CN110969663A - Static calibration method for external parameters of camera - Google Patents

Static calibration method for external parameters of camera Download PDF

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CN110969663A
CN110969663A CN201811154021.3A CN201811154021A CN110969663A CN 110969663 A CN110969663 A CN 110969663A CN 201811154021 A CN201811154021 A CN 201811154021A CN 110969663 A CN110969663 A CN 110969663A
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point
coordinates
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CN110969663B (en
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陈亮
李晓东
芦超
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Beijing Momenta Technology Co Ltd
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Beijing Chusudu Technology Co ltd
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Abstract

A static calibration method of external parameters of a camera; includes step S1: setting a plurality of mark points; step S2: converting the actual GPS position of the mark point to an IMU coordinate system with the IMU as the origin of coordinates to obtain a point P1; step S3: converting the P1 into a camera coordinate system with a camera as a coordinate origin through a rotation translation transformation matrix to obtain P2; step S4: converting P2 into an image coordinate system through an internal reference matrix of the camera device to obtain the projection coordinate of the point on the image; step S5: calculating a reprojection error function; step S6: optimizing the roto-translational transformation matrix, and repeating the steps S3-S5 until the reprojection error is below a specified threshold. The method constructs a loss function through the GPS coordinates of the mark points and the pixel coordinates of the mark points in the camera device to finally obtain the Euclidean transformation relation between the GPS coordinates and the pixel coordinates, namely a rotation translation transformation matrix. The method does not need to modify acquisition equipment or a vehicle, and can finish the calibration of the external parameters of the camera in a static state.

