CN115717901B - Inertial/visual odometer installation error estimation method based on filtering - Google Patents
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Abstract
The invention provides a filtering-based inertial/visual odometer installation error estimation method, which comprises the following steps: respectively calculating three axial positions calculated by a visual mileage calculation under a world coordinate system and three axial position information calculated by inertial navigation under a navigation coordinate system; converting the calculated position of the visual odometer under the world coordinate system into the navigation coordinate system, and acquiring the position information of the visual odometer under the navigation coordinate system; constructing an observed quantity according to the difference value between the position information calculated by inertial navigation under the navigation coordinate system and the position information of the visual odometer under the navigation coordinate system; calculating a measurement matrix; determining an error model; and estimating the systematic error based on the error model, the observed quantity and the measurement matrix by using a Kalman filtering method, and correcting the inertial navigation system error according to an error estimation result obtained by filtering. Compared with the prior art, the accuracy of the estimation result is greatly improved, and the installation error estimation method has convenience and universality.
Description
Technical Field
The invention relates to the technical field of visual navigation, in particular to a filtering-based inertial/visual odometer installation error estimation method.
Background
The visual odometer for carrying out carrier pose calculation according to the feature change between continuous images is an important field in visual navigation research. According to the characteristic of high output frequency of inertial navigation information and good complementarity with a visual odometer, the inertial/visual odometer navigation mode has higher precision and robustness. The installation error between the two is an important influence factor of the integrated navigation precision, and the reasonable and effective installation error estimation method has very wide application prospect in the fields of target tracking, path planning, robot obstacle avoidance and the like.
The traditional mechanical odometer is arranged on an unmanned vehicle, a robot crawler or wheels, and the forward mileage under a carrier system is calculated by measuring the rotation number of the wheels, so that the installation errors of pitching and heading directions exist between the traditional mechanical odometer and inertial navigation. The visual odometer can output displacement amounts in three directions, so that the traditional error model cannot be applied to inertial/visual odometer installation error estimation.
The current inertial/visual odometer usually adopts an off-line calibration method, namely, the calculation of the installation error between the inertial navigation and the camera is carried out by collecting a large number of calibration plate images with different rotation angles and inertial navigation data, and the method has limited precision and can not estimate the scale error of the visual odometer. Meanwhile, the method is inconvenient, a large amount of data needs to be acquired offline in advance for each calibration, and the method cannot be suitable for error calibration of a batch system.
Disclosure of Invention
The present invention aims to solve at least one of the problems in the prior art.
To this end, the invention provides a method for estimating inertial/visual odometer installation errors based on filtering.
The technical solution of the invention is as follows: the invention provides a filtering-based inertial/visual odometer installation error estimation method, which comprises the following steps:
Respectively calculating three axial positions calculated by a visual mileage calculation under a world coordinate system and three axial position information calculated by inertial navigation under a navigation coordinate system;
converting the calculated position of the visual odometer under the world coordinate system into the navigation coordinate system, and acquiring the position information of the visual odometer under the navigation coordinate system;
Constructing an observed quantity Z k according to the difference value between the position information calculated by inertial navigation under the navigation coordinate system and the position information of the visual odometer under the navigation coordinate system; calculating a measurement matrix H k;
Determining an error model;
and estimating the systematic error based on the error model, the observed quantity Z k and the measurement matrix H k by using a Kalman filtering method, and correcting the inertial navigation system error according to the error estimation result obtained by filtering.
Further, the position information of the visual odometer under the navigation coordinate system is obtained through the following steps
Wherein,For the gesture transfer matrix from the geographic coordinate system to the navigation coordinate system,/>For the transformation matrix of the camera coordinate system to the geographic coordinate system,/>Gesture transfer matrix between world coordinate system and camera coordinate system established for successful initialization momentAnd calculating the calculated position for the visual mileage under the world coordinate system.
