CN109360245A - The external parameters calibration method of automatic driving vehicle multicamera system - Google Patents
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Abstract
A kind of external parameters calibration method of automatic driving vehicle multicamera system, comprising: at least two relaying phase units are set between the adjacent camera to be calibrated of vehicle；Synchronize the camera to be calibrated and relaying camera；Around the mobile scaling board of the vehicle, the scaling board is made successively to pass through all cameras；Start the camera to be calibrated and relaying camera, mobile scaling board is shot；Each characteristic pattern is detected in each magazine 2D pixel coordinate；Outer parameter estimation.Redundancy posture figure optimisation strategy is established present invention introduces relaying camera and based on this, overcome the problem of accumulating between conventional method camera due to the outer parameter error that distance generates, it is satisfied the accurate outer parameter Estimation of SLAM system requirements, and it does not need to establish the calibration structure proportional to vehicle platform itself, the design and manufacturing expense of the marker that saves space, saves.
Description
Technical field
The invention belongs to multicamera system technical field more particularly to a kind of outer ginsengs of automatic driving vehicle multicamera system
Number scaling method.
Background technique
The technology one of most potential as the world today, it is unmanned to refer to automobile the case where not needing manual operation
Under, the sensor being equipped with by itself perceives ambient enviroment and completes navigation task.PricewaterhorseCoopers predicts unmanned technology
It is universal that whole traffic accident will be made to reduce 90 percent；The prediction of Bi Mawei Cera, unmanned technology will drive
Productivity and energy efficiency will be improved, and will appear new business model.
Pilotless automobile is commonly equipped with camera, Inertial Measurement Unit (IMU), laser radar and global positioning system
(GPS) sensors such as.Wherein, the external information that camera can perceive is the abundantest, color, structure, texture including scenery with
And some semantic informations (such as: road, pedestrian, traffic mark).It can only be observed compared to human driver in synchronization
The traffic condition of a direction, unmanned technology aim at 360 degrees omnidirection without dead angle to the environment of vehicle body surrounding into
Row perception.Since the field angle of single camera is limited, omnidirectional imaging system is formed usually using multiple cameras.Navigation task is usual
Be described it is required that the information of polyphaser is transformed under the same coordinate system, it is therefore desirable to the outer parameter between polyphaser into
Rower is fixed.For small vehicle, manufacturer or developer can pass through Global localization by building static marker (scaling board)
Obtain the outer parameter between more mesh cameras.However, looking around nothing taking into account for oversize vehicle (heavy truck as having trailer)
Dead angle and for cost viewpoint under the premise of, it will usually the camera of limited quantity is mounted on vehicle body in an orbiting manner.This
When will appear two problems: (1) very big interval is had between camera and camera；(2) do not have between certain cameras (or only very
Small) visual field overlay region.These actual conditions make the external parameters calibration for more mesh cameras become extremely difficult.Simply according to
Very big requirement can be had to place by bringing up to the calibration strategy stated and look around more mesh cameras for small vehicle.In addition, most of at present existing
Have and is developed for viewing system external parameters calibration technology primarily directed in 360 degree of birds-eye view tasks for generating high quality, and
The superiority and inferiority of final image splicing result is usually determined by visual perception.In fact, SLAM system is for the outer of more mesh camera systems
The requirement of parameter calibration precision is significantly larger than the system for the purpose of image mosaic.
Existing scaling scheme and its advantage and disadvantage are as follows:
1, patent of invention title: a kind of scaling method of panoramic view vision auxiliary parking system, publication number:
CN101425181B:
A kind of scaling method of panoramic view vision auxiliary parking system of the disclosure of the invention, with being mounted on four of automobile surrounding
The image that wide-angle fish eye camera generates generates the virtual birds-eye view of a certain height of automobile top.Wherein, each camera relative to
Virtually getting a bird's eye view the positional relationship of camera is by being determined based on the calculating of the homography conversion matrix of ground level.Due to virtual bird
Look down from a height the position of camera is to estimate to obtain by low precision measure, therefore, establishes more mesh phases on the basis of this virtually gets a bird's eye view camera
Although the spatial relation between machine meets the requirement for completing seamless image splicing, it is unable to satisfy based on more mesh cameras
The design requirement of SLAM system.