Description

Static calibration method for external parameters of camera
Technical Field
The invention relates to the field of intelligent driving, in particular to a static calibration method for external parameters of a camera.
Background
Currently, the accuracy of an autopilot map is measured in a GPS coordinate system, that is, each point on the map needs to be represented by a GPS coordinate. In a multi-sensor solution, the camera and GPS may not be mounted at the same time in the same location of the vehicle, but may be separated by a distance of 2-3 meters. Therefore, external parameters of the camera need to be calibrated to establish a spatial position relationship between the camera and the GPS module. If the external parameters of the camera are not calibrated, the image is directly constructed according to the image of the camera and the position of the vehicle body, and finally an error of two meters and three meters can be generated.
In the conventional calibration method, the camera and the IMU (the IMU is provided with the GPS module) need to be bound together very closely, and then the camera and the IMU are strongly shaken to excite the axis of the IMU and the axis of the camera to be attached together, so that the trajectory of the IMU and the trajectory of the camera are attached to each other, and the calibration of the camera is completed. However, the camera and IMU mounted on the vehicle cannot shake, so the conventional calibration method is not applicable.
In addition, in the existing technical scheme, a related research on how to adjust the transformation matrix by establishing a relation between the reprojection and the transformation matrix in the coordinate transformation process is lacked.
Disclosure of Invention
In view of the problems in the prior art, the invention adopts the following technical scheme:
a calibration method for external parameters of a camera is characterized by comprising the following steps:
step S1: setting one or more mark points, acquiring an image comprising the mark points through a camera device, and identifying the positions of the mark points in the image (u0, v 0);
step S2: converting the actual GPS position of the mark point to an IMU coordinate system with the IMU as the origin of coordinates to obtain a point P1;
step S3: converting P1 into a coordinate system with the camera as a coordinate origin through a rotation translation transformation matrix M [ R | t ] to obtain a point P2;
step S4: converting P2 into the coordinate system of the image through the internal reference matrix K of the camera device to obtain the projection coordinates (u1, v1) of the point on the image;
step S5: constructing a reprojection error function of (u0, v0) and (u1, v1), and calculating a reprojection error, wherein the reprojection error is related to the rotational-translational transformation matrix M [ R | t ];
step S6: optimizing the value of M [ R | t ], and repeating the steps S3-S5 until the reprojection error is lower than a specified threshold, wherein the value of M [ R | t ] is taken as a calibration result;
in the rotation and translation transformation matrix M [ R | t ], R represents rotation, and t represents translation.
Preferably, in step S1, the image capturing devices are disposed on the roof or around the vehicle.
Preferably, the camera device comprises a plurality of cameras, and images acquired by the cameras have an overlapping area.
Preferably, in step S1, the position (u0, v0) of the marker point in the image is found row by row and column by human or computer.
Preferably, in step S1, the positions (u0, v0) of the marker points in the image are obtained by a trained neural network.
Preferably, in step S5, the reprojection error function is as follows:
Figure BDA0001818570390000021
k is an internal reference matrix of the camera;
Rb，tb: the attitude and position of the IMU, respectively;
Figure BDA0001818570390000022
the GPS coordinate of the ith mark point;
Figure BDA0001818570390000023
the position of the ith marker point in the image.
Preferably, in step S6, the value of M [ R | t ] is optimized by the least squares method.
Preferably, the initial values of the rotation R and the translation t are both 0.
Preferably, in step S1, the number of the marked points is 10-20.
Preferably, in step S2, the actual GPS position of the marker may be measured in advance, or may be measured directly by using an RTK device during calibration.
The invention is characterized by the following aspects, but not limited to the following aspects:
the invention is characterized in that: (1) providing a static calibration method of external parameters of a camera; the calibration of the external parameters of the camera can be completed in a static state without modifying acquisition equipment or modifying a vehicle; the traditional method for marking points needs to bind a camera and an IMU (a GPS module is arranged on the IMU) together very closely and then shake violently, but when a vehicle is static, the traditional method cannot be used. This is one of the points of the present invention.
(2) A brand-new loss function is designed; the rotation and translation transformation matrix can be accurately and effectively solved through the function. This is mainly reflected in the following aspects: the loss function takes into account the attitude and position of the IMU and the GPS coordinates of the marked points; the loss function is associated with the roto-translational transformation matrix in the mathematical model, and the value of M [ R | t ] is adjusted as a result of the calibration by setting a specified threshold value for the reprojection error. This is one of the points of the present invention.
Drawings
FIG. 1 is a step diagram of the camera extrinsic parameter static calibration method of the present invention.
The present invention is described in further detail below. The following examples are merely illustrative of the present invention and do not represent or limit the scope of the claims, which are defined by the claims.
Detailed Description
The technical scheme of the invention is further explained by the specific implementation mode in combination with the attached drawings.
To better illustrate the invention and to facilitate the understanding of the technical solutions thereof, typical but non-limiting examples of the invention are as follows:
the three-dimensional map has good expression effect, has the characteristics of small data volume, high transmission speed, low cost and the like, and is widely applied to the fields of social management, public service, propaganda and display and the like through development for many years.
Generally, a three-dimensional map modeling worker completes three-dimensional modeling work by splicing images by taking a high-resolution satellite map or aerial photograph as a reference map according to data acquired by an acquisition worker; however, aerial photography or unmanned aerial vehicle shooting has many disadvantages, such as high cost and low resolution. The application provides a brand-new data acquisition mode of a three-dimensional map, and particularly relates to a method for acquiring data by installing a 360-degree omnibearing high-resolution camera device on a vehicle, wherein the camera device is installed on the roof of the vehicle, such as 4 high-resolution cameras around the vehicle, or the camera devices are respectively installed around the vehicle, the vehicle traverses each street crossing, simultaneously the camera device on the vehicle shoots high-resolution images as a reference map, and then the images are spliced to complete three-dimensional modeling work
Extrinsic parameters of the camera are parameters in the world coordinate system such as camera position, rotation direction, etc.; the camera external parameter calibration method is mapping from world coordinates to pixel coordinates, the world coordinates are considered to be defined, calibration is the world coordinates and the pixel coordinates of known calibration control points, the mapping relation is solved, and once the relation is solved, the world coordinates of the points can be reversely deduced from the pixel coordinates of the points.
The purpose of camera calibration is to determine the values of parameters of the camera by which the staff can map points in a three-dimensional space to image space, or vice versa.
The invention relates to a static calibration method, wherein a vehicle is static during calibration, and the position of a marker point is unchanged. The marking point may be a special marking board, or a marking rod. The GPS position of the marker is known, and the GPS position may be measured in advance, directly acquired during calibration, or directly measured using RTK equipment during calibration.