Further, the observed quantity Z k is constructed by the following difference between the position information calculated by inertial navigation in the navigation coordinate system and the position information of the visual odometer in the navigation coordinate system:
where p n represents the true value of the inertial navigation position, δp n represents the position error of the inertial navigation, True value representing position of visual odometer under navigation coordinate system,/>Representing the position error of the visual odometer.
Further, a measurement matrix H k is constructed based on the error equation coefficient relationship by:
Further, determining the error model includes:
determining an inertial navigation combined system state variable X k;
And determining an inertial navigation combined system state equation.
Further, a system state variable X k of the formula:
wherein:
δVn＝(δVN,δVU,δVE)
δPn＝(δL,δH,δλ)
εb＝(εbx,εby,εbz)
δV N,δVU,δVE represents the north, the sky and the east speed errors of the inertial navigation system under the navigation coordinate system respectively;
δL, δH, δλ respectively represent latitude, altitude, and longitude errors of the inertial navigation system under a navigation coordinate system;
Respectively representing north, sky and east misalignment angles of an inertial navigation system under a navigation coordinate system;
Epsilon bx,εby,εbz respectively represents gyro drift of the inertial navigation system in the X, Y, Z direction under a carrier coordinate system;
the accelerometer zero offset of the inertial navigation system in the X, Y, Z direction under the carrier coordinate system is respectively represented;
Respectively representing the position errors of the visual odometer in the north, the sky and the east directions of a navigation coordinate system;
δαγ, δα θ represents the mounting error angle between the visual odometer and the inertial navigation in the carrier coordinate system, respectively;
δS represents the scale error of the visual odometer in the navigation coordinate system;
further, the position acquisition of the visual odometer is obtained by
Wherein,
δαγ、/>Delta alpha θ is the roll error angle, heading error offset angle and pitch error angle existing around the inertial navigation longitudinal axis x b, the vertical axis y b and the horizontal axis z b respectively; δs is the dimensional error of the visual odometer, For the position information of the visual odometer in the camera coordinate system,/>Representing the position output of the visual odometer in the x, y and z directions under the camera coordinate system respectively.
Further, the equation of state is obtained using the following formula:
Wherein, Is a state variable,/>For white noise vectors, F (t) is the state transition matrix.
Further, the state transition matrix is obtained by adopting the following formula:
wherein, the submatrix F 1→F10 is established by the strapdown inertial navigation error equation, and the submatrix F O1→FO2 is established by the visual odometer error equation as follows:
compared with the prior art, the invention has the beneficial effects that:
the position result difference of the inertial navigation system and the visual odometer under the navigation coordinate system is used as an observed quantity, an error model is built aiming at all error sources of the two navigation modes, and the accurate estimation of the inertial navigation system error, the installation error between the visual odometer and the inertial navigation and the dimensional error of the visual odometer are realized by a Kalman filtering-based method, and are corrected in real time. Compared with the prior art, the accuracy of the estimation result is greatly improved, and the installation error estimation method has convenience and universality.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
Fig. 1 shows a flow chart of an inertial/odometer integrated navigation method based on velocity integration according to an embodiment of the invention.
Detailed Description
Specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. In the following description, for purposes of explanation and not limitation, specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details.
It should be noted here that, in order to avoid obscuring the present invention due to unnecessary details, only the device structures and/or processing steps closely related to the solution according to the present invention are shown in the drawings, while other details not greatly related to the present invention are omitted.
Fig. 1 shows a flowchart of a method for estimating an installation error of an inertial/visual odometer based on filtering according to an embodiment of the invention.
As shown in fig. 1, a method for estimating an inertial/visual odometer installation error based on filtering is provided, wherein the method may include:
step one, respectively calculating three axial positions calculated by a visual odometer under a world coordinate system and three axial position information calculated by inertial navigation under a navigation coordinate system;
Step two, converting the calculated position of the visual odometer under the world coordinate system into the navigation coordinate system, and acquiring the position information of the visual odometer under the navigation coordinate system;
Thirdly, constructing an observed quantity Z k according to the difference value between the position information calculated by inertial navigation under the navigation coordinate system and the position information of the visual odometer under the navigation coordinate system; calculating a measurement matrix H k;
Determining an error model;
And fifthly, estimating the systematic error based on the error model, the observed quantity Z k and the measurement matrix H k by using a Kalman filtering method, and correcting the inertial navigation system error according to the error estimation result obtained by filtering.
