the applications of vision positioning, vision navigation, vision mapping and the like, and further guiding the improvement of the vision measurement precision.
On the basis of the technical scheme, the invention can be further improved as follows.
Further, in the step 1, a monte carlo statistical method is adopted to calculate each error covariance matrix of the vehicle-end monocular vision system.
Further, the step 2 comprises:
Further, the position covariance matrix is an error of the target in the transverse direction and the longitudinal direction under the coordinate system of the monocular camera, and the size of the position covariance matrix is 2 × 2 square matrix:
further, the step 2 further comprises: perfecting the boundary value of the position covariance matrix of the target point according to the sample data;
the process of calculating the boundary values of the position covariance matrix of the target point includes:
in step 205, the estimated value of the boundary value of the covariance matrix of the target point is:
n represents the number of position covariance matrices of the target point.
Further, in step 3, the error radius is set to R according to the measurement precision requirement, and the corresponding confidence CIf is calculated according to the relationship between the confidence and the confidence interval as: CIf-1-exp (-R)2/(2λ))；
The beneficial effect of adopting the further scheme is that: the system covariance is calculated by adopting a Monte Carlo statistical method, and the accuracy of parameter estimation is improved.
Drawings
FIG. 1 is a flow chart of a method for evaluating confidence in a monocular vision mapping measurement at a vehicle end according to the present invention;
FIG. 2 is a flowchart of an embodiment of a method for evaluating confidence in a vehicle-end monocular vision mapping measurement according to the present invention;
FIG. 3 is a block diagram of an embodiment of a system for evaluating confidence in a monocular vision mapping measurement at a vehicle end according to the present invention;
fig. 4 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention. :
in the drawings, the components represented by the respective reference numerals are listed below:
101. the device comprises an initialization module 102, a position covariance matrix estimation module 103, a confidence coefficient calculation module 201, a processor 202, a communication interface 203, a memory 204 and a communication bus.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
Fig. 1 is a flowchart of a method for evaluating confidence in a vehicle-end monocular vision mapping measurement according to the present invention, and as shown in fig. 1, the method includes:
step 1, initializing each module error covariance matrix of a vehicle-end monocular vision system, wherein the monocular vision system comprises a plurality of modules, such as a vehicle-end position and attitude calculation module and an image feature point matching pixel calculation module, and the corresponding covariance matrix comprises a vehicle-end position and attitude error covariance matrix omegaGAnd the image characteristic point matching pixel error covariance matrix omegaP。
The covariance error matrix of the position and the attitude is the covariance error matrix of the position and the attitude of the GPS. In the process of visual mapping, the above error covariance matrixes are kept unchanged.
And 2, calculating a position covariance matrix of the target point after passing through the monocular vision system.
And 3, calculating the corresponding confidence coefficient under the specified error radius according to the position covariance matrix based on a confidence ellipse theory.
The invention provides a method for evaluating the confidence of a vehicle-end monocular vision-based mapping measurement, which is used for calculating the confidence of a measurement result based on a specified error confidence interval so as to solve the problem of quantitative evaluation of uncertainty of the vehicle-end monocular measurement result, directly providing the error uncertainty of the current measurement result for a user, providing a reliability evaluation basis of the vision measurement result in the applications of vision positioning, vision navigation, vision mapping and the like, and further guiding the improvement of the vision measurement precision.
Example 1
Embodiment 1 provided by the present invention is an embodiment of a method for evaluating confidence in a vehicle-end monocular vision mapping measurement provided by the present invention, and as shown in fig. 2, is a flowchart of an embodiment of a method for evaluating confidence in a vehicle-end monocular vision mapping measurement provided by the present invention, as can be seen from fig. 2, the embodiment includes:
step 1, initializing each module error covariance matrix of the vehicle-end monocular vision system, wherein the covariance matrix comprises a vehicle-end position and attitude error covariance matrix omegaGAnd the image characteristic point matching pixel error covariance matrix omegaP。
Preferably, in step 1, a monte carlo statistical method is adopted to calculate each error covariance matrix of the vehicle-end monocular vision system.