Generally, there are 6 external parameters of the camera, the rotation parameters of three axes are (ω, δ, θ), and then 3 × 3 rotation matrices of each axis are combined (i.e. multiplication between matrices is performed first) to obtain a set of three-axis rotation information R, the size of which is also 3 × 3; t (Tx, Ty, Tz).
Example 1
In this embodiment, a plurality of high resolution video cameras or cameras on the top of the vehicle can realize 360-degree high-definition video shooting or photo shooting, and the calibration method for the external parameters of the cameras in the state that the vehicle is stationary includes the following steps:
step S1: one or more marker points are set, an image including the marker points is acquired by a camera on the roof of the vehicle, and the positions of the marker points in the image are identified (u0, v 0). The position of the mark point in the image is an observed value, the mark point can be a special mark plate or a mark rod, and the acquired signal accuracy is high due to the fact that the mark plate occupies a small area and the GPS position is known; the GPS position of the marker is known, and the GPS position may be measured in advance, directly acquired during calibration, or directly measured using RTK equipment during calibration.
Because there are 6 external parameters of the camera, at least 6 mark points are needed to solve the corresponding parameters through the corresponding mathematical equation, generally, in actual operation, about 10 to 20 mark points are selected to reduce the influence of data noise on the solution, although the more mark points are more beneficial to the solution theoretically, the solution cost is increased; the coordinates of the mark points can be manually searched for corresponding coordinates row by row and column by column in an image shot by a camera, and can also be searched for by a computer.
Step S2: the actual GPS position of the marked point is converted to an IMU coordinate system with the IMU as the origin of coordinates, resulting in point P1. The longitude and latitude system of GPS is an ellipsoid system, not orthogonal, but the rotational-translational transformation matrix M R | t is based on an orthogonal coordinate system. Thus, converting GPS points to IMU coordinates means that longitude and latitude coordinates can be converted to cartesian coordinates (i.e., orthogonal coordinates). This conversion relationship is a standard conversion method within a GIS system. After transformation, the coordinates of the point are three-dimensional (X, Y, Z) coordinates representing the location of the marker point relative to the IMU. The above-mentioned rotation-translation transformation matrix M [ R | t ], R represents rotation, and t represents translation.
Step S3: the point P2 is obtained by transforming P1 into the camera coordinate system with the camera as the origin of coordinates through the rotation-translation transformation matrix M [ R | t ].
Step S4: the projection coordinates of the point on the image are obtained by converting P2 into the image coordinate system through the projection matrix of the camera (u1, v 1). The projection coordinates on the image are estimated values, the projection matrix is the camera's internal reference matrix, and the step is actually converting the three-dimensional point coordinates P2 into two-dimensional coordinates (u1, v1) of the point theoretically on the camera image by the camera's internal reference matrix.
Step S5: constructing a reprojection error function of (u0, v0) and (u1, v1), and calculating a reprojection error, wherein the reprojection error is related to M [ R | t ]; the reprojection error function is as follows:
Figure BDA0001818570390000041
k is an internal reference matrix of the camera;
Rb，tb: the attitude and position of the IMU, respectively;
Figure BDA0001818570390000051
the GPS coordinate of the ith mark point;
Figure BDA0001818570390000052
the position of the ith marker point in the image. This location is also an observation and may be identified manually.
Wherein
Figure BDA0001818570390000053
The coordinates (u1, v1) of the ith marker point are estimated values.
Step S6: optimizing the value of M [ R | t ] by a least square method, repeating the steps S3-S5, and continuously iterating the value of M [ R | t ] until the reprojection error is lower than a specified threshold value, wherein the value of M [ R | t ] is used as a calibration result. The method of optimizing the value of M [ R | t ] by the least square method is the prior art, and there are various ways to solve the value of M [ R | t ], for example, the value of M [ R | t ] optimization can be determined by derivation of a reprojection error function and the magnitude of the derivative; the initial value of M [ R | t ] may be 0 for simplicity, with a true size around the minus sixth power of 10.
Example 2
In this embodiment, a plurality of high resolution video cameras or cameras may be arranged around the vehicle, as long as 360-degree high-definition video shooting or photographing is implemented, and the calibration method for the external parameters of the cameras includes the following steps:
step S1: one or more marker points are set, an image including the marker points is acquired by cameras or cameras around the vehicle, and the positions of the marker points in the image are identified (u0, v 0). The position coordinates of the mark points in the image can be calculated through a trained neural network, and the neural network is trained through a large amount of existing data (namely the coordinates of the known mark points in the image are corresponding).
Step S2: the actual GPS position of the marked point is converted to an IMU coordinate system with the IMU as the origin of coordinates, resulting in point P1. Here, the GPS position and IMU coordinates are both three-dimensional coordinates, and the resulting point P1 is also a three-dimensional coordinate.
Step S3: converting P1 to a camera coordinate system with the camera as the origin of coordinates through a rotation translation transformation matrix M [ R | t ], and obtaining a point P2. Here also point P2 is a three-dimensional coordinate.
Step S4: the projection coordinates of the point on the image are obtained by converting P2 into the image coordinate system through the projection matrix of the camera (u1, v 1). The projection matrix is the camera's internal reference matrix, and this step is actually converting the three-dimensional point coordinate P2 into the two-dimensional coordinates (u1, v1) of the point theoretically on the camera image by the camera's internal reference matrix.
Step S5: constructing a reprojection error function of (u0, v0) and (u1, v1), and calculating a reprojection error, wherein the reprojection error is related to M [ R | t ]; the reprojection error function is as follows:
Figure BDA0001818570390000054
wherein K: an internal reference matrix of the camera;
Rb，tb: the attitude and position of the IMU, respectively;
Figure BDA0001818570390000061
the GPS coordinate of the ith mark point;
Figure BDA0001818570390000062
the position of the ith marker point in the image. This location is also an observation and may be identified manually.
Wherein
Figure BDA0001818570390000063
The coordinates (u1, v1) of the ith marker point are estimated values.
Step S6: and optimizing the value of M [ R | t ] by a least square method, and repeating the steps S3-S5 until the reprojection error is lower than a specified threshold value, wherein the value of M [ R | t ] is used as a calibration result.
The applicant declares that the present invention illustrates the detailed structural features of the present invention through the above embodiments, but the present invention is not limited to the above detailed structural features, that is, it does not mean that the present invention must be implemented depending on the above detailed structural features. It should be understood by those skilled in the art that any modifications of the present invention, equivalent substitutions of selected components of the present invention, additions of auxiliary components, selection of specific modes, etc., are within the scope and disclosure of the present invention.
The preferred embodiments of the present invention have been described in detail, however, the present invention is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present invention within the technical idea of the present invention, and these simple modifications are within the protective scope of the present invention.
It should be noted that the various technical features described in the above embodiments can be combined in any suitable manner without contradiction, and the invention is not described in any way for the possible combinations in order to avoid unnecessary repetition.
In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as the disclosure of the present invention as long as it does not depart from the spirit of the present invention.

