CN110969663B - Static calibration method for external parameters of camera - Google Patents
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Abstract
A static calibration method for external parameters of a camera; comprises the following steps of S1: setting a plurality of mark points; step S2: converting the actual GPS position of the marked point into an IMU coordinate system with the IMU as a coordinate origin to obtain a point P1; step S3: converting P1 into a camera coordinate system with a camera as a coordinate origin by using 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 a projection coordinate of the point on the image; step S5: calculating a re-projection error by the constructed re-projection error function; step S6: and optimizing the rotation translation transformation matrix, and repeating the steps S3-S5 until the re-projection error is lower than a specified threshold. The method constructs a loss function through GPS coordinates of the mark points and pixel coordinates of the mark points in the image pickup device, so as to finally obtain an European transformation relation between the two, namely a rotation translation transformation matrix. According to the method, the acquisition equipment is not required to be modified, the vehicle is not required to be modified, and the calibration of the camera external parameters can be completed in a static state.
Description
Technical Field
The application 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 under the GPS coordinate system, that is, every point on the map needs to be represented by GPS coordinates. In a multi-sensor approach, the camera and GPS may not be mounted at the same location of the vehicle at the same time, but may be separated by a distance of 2-3 meters. Therefore, external parameters of the camera need to be calibrated, and a spatial position relation between the camera and the GPS module is established. If the calibration of the camera external parameters is not carried out, the image is directly built according to the image of the camera and the position of the vehicle body, and finally, errors of two meters and three meters can be generated.
In the traditional calibration method, the camera and the IMU (the IMU is provided with a GPS module) are closely bound together, and then shake vigorously to excite the shaft of the IMU and the shaft of the camera to be stuck together, so that the locus of the IMU and the locus of the camera are stuck together, and the calibration of the camera is completed. However, the camera and IMU mounted on the vehicle cannot be rocked, so that the conventional calibration method is not applicable.
In addition, in the prior art, related research on how to build a relation between the reprojection and the transformation matrix to adjust the transformation matrix in the coordinate transformation process is not available.
Disclosure of Invention
In view of the problems existing in the prior art, the application adopts the following technical scheme:
a method for calibrating external parameters of a camera, the method comprising the steps of:
step S1: setting one or more mark points, acquiring an image comprising the mark points through an imaging device, and identifying the positions (u 0, v 0) of the mark points in the image;
step S2: converting the actual GPS position of the marked point into an IMU coordinate system with the IMU as a coordinate origin to obtain a point P1;
step S3: converting P1 into a coordinate system with the image pickup device as a coordinate origin by using a rotation translation transformation matrix M [ R|t ] to obtain a point P2;
step S4: converting P2 into a coordinate system of the image through an internal reference matrix K of the image pickup device to obtain projection coordinates (u 1, v 1) of the point on the image;
step S5: constructing a reprojection error function of (u 0, v 0) and (u 1, v 1), and calculating a reprojection error, wherein the reprojection error is related to the rotary translational transformation matrix M [ R|t ];
step S6: optimizing the value of M [ R|t ], repeating the steps S3-S5 until the re-projection error is lower than the specified threshold, wherein the value of M [ R|t ] is used as the calibration result;
r in the rotation translation transformation matrix M [ R|t ] represents rotation, and t represents translation.
Preferably, in step S1, the image capturing device is disposed around a roof or a vehicle.
Preferably, the image pickup device includes a plurality of cameras, and an overlapping area is provided between images acquired by the cameras.
Preferably, in step S1, the positions (u 0, v 0) of the marker points in the image are found by human or computer line by line.
Preferably, in step S1, the position (u 0, v 0) of the marker point in the image is obtained by means of a trained neural network.
Preferably, in step S5, the re-projection error function is as follows:
k is an internal reference matrix of the camera;
R b ，t b : the gesture and the position of the IMU are respectively;
the GPS coordinates of the ith mark point;
the position of the i-th 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 all 0.
Preferably, in step S1, the number of the marking points is 10-20.