2, patent of invention title: combined optimization method and device in dynamic calibration system, dynamic calibration system, it is open
Number: CN105844624A:
The invention provides the combined optimization methods and device in a kind of dynamic calibration system, dynamic calibration system.Wherein,
External parameters calibration step between camera needs artificially to build static demarcating object known to several groups relative tertiary location.Calibration task
It needs the vehicle equipped with camera to pass through above-mentioned static mark object along the track of design, nominal data is completed in dynamic process
Acquisition.Compared with the method in 101425181 B of patent CN, the Camera extrinsic number that this method obtains is more accurate, but for
The requirement for demarcating scene is excessively stringent, is not easy to be generalized to the calibration for looking around more mesh camera systems under oversize vehicle platform and appoints
Business.
3, a kind of patent of invention title: load truck traveling detection of obstacles and track side based on binocular fish-eye camera
Method, publication number: CN105678787A:
A kind of load truck traveling detection of obstacles and tracking based on binocular fish-eye camera of the disclosure of the invention,
Belong to vehicular traffic active safety technologies field.Rear of vehicle barrier when in order to detect load truck reversing, pacifies in tailstock portion
Equipped with one group of binocular fisheye camera.There is figure to need to measure the depth of field, the visual field of two fisheye cameras there are enough overlapped views.It is this
Configuration simplifies the external parameters calibration of binocular camera, and the camera calibration tool box of available existing open source is completed.However, right
More mesh camera systems are looked around in large-scale load truck, are not available the above method, the reason is that the distance between camera (baseline)
Farther out, and there is no (or only very little) visual field overlay region.
Summary of the invention
Based on this, in view of the above technical problems, a kind of external parameters calibration side of automatic driving vehicle multicamera system is provided
Method.
In order to solve the above technical problems, the present invention adopts the following technical scheme:
A kind of external parameters calibration method of automatic driving vehicle multicamera system, comprising:
110, at least two relaying phase unit of setting between the adjacent camera to be calibrated of vehicle, at least two relaying
Phase unit or so arrangement, each relaying phase unit include at least one relaying camera in above-below direction, are had between adjacent cameras
Zone of mutual visibility domain, the angle in the zone of mutual visibility domain are 50 ° -150 °；
120, the camera to be calibrated and relaying camera are synchronized；
130, around the mobile scaling board of the vehicle, the scaling board is made successively to pass through all cameras, the scaling board is just
Face, which faces the camera to be calibrated and relaying camera, the front, has multiple characteristic patterns of matrix arrangements；
140, start the camera to be calibrated and relaying camera, mobile scaling board is shot；
150, each characteristic pattern is detected in each magazine 2D pixel coordinate；
160, outer parameter estimation:
161, total view is carried out to the 2D pixel coordinate of the characteristic pattern in zone of mutual visibility domain and 3D world coordinates to be associated with, generation with
The posture figure that the absolute pose of camera is node, regards relationship altogether as side, absolute pose indicate that R is spin matrix, and t is by { R, t }
Translation vector；
162, it according to the quantity and maximum re-projection error for regarding characteristic pattern altogether, is generated by breadth-first search
Minimum spanning tree: select to regard altogether more than characteristic pattern quantity while as two nodes of connection while, if regarding characteristic pattern quantity phase altogether
Together, then select maximum re-projection error lesser while for two nodes of connection while；
163, the absolute pose of an optional camera node to be calibrated and minimum spanning tree in the minimum spanning tree
For the relative pose between all nodes on path as outer parameter sets to be calibrated, relative pose passes through formula Tf Wn=
Tf WmTf mnIt obtains, wherein Tf WnAbsolute pose for n camera at the f moment, Tf WmAbsolute pose for m camera at the f moment,
Tf mnRelative pose for n camera relative to m camera；
164, by the way that using re-projection error as the nonlinear least square method of energy, the optimization for obtaining outer parameter to be calibrated is estimated
Evaluation.
The relaying camera is fixed on the vehicle, or can left-right rotation adjust be fixed on tripod.
By sending synchronizing clock signals, the camera to be calibrated and relaying camera are synchronized.
It is mobile around the vehicle by man-hour manually hand-held scaling board.
Present invention introduces relaying camera and redundancy posture figure optimisation strategy is established based on this, overcome conventional method camera it
Between due to distance generate outer parameter error accumulation the problem of, be satisfied the accurate outer parameter Estimation of SLAM system requirements,
And it does not need to establish the calibration structure proportional to vehicle platform itself, the design and manufacture of the marker that saves space, saves
Expense.
Detailed description of the invention
The present invention is described in detail with reference to the accompanying drawings and detailed description:
Fig. 1 is the principle of the present invention schematic diagram；
Fig. 2 is the structural schematic diagram of relaying phase unit of the invention；
Fig. 3 is the schematic top plan view of relaying phase unit of the invention；
Fig. 4 is posture figure and minimum spanning tree schematic diagram of the invention；
Fig. 5 is the schematic diagram of the scaling board characteristic pattern used in the present invention.
