CN113763479B - Calibration method of refraction and reflection panoramic camera and IMU sensor - Google Patents
Calibration method of refraction and reflection panoramic camera and IMU sensor Download PDFInfo
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Abstract
A calibration method of a refraction and reflection panoramic camera and an IMU sensor belongs to the technical field of camera calibration, and in order to solve the problems existing in the prior art, the method comprises the following steps: calculating the external parameters through an EPNP algorithm according to the internal parameters of the refraction and reflection camera, and establishing coordinates of constraint calculation control points under a camera coordinate system by using N three-dimensional points so as to obtain the external parameters of the refraction and reflection panoramic camera; acquiring measurement data of the IMU, performing pre-integration processing on the measurement data, deducing state information of a next key frame according to a measurement model and an estimated quantity of pre-integration about bias, and solving external parameter data of the IMU through singular value decomposition; and finding the relation between the two coordinate systems according to the measurement models of the refraction and reflection panoramic camera and the IMU, optimizing external parameters between the refraction and reflection camera and the IMU for constructing a re-projection error and a pose estimated value between the camera and the IMU, obtaining a final external parameter standard number, and realizing accurate calibration of the refraction and reflection panoramic camera and the IMU on the external parameters.
Description
Technical Field
The invention belongs to the technical field of camera calibration, and particularly relates to a calibration method of a refraction and reflection panoramic camera and an IMU sensor.
Background
Camera calibration is one of the fundamental problems in computer vision, a hotspot problem for vision measurement research for a long time. Because the internal structure of the camera, the imaging mechanism difference, the camera motion and the measured value of the sensor are deviated from the true value due to various reasons (such as electromagnetic disturbance, inaccurate manual installation and the like), the obtained image has different degrees of distortion, the sensor and the camera must be modeled and calibrated, the measuring characteristics of the sensor and the distortion characteristics of the camera are mastered to acquire accurate camera parameters to establish a camera model, so that good image information is acquired and the accuracy of the camera model is improved. The calibration of the camera is basically divided into two types, the first is the self calibration of the camera; the second is a calibration method that relies on a calibration reference. The method is characterized in that surrounding objects are shot by a camera, intersection points on a camera image plane are found to obtain camera parameters by utilizing constraint information of camera motion, but the method is based on curved surfaces and quadratic curves, has larger errors and poor robustness, and is not suitable for high-precision application occasions. The latter is imaged by the camera through a calibration reference, and internal and external parameters of the camera are calculated through mutual conversion of an image coordinate system, a camera coordinate system and a world coordinate system, and a later spatial arithmetic operation. The method is high in calibration precision and suitable for application occasions with high precision requirements. The calibration of the conventional camera is a linear problem, while the calibration of the catadioptric panoramic camera is a nonlinear problem, most of the existing methods are aimed at the conventional camera, and the external parameter calibration of the catadioptric panoramic camera and an Inertial Measurement Unit (IMU) is still a challenging problem.
Compared with the traditional camera system, the catadioptric panoramic imaging system has the advantages that the field of view is enlarged by adding one reflecting element on the basis of the traditional camera, and the maximum spatial field of view range can reach a hemisphere. And the refraction and reflection panoramic imaging system uses the convex surface of the quadric surface with rotational symmetry as a reflecting mirror, collects, compresses and reflects the light rays in the field of view into the camera system below, and finally realizes panoramic imaging of the circular view. The IMU can acquire the information such as the acceleration and the angular velocity of the IMU, and is used for detecting and measuring the sensor of the acceleration and the rotation. The IMU may provide a relative positioning information, determined by measuring the path of motion relative to the origin object, and by incorporating a folio reflection panoramic camera, a relatively accurate positioning may be achieved. Therefore, the external parameter calibration method based on the refraction and reflection panoramic camera and the IMU sensor has great effect on the influence of precision.