The system covariance is calculated by adopting a Monte Carlo statistical method, and the accuracy of parameter estimation is improved.
And 2, calculating a position covariance matrix of the target point after passing through the monocular vision system.
Preferably, step 2 comprises:
Specifically, the position covariance matrix is the error distribution of the target in the transverse direction and the longitudinal direction under the monocular camera coordinate system, and the size is 2 × 2 square matrix:
each target point possesses three-dimensional spatial position information, and its position covariance matrix is 3 × 3 matrix, but the position of the target point can be considered as negligible error in the height direction, mainly considering errors from the transverse direction and the longitudinal direction along the monocular camera coordinate system, so the position covariance matrix is reduced to 2 × 2 square matrix, here expressed as:
the covariance matrix is a diagonal matrixI.e. omega12＝Ω21。
Further, the calculation of the boundary value of the position covariance matrix of the target point is mainly based on a comparison method, and the process includes:
And step 204, sequentially comparing the difference values of the diagonal element values of the position covariance matrices of the new target point and the current target point, and adding the position covariance matrix of the new target point into the set omega of the position covariance matrices when the difference value exceeds a set threshold value.
In step 205, the estimated value of the boundary value of the covariance matrix of the target point is:
n represents the number of position covariance matrices of the target point, and may be, for example, 30.
And 3, calculating the corresponding confidence coefficient under the specified error radius according to the position covariance matrix based on a confidence ellipse theory.
Preferably, in step 3, the error radius is set to R according to the measurement precision requirement, and the corresponding confidence CIf is calculated according to the relationship between the confidence and the confidence interval as: CIf-1-exp (-R)2/(2λ))。
Ω (1,1), Ω (2,2), and Ω (1,2) respectively denote elements of corresponding positions in the covariance matrix of the target position.
Example 2
Embodiment 2 provided by the present invention is an embodiment of a vehicle-end monocular vision mapping measurement confidence evaluation system provided by the present invention, and as shown in fig. 2, is a structural block diagram of an embodiment of a vehicle-end monocular vision mapping measurement confidence evaluation system provided by the present invention, as can be seen from fig. 2, the system includes: an initialization module 101, a location covariance matrix estimation module 102 and a confidence calculation module 103.
An initialization module 101, configured to initialize each error covariance matrix of the vehicle-end monocular vision system, where the covariance matrix includes a vehicle-end position and attitude error covariance matrix ΩGAnd the image characteristic point matching pixel error covariance matrix omegaP。
And the position covariance matrix estimation module 102 is configured to calculate a position covariance matrix of the target point after monocular vision mapping.
The confidence coefficient calculation module 103 is configured to calculate, based on a confidence ellipse theory, a corresponding confidence coefficient under the specified error radius according to the position covariance matrix.
Fig. 3 is a schematic entity structure diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, the electronic device may include: the system comprises a processor 201, a communication interface 202, a memory 203 and a communication bus 204, wherein the processor 201, the communication interface 202 and the memory 203 are communicated with each other through the communication bus 204. The processor 201 may invoke a computer program stored on the memory 203 and operable on the processor 201 to perform the method for estimating the confidence of the vehicle-end monocular vision mapping measurement provided by the above embodiments, for example, including: step 1, initializing each error covariance matrix of the vehicle-end monocular vision system, wherein the covariance matrix comprises a vehicle-end position and attitude error covariance matrix omegaGAnd the image characteristic point matching pixel error covariance matrix omegaP(ii) a Step 2, calculating a position covariance matrix of the target point after monocular vision mapping; and 3, calculating the corresponding confidence coefficient under the specified error radius according to the position covariance matrix based on a confidence ellipse theory.
An embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to, when executed by a processor, perform the method for estimating confidence in vehicle-end monocular vision mapping measurement provided in the foregoing embodiments, for example, the method includes: step 1, initializing each error covariance matrix of a vehicle-end monocular vision system, wherein the covariance matrix comprises vehicle-end position and attitude error covariance matrixArray omegaGAnd the image characteristic point matching pixel error covariance matrix omegaP(ii) a Step 2, calculating a position covariance matrix of the target point after monocular vision mapping; and 3, calculating the corresponding confidence coefficient under the specified error radius according to the position covariance matrix based on a confidence ellipse theory.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.