Specific embodiment
A kind of external parameters calibration method of automatic driving vehicle multicamera system, comprising:
110, as shown in Figure 1, at least two relaying phase units 30 are arranged between the adjacent camera 21 to be calibrated of vehicle 20,
It relays phase unit 30 or so to arrange, each relaying phase unit 30 includes at least one relaying camera 31, adjacent phase in above-below direction
There is zone of mutual visibility domain, zone of mutual visibility domain is sector, and angle is 50 ° -150 ° between machine.
It, can be by being mechanically fixed or magnetic force when the quantity of relaying camera 31 is 1 in each relaying phase unit 30
The modes such as absorption are fixed on vehicle 20, as shown in FIG. 2 and 3, relay the quantity of camera 31 if it is 2 or 2 or more
When, it can be arranged on tripod 33, relaying camera can be carried out by camera fixed frame left by about 32 camera fixed frame
It turns right to move and adjust.
120, by sending synchronizing clock signals to each camera, synchronizing camera 21 to be calibrated and relaying camera 31, thus
It can be shot with identical frame per second.
130, around the mobile scaling board 40 of vehicle 20, make the scaling board 40 successively by all cameras, scaling board 40 is just
Face, which faces camera 21 to be calibrated and relaying camera 31, front, has multiple characteristic patterns of matrix arrangements, and the present embodiment is adopted
With AprilTags characteristic pattern, referring to Fig. 5.
In the present embodiment, mobile around vehicle 20 by man-hour manually hand-held scaling board 40, movement routine L is shown in Fig. 1.
140, start camera 21 to be calibrated and relaying camera 31, mobile scaling board 40 is shot.
150, each characteristic pattern is detected in each magazine 2D pixel coordinate xk=(uk,vk)T。
160, outer parameter estimation:
161, total view is carried out to the 2D pixel coordinate of the characteristic pattern in zone of mutual visibility domain and 3D world coordinates to be associated with, generation with
The posture figure that the absolute pose of camera is node, regards relationship altogether as side.
Wherein, have in two adjacent cameras in some characteristic pattern in the zone of mutual visibility domain of two cameras different
2D pixel coordinate is exactly altogether to close to the 3D world coordinates of this feature pattern with corresponding two 2D pixel coordinates depending on association
Connection.
Refer to that two cameras have zone of mutual visibility domain depending on relationship altogether, then corresponding two nodes are connected to by side.
162, it according to the quantity and maximum re-projection error for regarding characteristic pattern altogether, is generated by breadth-first search
Minimum spanning tree: select to regard altogether more than characteristic pattern quantity while as two nodes of connection while, if regarding characteristic pattern quantity phase altogether
Together, then select maximum re-projection error lesser while for two nodes of connection while.
In computer vision, often uses re-projection error (Reprojection error): being the picture of 3D pattern
The position that plain coordinate (projected position that camera observes) and 3D pattern are projected according to the pose that the camera is currently estimated
The error to compare is set, as the 3d coordinate of some characteristic pattern is sat by the 2d pixel that the absolute pose of A camera is converted into
Error between mark and the 2d pixel coordinate observed by A camera.
Wherein, absolute pose indicates that R is spin matrix, and t is translation vector by { R, t }.
The initial value of the absolute pose of each camera is obtained by PnP (Perspective-n-Point) algorithm, after association
3D world coordinates and 2D pixel coordinate be PnP algorithm input parameter, 3D world coordinate system is defined on scaling board 40 by we
An angle on, scaling board 40 relative to camera motion be equivalent to camera relative to scaling board 40 move, due on scaling board 40
The characteristic pattern size of spraying is it is known that therefore known to the 3D world coordinates of characteristic pattern.
163, the absolute pose of an optional camera node to be calibrated and minimum spanning tree path in minimum spanning tree
On all nodes between relative pose as outer parameter sets to be calibrated, relative pose passes through formula Tf Wn=Tf WmTf mn?
It arrives, wherein Tf WnAbsolute pose for n camera at the f moment, Tf WmAbsolute pose for m camera at the f moment, Tf mnFor n phase
Relative pose of the machine relative to m camera.
164, by the way that using re-projection error as the nonlinear least square method of energy, the optimization for obtaining outer parameter to be calibrated is estimated
Evaluation.
When relaying phase unit 30 comprising multiple relaying cameras 31 arranged up and down, one can be constituted with camera 21 to be calibrated
Dense pose figure (least square problems of i.e. one Planar Mechanisms), is conducive to obtain more accurate estimated value.