The Chinese patent publication number is CN105678783A, the patent name is a data fusion calibration method of a refraction and reflection panoramic camera and a laser radar, and the method is mainly used for effectively calibrating parameters in the panoramic camera, but lacks of external parameter calibration and data optimization.
The Chinese patent publication number is CN111207774A, the patent name is CN111207774A, and the method and system for calibrating the external parameters of the laser-IMU are disclosed.
Disclosure of Invention
The invention aims to solve the problem that the refraction and reflection panoramic camera and the laser radar lack external parameter calibration and data optimization, so that errors occur in measurement; a problem of limited laser field of view; an external parameter calibration method for a refraction and reflection panoramic camera and an IMU sensor is provided.
The invention solves the technical problems by adopting the technical scheme that:
a method for calibrating external parameters of a refraction and reflection panoramic camera and an IMU sensor comprises the following steps:
step 1, obtaining external parameters of a refraction and reflection panoramic camera: according to the internal parameters of the refraction and reflection camera, calculating the external parameters through an EPNP algorithm, and establishing coordinates of constraint calculation control points under a camera coordinate system by using N three-dimensional points, so that the influence of true characteristic points on the estimation error of the pose of the camera is reduced, and the external parameters of the refraction and reflection panoramic camera are obtained;
step 2, obtaining external parameters of the IMU sensor: acquiring measurement data of the IMU, performing pre-integration processing on the acquired IMU data, deducing state information of a next key frame according to a measurement model and an estimated quantity of pre-integration about offset, and solving external parameter data of the IMU through singular value decomposition;
step 3, calibrating external parameters of the refraction and reflection camera-IMU: and according to the measurement models of the refraction and reflection panoramic camera and the IMU sensor, finding the relation between the refraction and reflection panoramic camera coordinate system and the IMU coordinate system, constructing a re-projection error and a pose estimation value between the camera and the IMU, optimizing external parameters between the refraction and reflection camera and the IMU, obtaining a final external parameter standard number, and realizing accurate calibration of the refraction and reflection panoramic camera and the IMU on the external parameters.
The beneficial effects of the invention are as follows: firstly, the invention fuses the refraction and reflection panoramic camera and the sensor used in the positioning and mapping process, and ensures the accuracy of the original data while expanding the field of view. And secondly, the IMU pre-integration and pose estimation which is judged at the upper and lower moments are used for correcting the deviation, so that the result of external parameter calibration optimization is improved. Meanwhile, error optimization is carried out between the refraction and reflection panoramic camera and the IMU, residual errors are used as constraint conditions for external parameter calibration, and more accurate external acquisition estimation is carried out by continuously optimizing the errors. And finally, the relative pose of the camera and the IMU is required to be used in the positioning process, and the data fusion between the camera and the IMU is required to be combined for calibration, so that the method has an important influence on the quality of the effect of subsequent processing.
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Fig. 1: the invention discloses an external parameter calibration method flow chart of a refraction and reflection panoramic camera and an IMU sensor.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings.
As shown in fig. 1, an external parameter calibration method for a catadioptric panoramic camera and an IMU sensor includes the following steps:
step 1, obtaining external parameters of a refraction and reflection panoramic camera: according to the internal parameters of the refraction and reflection camera, calculating the external parameters through an EPNP algorithm, establishing coordinates of constraint calculation control points under a camera coordinate system by using N three-dimensional points, and reducing the influence of true characteristic points on the estimation error of the pose of the camera in this way to obtain the external parameters of the refraction and reflection panoramic camera;
step 2, obtaining external parameters of the IMU sensor: acquiring measurement data of the IMU, performing pre-integration processing on the acquired IMU data, deducing state information of a next key frame according to a measurement model and an estimated quantity of pre-integration about bias, and solving external parameter data of the IMU through singular value decomposition;
step 3, calibrating external parameters of the refraction and reflection camera-IMU: and according to the measurement models of the refraction and reflection panoramic camera and the IMU sensor, finding the relation between the refraction and reflection panoramic camera coordinate system and the IMU coordinate system, constructing a re-projection error and a pose estimation value between the camera and the IMU, optimizing external parameters between the refraction and reflection camera and the IMU, obtaining a final external parameter standard number, and realizing accurate calibration of the refraction and reflection panoramic camera and the IMU on the external parameters.