Preferably, in step S2, the actual GPS position of the marker point may be measured in advance, or may be measured directly by using the RTK apparatus at the time of calibration.
The application is characterized in the following aspects, but is not limited to the following aspects:
the application has the following points: (1) A static calibration method for external parameters of a camera is provided; the calibration of camera external parameters can be completed in a static state without modifying acquisition equipment or vehicles; the traditional punctuation method needs to bind a camera and an IMU (a GPS module is arranged on the IMU) together very closely and shake the IMU severely, and when a vehicle is stationary, the traditional method cannot be used. This is one of the points of the application.
(2) A brand new loss function is designed; the rotation translation transformation matrix can be accurately and effectively obtained through the function. This is mainly manifested in the following aspects: the loss function takes into account the attitude and position of the IMU and the GPS coordinates of the marker points; the loss function is linked with the rotation translation transformation matrix in the mathematical model, and the value of M [ R|t ] is adjusted by setting a specified threshold value through the reprojection error as the calibrated result. This is one of the points of the application.
Drawings
FIG. 1 is a step diagram of a camera external parameter static calibration method according to the present application.
The present application will be described in further detail below. The following examples are merely illustrative of the present application and are not intended to represent or limit the scope of the application as defined in the claims.
Detailed Description
The technical scheme of the application is further described below by the specific embodiments with reference to the accompanying drawings.
For a better illustration of the present application, which is convenient for understanding the technical solution of the present application, exemplary but non-limiting examples of the present application are as follows:
the three-dimensional map has the advantages of good expression effect, small data volume, high transmission speed, low cost and the like, and is widely applied to the fields of social management, public service, propaganda display and the like after years of development.
Generally, three-dimensional modeling staff complete three-dimensional modeling work by splicing images by taking a high-resolution satellite map or an aerial photo as a reference map according to data acquired by acquisition staff; however, aerial photography or unmanned aerial vehicle photography has numerous disadvantages, such as excessive cost, low resolution, and the like. The application provides a brand new data acquisition mode of a three-dimensional map, which is characterized in that a 360-degree omnibearing high-resolution camera device is arranged on a vehicle, for example, 4 high-resolution cameras are arranged on the front, rear, left and right of a roof, and the camera devices are respectively arranged on the front, rear, left and right of the vehicle, the vehicle traverses each street crossing, the camera device on the vehicle shoots a high-resolution image as a reference map, and then the image is spliced to complete the three-dimensional modeling work
The external parameters of the camera are parameters in the world coordinate system, such as camera position, rotation direction, etc.; the method for calibrating the external parameters of the camera is to map world coordinates to pixel coordinates, wherein the world coordinates are considered to be defined, calibration is to know the world coordinates and the pixel coordinates of calibration control points, the mapping relation is solved, and once the relation is solved, the world coordinates of the points can be reversely deduced by the pixel coordinates of the points.
The purpose of camera calibration is to determine the values of some parameters of the camera by which a worker can map points in a three-dimensional space to image space, or vice versa.
The application relates to a static calibration method, wherein the vehicle is static during calibration, and the position of a marking point is unchanged. The marking point may be a special sign, or a marking bar. The GPS position of the marker point is known, and can be measured in advance, directly obtained during calibration, or measured by using RTK equipment during calibration.
Typically, there are 6 external parameters of the camera, the rotation parameters of the three axes are (ω, δ, θ), and then the 3*3 rotation matrices of each axis are combined (i.e. multiplied between the first matrices) to obtain the three axis rotation information R, whose size is 3*3; translation parameters (Tx, ty, tz) of the three axes of t.
Example 1
In this embodiment, through a plurality of high resolution cameras or cameras on the top of the vehicle, 360-degree high definition shooting or photographing can be realized, and in a stationary state of the vehicle, the calibration method for external parameters of the camera includes the following steps:
step S1: one or more marker points are set, an image including the marker points is acquired by a camera at the roof of the vehicle, and the positions (u 0, v 0) of the marker points in the image are identified. The position of the marking point in the image is an observed value, the marking point can be a special marking plate or a marking rod, and the acquired signal is high in accuracy due to the fact that the marking point occupies a small area and the GPS position is known; the GPS position of the marker point is known, and can be measured in advance, directly obtained during calibration, or measured by using RTK equipment during calibration.