As shown in figure 4, by taking two cameras to be calibrated as an example, two relaying phases are arranged in we between two cameras to be calibrated
Machine, in figure, 1,2 represent two cameras to be calibrated, and 3,4 represent two relaying cameras, and light side is the path of minimum spanning tree, lead to
It crosses energy function and estimation is optimized to outer parameter to be calibrated:
Wherein,Indicate re-projection error, xkThe 2D pixel coordinate of characteristic pattern is observed for camera,Pass through 2d pixel coordinate made of the absolute pose conversion of n camera for the 3d world coordinates of this feature pattern,Absolute pose for n camera at the f moment, XkFor the 3d world coordinates of this feature pattern, n is camera serial number, and k is characterized
The serial number of pattern.
Indicate outer parameter to be calibrated, the i.e. absolute pose of No. 1 camera at various moments, No. 3 cameras
Relative to the relative pose of No. 1 camera, relative pose of No. 4 cameras relative to No. 3 cameras, No. 4 cameras are relative to No. 2 cameras
Relative pose, the projected position coordinate x of characteristic point on the imagekIt indicates.
Present invention introduces relaying camera and redundancy posture figure optimisation strategy is established based on this, overcome conventional method camera it
Between due to distance generate outer parameter error accumulation the problem of, be satisfied the accurate outer parameter Estimation of SLAM system requirements,
And it does not need to establish the calibration structure proportional to vehicle platform itself, the design and manufacture of the marker that saves space, saves
Expense, suitable for the external parameters calibration for looking around more mesh camera systems on oversize vehicle platform.
But those of ordinary skill in the art it should be appreciated that more than embodiment be intended merely to illustrate this
Invention, and be not used as limitation of the invention, as long as in spirit of the invention, to embodiment described above
Variation, modification will all fall within the scope of claims of the present invention.
Claims (4)
1. a kind of external parameters calibration method of automatic driving vehicle multicamera system characterized by comprising
110, at least two relaying phase unit of setting between the adjacent camera to be calibrated of vehicle, at least two relayings camera
Group or so arrangement, each relaying phase unit include at least one relaying camera in above-below direction, have view altogether between adjacent cameras
Region, the angle in the zone of mutual visibility domain are 50 ° -150 °；
120, the camera to be calibrated and relaying camera are synchronized；
130, around the mobile scaling board of the vehicle, the scaling board is made successively to pass through all cameras, the positive face of the scaling board
There are multiple characteristic patterns of matrix arrangements towards the camera to be calibrated and relaying camera, the front；
140, start the camera to be calibrated and relaying camera, mobile scaling board is shot；
150, each characteristic pattern is detected in each magazine 2D pixel coordinate；
160, outer parameter estimation:
161, the 2D pixel coordinate of the characteristic pattern in zone of mutual visibility domain be total to depending on being associated with 3D world coordinates, be generated with camera
Absolute pose be node, the posture figure that regards relationship altogether as side, absolute pose indicates that R is spin matrix by { R, t }, and t is translation
Vector；
162, it according to the quantity and maximum re-projection error for regarding characteristic pattern altogether, is generated by breadth-first search minimum
Spanning tree: select to regard altogether more than characteristic pattern quantity while as two nodes of connection while, if view characteristic pattern quantity is identical altogether,
Select maximum re-projection error lesser while for connect two nodes while；
163, the absolute pose of an optional camera node to be calibrated and minimum spanning tree path in the minimum spanning tree
On all nodes between relative pose as outer parameter sets to be calibrated, relative pose passes through formula Tf Wn=Tf WmTf mn?
It arrives, wherein Tf WnAbsolute pose for n camera at the f moment, Tf WmAbsolute pose for m camera at the f moment, Tf mnFor n phase
Relative pose of the machine relative to m camera；
164, by obtaining the optimal estimating of outer parameter to be calibrated using re-projection error as the nonlinear least square method of energy
Value.
2. a kind of external parameters calibration method of automatic driving vehicle multicamera system according to claim 1, feature exist
In, the relaying camera is fixed on the vehicle, or can left-right rotation adjust be fixed on tripod.
3. a kind of external parameters calibration method of automatic driving vehicle multicamera system according to claim 1 or 2, feature
It is, by sending synchronizing clock signals, synchronizes the camera to be calibrated and relaying camera.
4. a kind of external parameters calibration method of automatic driving vehicle multicamera system according to claim 3, feature exist
In mobile around the vehicle by man-hour manually hand-held scaling board.
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