The step 1 is to obtain the calibration of the catadioptric panoramic camera, and the specific steps are as follows:
1-1, preparing a checkerboard calibration plate with known size;
1-2, fixing a calibration plate, and acquiring images of different positions and angles of the calibration plate in the images by using a mobile catadioptric panoramic camera;
1-3, extracting angular point information calibrated by a checkerboard in each image through a calibration algorithm, and calculating external parameters of the catadioptric camera by using an EPNP algorithm according to camera internal parameter results of prior information;
1) Establishing a world coordinate system, wherein reference points of the camera coordinate system are respectivelyAnd->And the control points under the two coordinate systems are respectively +.>And->
2) The coordinates of the reference point are expressed by EPNP algorithm as a weighted sum of the coordinates of the control points:and the same relationship still exists under the camera coordinate system:
selecting the center of gravity of the three-dimensional reference point as a first control point:
3) Calculation ofCenter of gravity->Sum matrix A
4) Calculation ofAnd center of gravity->Sum matrix B
5) Calculate h=b -1 A, carrying out singular value decomposition on H:
H＝UΣV T (5)
6) Calculating rotation matrix and translation amount
And 2, obtaining external parameter calibration of the IMU sensor, wherein the external parameter calibration comprises the following specific steps of:
the measurement model of the IMU sensor is as follows:
B a (t) is the acceleration of the accelerometer in the inertial coordinate system,the gyroscope measures the rotation angular velocity; b a (t) and b g (t) time-varying accelerometer bias and gyroscope bias; n is n a (t) and n g (t) accelerometer and gyroscope white noise over time; g w A gravity vector in world coordinates; a, a w (t) acceleration of the world coordinate system over time; />Angular velocity at the carrier coordinates; and WB is the conversion of IMU coordinate system B to world coordinate system W; b, W is a carrier coordinate system B and a world coordinate system W;
assume that the last second of IMU sensor bias isThe sum integral delta R of the IMU sensor measurement between the moment i and the moment j is transformed from the new value b to the new value b under the influence of the small disturbance delta b i,j ，Δυ i,j ，Δp i,j Rotation information R, position information p, velocity information v and offset b under IMU i . The estimated amount of pre-integration with respect to bias is updated with a first order approximation formula based on the measurement model:
deriving state information of the next key frame according to the above formula:
a jacobian matrix biased by the rotation increment relative to the gyroscope; />A jacobian matrix of velocity delta versus gyroscope bias and accelerometer; />And->Jacobian matrix of displacement delta relative to gyroscope bias and acceleration bias; then the gyroscope bias estimate:
the gravity vector and accelerometer bias are solved by singular value decomposition. All poses include rotation angle R, velocity V and position P. And obtaining actual IMU measurement with the IMU moving track of t+deltat in deltat time under the IMU initial pose coordinate system.
And 3, calibrating external parameters of the catadioptric camera-IMU, wherein the method comprises the following specific steps of:
and according to the two steps, respectively starting from the measurement models of the catadioptric panoramic camera and the IMU sensor, analyzing the pose of the two sensors. The following relation is satisfied between the refraction and reflection panoramic camera coordinate system and the IMU coordinate system:
T wb ＝T wc .T cb (12)
expanding the above to obtain the relation between the rotation angle and the translation amount between the camera coordinate system and the IMU coordinate system: and obtaining the pose relation between the catadioptric panoramic camera and the IMU sensor.
R wb ＝R wc .R cb (14)
P wb ＝R wc .P cb +s.P wc (15)
And then, for constructing the re-projection error and the pose estimation value between the camera and the IMU, adopting a least square method to associate the set S so as to iterate external parameters between the camera and the IMU and obtain a final external parameter standard number.