Because there are 6 external parameters of the camera, in order to calculate the corresponding parameters through the corresponding mathematical equation, at least 6 mark points are needed, usually in actual operation, in order to reduce the influence of data noise on the solution, about 10 to 20 mark points are selected, although theoretically, more mark points are more beneficial to the solution, but the cost of the solution is increased; the coordinates of the marking points can be found in the image shot by the camera by manually searching corresponding coordinates row by row and column by column, and can also be found by a computer.
Step S2: and converting the actual GPS position of the marked point into an IMU coordinate system with the IMU as the origin of coordinates to obtain a point P1. The longitude and latitude system of GPS is an ellipsoid system, not orthogonal, but the rotation translation transformation matrix M [ R|t ] is based on an orthogonal coordinate system. Thus, converting GPS points to an IMU coordinate system lower representation may convert the longitude and latitude high coordinates to Cartesian coordinates (i.e., an orthogonal coordinate system). The conversion relation is a standard conversion method in the GIS system. After conversion, the coordinates of the point are three-dimensional coordinates (X, Y, Z) representing the position of the marker point relative to the IMU. The rotation-translation transformation matrix M [ R|t ], R represents rotation, and t represents translation.
Step S3: and converting P1 into a camera coordinate system with a camera as a coordinate origin by rotating and translating a transformation matrix M [ R|t ] to obtain a point P2.
Step S4: p2 is converted into an image coordinate system through a projection matrix of the camera, and projection coordinates (u 1, v 1) of the point on the image are obtained. The projection coordinates on the image are estimated values, the projection matrix here being the reference matrix of the camera, this step is in fact the conversion of the three-dimensional point coordinates P2 into two-dimensional coordinates (u 1, v 1) of the point on the camera image in theory by means of the reference matrix of the camera.
Step S5: constructing a re-projection error function of (u 0, v 0) and (u 1, v 1), calculating a re-projection error, the error being related to M [ R|t ]; the reprojection error function is as follows:
k is an internal reference matrix of the camera;
R b ，t b : the gesture and the position of the IMU are respectively;
the GPS coordinates of the ith mark point;
the position of the i-th marker point in the image. The location is also an observation and may be manually identified.
Wherein the method comprises the steps ofIs the ithThe coordinates (u 1, v 1) of the marker point are estimated values.
Step S6: and 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 value of M [ R|t ] is optimized by a least square method, which is the prior art, and the value of M [ R|t ] is solved in a plurality of modes, for example, the value of M [ R|t ] is judged by the derivative through derivation of a reprojection error function; the initial value of M R|t may be 0 for simplicity, with a true size around the minus six power of 10.
Example 2
In this embodiment, a plurality of high-resolution cameras or cameras may be disposed around a vehicle, so long as 360-degree high-definition shooting or photographing is achieved, the calibration method of 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 a camera or a camera around the vehicle, and the positions (u 0, v 0) of the marker points in the image are identified. The position coordinates of the marker points in the image can be calculated by a trained neural network, which is trained by a large amount of existing data (namely, the coordinates of the known marker points in the image).
Step S2: and converting the actual GPS position of the marked point into an IMU coordinate system with the IMU as the origin of coordinates to obtain a 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: and converting P1 into a camera coordinate system with a camera as a coordinate origin by rotating and translating a transformation matrix M [ R|t ] to obtain a point P2. The point P2 here is also a three-dimensional coordinate.
Step S4: p2 is converted into an image coordinate system through a projection matrix of the camera, and projection coordinates (u 1, v 1) of the point on the image are obtained. The projection matrix here is the internal reference matrix of the camera, which is in effect the conversion of the three-dimensional point coordinates P2 into two-dimensional coordinates (u 1, v 1) of the point on the camera image in theory by means of the internal reference matrix of the camera.