Claims (6)
1. A method for evaluating confidence of monocular vision mapping measurement at a vehicle end is characterized by comprising the following steps:
step 1, initializing each module covariance matrix of a vehicle-end monocular vision system, wherein the covariance matrix comprises a vehicle-end position and attitude error covariance matrix omegaGAnd the image characteristic point matching pixel error covariance matrix omegaP；
Step 2, calculating a position covariance matrix of the target point after passing through the monocular vision system;
step 3, based on a confidence ellipse theory, calculating a corresponding confidence coefficient under the appointed error radius according to the position covariance matrix;
the step 2 comprises the following steps:
step 201, combining with the monocular vision mapping process, based on the ceres optimization library, calculating a position covariance matrix set Ω of the feature point set on the target point surfaceiI is 1, …, n, wherein n is the number of the characteristic points;
step 202, calculating the position covariance matrix omega of the target pointO：
The position covariance matrix represents errors of the target in the transverse direction and the longitudinal direction of the monocular camera coordinate system, and the size of the position covariance matrix is 2 x 2 square matrix:
in the step 3, the error radius is set to R according to the requirement of measurement accuracy, and the corresponding confidence CIf is calculated according to the relationship between the confidence and the confidence interval as follows: CIf-1-exp (-R)2/(2λ))；
2. The method of claim 1, wherein the covariance matrix of each module of the vehicle-end monocular vision system is calculated in step 1 using monte carlo statistics.
3. The method of claim 1, wherein step 2 further comprises: perfecting the boundary value of the position covariance matrix of the target point according to the sample data;
the process of calculating the boundary values of the position covariance matrix of the target point includes:
step 203, initializing a set Ω ═ { Φ } of the covariance matrix of the current target point, and adding the covariance matrix of the first target point position, where Ω ═ Ω { (Ω) } at this time1}；
Step 204, comparing the difference values of diagonal element values of the position covariance matrices of the new target point and the current target point in sequence, and adding the position covariance matrix of the new target point into the set omega of the position covariance matrices when judging that the difference value exceeds a set threshold;
in step 205, the estimated value of the boundary value of the covariance matrix of the target point is:
n represents the number of position covariance matrices of the target point.
4. A vehicle-end monocular vision mapping measurement confidence evaluation system is characterized by comprising: the device comprises an initialization module, a position covariance matrix estimation module and a confidence coefficient calculation module;
an initialization module for initializing each module error covariance matrix of the vehicle-end monocular vision system, wherein the covariance matrix comprises a vehicle-end position and attitude error covariance matrix omegaGAnd image feature point matching pixel error covariance matrix omegaP；
The position covariance matrix estimation module is used for calculating a position covariance matrix of the target point after monocular vision mapping;
the confidence coefficient calculation module is used for calculating the corresponding confidence coefficient under the designated error radius;
the process of the location covariance matrix estimation module calculating the location covariance matrix comprises:
step 201, combining with the monocular vision mapping process, based on the ceres optimization library, calculating a position covariance matrix set Ω of the feature point set on the target point surfaceiI is 1, …, n, wherein n is the number of the characteristic points;
step 202, calculating the position covariance matrix omega of the target pointO：
The position covariance matrix represents errors of the target in the transverse direction and the longitudinal direction of the monocular camera coordinate system, and the size of the position covariance matrix is 2 x 2 square matrix:
the confidence coefficient calculation module sets the error radius as R according to the measurement precision requirement, and calculates the corresponding confidence coefficient CIf according to the relation between the confidence coefficient and the confidence interval as follows: CIf-1-exp (-R)2/(2λ))；
5. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the method for vehicle-end monocular visual mapping measurement confidence assessment according to any one of claims 1 to 3.
6. A non-transitory computer readable storage medium, having stored thereon a computer program, wherein the computer program, when being executed by a processor, implements the steps of the method for vehicle end monocular vision mapping measurement confidence assessment according to any one of claims 1 to 3.
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