Algebraic function E of reprojection error term visual (k, j) performing an optimal estimation of the state variable Xc of the camera:
X c ＝{R 0 ,p 0 ,R 1 ,p 1 ,...,R n ,p n,w X 0,w X 1 ,..., w X m } (16)
w X k three-dimensional space points under a world coordinate system; x is x k Pixel plane coordinates in the pixel plane; simultaneous construction of the cost function E of an IMU sensor IMU (k, j) and for state X by nonlinear optimization i And (3) estimating:
and (5) carrying out joint optimization according to the two sensor error equations to finally obtain the final external parameters.
External parameter calibration experiment of refraction and reflection camera and IMU sensor:
the Kalibr toolbox is adopted, and a checkerboard calibration plate is selected, and the specific operation is as follows:
1. knowing parameters of the calibration plate, shooting a panoramic picture, carrying out gray processing, and identifying the calibration plate in each frame of image;
2. extracting corner points by using Harris operator according to the characteristic area where the selected calibration plate is positioned, obtaining internal parameters according to calculation, and using EPNE algorithm to experiment calculation of external parameters;
3. the refraction and reflection camera and the IMU sensor are regarded as a whole, the camera is moved, and images in the movement process are recorded into a bag file;
4. using the kalibr toolbox, executing a kalibe_calirate command, and respectively importing camera internal parameters and IMU noise parameters (as known), so as to obtain a rotation matrix between the camera and the IMU and internal parameters of the gyroscope;
5. after the rotation matrix is determined, the camera position and the IMU pre-integration position are continuously optimized to obtain the displacement part in the external parameter, and then the external parameter calibration results of the refraction and reflection camera and the IMU sensor are obtained.
Claims (2)
1. A method for calibrating external parameters of a refraction and reflection panoramic camera and an IMU sensor comprises the following steps:
step 1, obtaining external parameters of a refraction and reflection panoramic camera: according to the internal parameters of the refraction and reflection camera, calculating the external parameters through an EPNP algorithm, establishing coordinates of constraint calculation control points under a camera coordinate system by using N three-dimensional points, and reducing the influence of real characteristic points on the estimation error of the pose of the camera in this way to obtain the external parameters of the refraction and reflection panoramic camera;
step 2, obtaining external parameters of the IMU sensor: carrying out pre-integration processing on the obtained IMU data by obtaining the measurement data of the IMU, deducing the state information of the next key frame according to a measurement model and an estimated quantity of the pre-integration on bias, and solving the external parameter data of the IMU by singular value decomposition;
step 3, calibrating external parameters of the refraction and reflection camera-IMU: according to the measurement models of the refraction and reflection panoramic camera and the IMU sensor, the relation between the refraction and reflection panoramic camera coordinate system and the IMU coordinate system is found, the construction of the re-projection error and the pose estimation value between the camera and the IMU are carried out, the external parameters between the refraction and reflection camera and the IMU are optimized, the final external parameter standard number is obtained, and the refraction and reflection panoramic camera and the IMU accurately calibrate the external parameters;
and 3, calibrating external parameters of the catadioptric camera-IMU, wherein the method comprises the following specific steps of:
according to the steps 1 and 2, respectively starting from the measurement models of the catadioptric panoramic camera and the IMU sensor, analyzing the pose of the two sensors; the following relation is satisfied between the refraction and reflection panoramic camera coordinate system and the IMU coordinate system:
T wb ＝T wc .T cb (1)
expanding the above to obtain the relation between the rotation angle and the translation amount between the camera coordinate system and the IMU coordinate system: obtaining the pose relation between the refraction and reflection panoramic camera and the IMU sensor;
R wb ＝R wc .R cb (3)
P wb ＝R wc .P cb +s.P wc (4)
then, for constructing the re-projection error and the pose estimation value between the camera and the IMU, adopting a least square method to associate the set S so as to iterate external parameters between the camera and the IMU and obtain a final external parameter standard number;
reprojection error termAlgebraic function E visual (k, j) performing an optimal estimation of the state variable Xc of the camera:
X c ＝{R 0 ,p 0 ,R 1 ,p 1 ,...,R n ,p n,w X 0,w X 1 ,..., w X m } (5)
w X k three-dimensional space points under a world coordinate system; x is x k Pixel plane coordinates in the pixel plane; simultaneous construction of the cost function E of an IMU sensor IMU (k, j) and for state X by nonlinear optimization i And (3) estimating:
and (5) carrying out joint optimization according to the two sensor error equations to finally obtain the final external parameters.