Step S5: constructing a re-projection error function of (u 0, v 0) and (u 1, v 1), calculating a re-projection error, the error being related to M [ R|t ]; the reprojection error function is as follows:
wherein K: an internal reference matrix of the camera;
R b ，t b : the gesture and the position of the IMU are respectively;
the GPS coordinates of the ith mark point;
the position of the i-th marker point in the image. The location is also an observation and may be manually identified.
Wherein the method comprises the steps ofThe coordinates (u 1, v 1) of the i-th 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 re-projection error is lower than a specified threshold value, wherein the value of M [ R|t ] is used as a calibration result.
The applicant states that the detailed structural features of the present application are described by the above embodiments, but the present application is not limited to the above detailed structural features, i.e. it does not mean that the present application must be implemented depending on the above detailed structural features. It should be apparent to those skilled in the art that any modifications of the present application, equivalent substitutions of selected components of the present application, addition of auxiliary components, selection of specific modes, etc., are within the scope of the present application and the scope of the disclosure.
The preferred embodiments of the present application have been described in detail above, but the present application is not limited to the specific details of the above embodiments, and various simple modifications can be made to the technical solution of the present application within the scope of the technical concept of the present application, and all the simple modifications belong to the protection scope of the present application.
In addition, the specific features described in the above embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various possible combinations are not described further.
Moreover, any combination of the various embodiments of the application can be made without departing from the spirit of the application, which should also be considered as disclosed herein.
Claims (9)
1. A method for calibrating external parameters of a camera, the method comprising the steps of:
step S1: setting one or more mark points, acquiring an image comprising the mark points through an imaging device, and identifying the positions (u 0, v 0) of the mark points in the image;
step S2: converting the actual GPS position of the marked point into an IMU coordinate system with the IMU as a coordinate origin to obtain a point P1;
step S3: converting P1 into a coordinate system with the image pickup device as a coordinate origin by using a rotation translation transformation matrix M [ R|t ] to obtain a point P2;
step S4: converting P2 into a coordinate system of the image through an internal reference matrix K of the image pickup device to obtain projection coordinates (u 1, v 1) of the point on the image;
step S5: constructing a reprojection error function of (u 0, v 0) and (u 1, v 1), and calculating a reprojection error, wherein the reprojection error is related to the rotary translational transformation matrix M [ R|t ];
step S6: optimizing the value of M [ R|t ], repeating the steps S3-S5 until the re-projection error is lower than the specified threshold, wherein the value of M [ R|t ] is used as the calibration result;
r in the rotation translation transformation matrix M [ R|t ] represents rotation, and t represents translation;
in step S5, the re-projection error function is as follows:
wherein K: an internal reference matrix of the camera;
R b ，t b : the gesture and the position of the IMU are respectively;
the GPS coordinates of the ith mark point;
the position of the i-th marker point in the image.
2. The method according to claim 1, characterized in that: in step S1, the image pickup device is disposed around a roof or a vehicle.
3. The method according to claim 1, characterized in that: the image pickup device comprises a plurality of cameras, and an overlapping area is formed between images acquired by the cameras.
4. The method according to claim 1, characterized in that: in step S1, the positions (u 0, v 0) of the marker points in the image are obtained by searching row by row and column by human or computer.
5. The method according to claim 1, characterized in that: in step S1, the positions (u 0, v 0) of the marker points in the image are obtained through a trained neural network.
6. The method according to claim 1, characterized in that: in step S6, the value of M [ R|t ] is optimized by the least squares method.
7. The method according to claim 1, characterized in that: the initial values of the rotation R and the translation t are all 0.
8. The method according to claim 1, characterized in that: in step S1, the number of the marked points is 10-20.
9. The method according to claim 1, characterized in that: in step S2, the actual GPS position of the marker point may be measured in advance, or may be measured directly by using the RTK apparatus during calibration.
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| CN112489111B (en) * | 2020-11-25 | 2024-01-30 | 深圳地平线机器人科技有限公司 | Camera external parameter calibration method and device and camera external parameter calibration system |
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