Claims (10)

1. A calibration method for external parameters of a camera is characterized by comprising the following steps:
step S1: setting one or more mark points, acquiring an image comprising the mark points through a camera device, and identifying the positions of the mark points in the image (u0, v 0);
step S2: converting the actual GPS position of the mark point to an IMU coordinate system with the IMU as the origin of coordinates to obtain a point P1;
step S3: converting P1 into a coordinate system with the camera as a coordinate origin through a rotation translation transformation matrix M [ R | t ] to obtain a point P2;
step S4: converting P2 into the coordinate system of the image through the internal reference matrix K of the camera device to obtain the projection coordinates (u1, v1) of the point on the image;
step S5: constructing a reprojection error function of (u0, v0) and (u1, v1), and calculating a reprojection error, wherein the reprojection error is related to the rotational-translational transformation matrix M [ R | t ];
step S6: optimizing the value of M [ R | t ], and repeating the steps S3-S5 until the reprojection error is lower than a specified threshold, wherein the value of M [ R | t ] is taken as a calibration result;
in the rotation and translation transformation matrix M [ R | t ], R represents rotation, and t represents translation.
2. The method of claim 1, wherein: in step S1, the image pickup devices are provided on the roof or around the vehicle.
3. The method according to claims 1-2, characterized in that: the camera device comprises a plurality of cameras, and images acquired by the cameras have an overlapping area.
4. A method according to claims 1-3, characterized in that: in step S1, the position (u0, v0) of the marker point in the image is found row by row and column by human or computer.
5. The method according to claims 1-4, characterized in that: in step S1, the positions (u0, v0) of the marker points in the image are obtained through a trained neural network.
6. The method of claim 1, wherein: in step S5, the reprojection error function is as follows:
Figure FDA0001818570380000011
wherein K: an internal reference matrix of the camera;
Rb，tb: the attitude and position of the IMU, respectively;
Figure FDA0001818570380000012
the GPS coordinate of the ith mark point;
Figure FDA0001818570380000021
the position of the ith marker point in the image.
7. The method according to claims 1-6, characterized in that: in step S6, the value of M [ R | t ] is optimized by the least square method.
8. The method according to claims 1-7, characterized in that: the initial values of the rotation R and the translation t are both 0.
9. The method according to claims 1-8, characterized in that: in step S1, the number of the marking points is 10-20.
10. The method according to claims 1-9, characterized in that: in step S2, the actual GPS position of the marker may be measured in advance, or may be measured directly by using an RTK device during calibration.
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