2. The method for calibrating the external parameters of the catadioptric panoramic camera and the IMU sensor according to claim 1, wherein the step 1 is to obtain the calibration of the catadioptric panoramic camera, and the specific steps are as follows:
1-1, preparing a checkerboard calibration plate with known size;
1-2, fixing a calibration plate, and acquiring images of different positions and angles of the calibration plate in the images by using a mobile catadioptric panoramic camera;
1-3, extracting angular point information calibrated by a checkerboard in each image through a calibration algorithm, and calculating external parameters of the catadioptric camera by using an EPNP algorithm according to camera internal parameter results of prior information;
the EPNP algorithm comprises the following steps:
1) Establishing a world coordinate system, wherein reference points of the camera coordinate system are respectivelyAnd->And the control points under the two coordinate systems are respectively +.>And->
2) The coordinates of the reference point are expressed by EPNP algorithm as a weighted sum of the coordinates of the control points:and the same relationship still exists under the camera coordinate system:
selecting the center of gravity of the three-dimensional reference point as a first control point:
3) Calculation ofCenter of gravity->Sum matrix A
4) Calculation ofAnd center of gravity->Sum matrix B
5) Calculate h=b -1 A, carrying out singular value decomposition on H:
H＝UΣV T (15)
6) Calculating rotation matrix and translation amount
And 2, obtaining external parameter calibration of the IMU sensor, wherein the external parameter calibration comprises the following specific steps of:
the measurement model of the IMU sensor is as follows:
B a (t) is the acceleration of the accelerometer in the inertial coordinate system, B w* wb (t) gyroscopic measurementMeasuring the rotation angular velocity; b a (t) and b g (t) time-varying accelerometer bias and gyroscope bias; n is n a (t) and n g (t) accelerometer and gyroscope white noise over time; g w A gravity vector in world coordinates; a, a w (t) acceleration of the world coordinate system over time; B w wb (t) angular velocity at the carrier coordinates; and WB is the conversion of IMU coordinate system B to world coordinate system W; b, W is a carrier coordinate system B and a world coordinate system W;
assume that the last second of IMU sensor bias isThe sum integral delta R of the IMU sensor measurement between the moment i and the moment j is transformed from the new value b to the new value b under the influence of the small disturbance delta b i,j ，Δv i,j ，Δp i,j Rotation information R, position information p, velocity information v and offset b under IMU i The method comprises the steps of carrying out a first treatment on the surface of the The estimated amount of pre-integration with respect to bias is updated with a first order approximation formula based on the measurement model:
deriving state information of the next key frame according to the above formula:
a jacobian matrix biased by the rotation increment relative to the gyroscope; />A jacobian matrix of velocity delta versus gyroscope bias and accelerometer; />And->Jacobian matrix of displacement delta relative to gyroscope bias and acceleration bias; then the gyroscope bias estimate:
solving a gravity vector and accelerometer bias through singular value decomposition; all the gestures comprise a rotation angle R, a speed V and a position P; and obtaining actual IMU measurement with the IMU moving track of t+deltat in deltat time under the IMU initial pose coordinate system.
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| WO2020259106A1 (en) * | 2019-06-24 | 2020-12-30 | 深圳奥比中光科技有限公司 | Calibration method and device for relative attitudes of camera and inertial measurement unit |
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