Therefore, compared with the traditional method for calibrating the offline collected data, the method adopts an online estimation method. An error model is built through an inertial navigation system and an error source of a visual odometer, the installation error is accurately estimated and corrected in real time based on a Kalman filtering algorithm, and compared with the prior art, the method has the advantages that the estimation accuracy of the installation error is improved, the method is more convenient, and the method is suitable for calibrating mass equipment.
For example, the invention is implemented in the following coordinate system:
a) Carrier coordinate system (b system)
A "front-up-right" coordinate system is used. The origin of coordinates O selects the mass center of the carrier, and the axis OX b is forward along the longitudinal axis direction of the inertial navigation system; the OY b axis is positive upwards along the vertical axis direction of the inertial navigation system; the OZ b axis is positive to the right along the transverse axis direction of the inertial navigation system.
B) Geographic coordinate system (g system)
The "north-day-east" coordinate system is used. The origin of coordinates selects the centroid of the inertial navigation system, and the axis OX g is in the north-south direction of the geography, and the north is positive; the OY g axis is along the geographic direction of the day, and the mean day is positive; the OZ g axis is along the east-west direction, and the east direction is positive;
c) Navigation coordinate system (n system)
And selecting the geographic coordinate system as a navigation coordinate system.
D) Camera coordinate system (c system)
A "front-up-right" coordinate system is used. The origin of coordinates O selects the mass center of the camera, and the axis OX c is forward positive along the longitudinal axis direction of the camera; the OY c axis is positive upwards along the vertical axis direction of the camera; the OZ c axis is positive to the right along the camera's lateral axis.
E) World coordinate system (w system)
And taking a camera coordinate system at the moment of successful initialization of the visual odometer as a world coordinate system.
According to one embodiment of the invention, the b-system to n-system attitude transfer matrix is calculated through inertial navigationCalculated/>, of the mounting angle between inertial navigation and cameraPosture matrix/>, between world coordinate system and camera coordinate system, established at successful moment of initializationConverting the position information under the world coordinate system calculated by the visual odometer into a navigation coordinate system:
next, calculating a position error δp between the inertial navigation system and the visual odometer under the navigation coordinates:
Wherein p n, And the position true values of the inertial navigation system and the visual odometer are respectively represented, so that the inertial navigation system and the visual odometer are equal, namely the position difference between the inertial navigation system and the visual odometer is the difference of the position errors of the inertial navigation system and the visual odometer.
Thus, an observed quantity Z k is constructed from the difference in position between the inertial navigation system and the visual odometer:
The measurement matrix H k of the integrated navigation system is constructed by:
According to one embodiment of the invention, a state variable X k of the integrated navigation system is constructed:
wherein:
δVn＝(δVN,δVU,δVE)
δPn＝(δL,δH,δλ)
εb＝(εbx,εby,εbz)
δV N,δVU,δVE represents the north, the sky and the east speed errors of the inertial navigation system under the navigation coordinate system respectively;
δL, δH, δλ respectively represent latitude, altitude, and longitude errors of the inertial navigation system under a navigation coordinate system;
Respectively representing north, sky and east misalignment angles of an inertial navigation system under a navigation coordinate system;
Epsilon bx,εby,εbz respectively represents gyro drift of the inertial navigation system in the X, Y, Z direction under a carrier coordinate system;
the accelerometer zero offset of the inertial navigation system in the X, Y, Z direction under the carrier coordinate system is respectively represented;
Respectively representing the position errors of the visual odometer in the north, the sky and the east directions of a navigation coordinate system;
δαγ, δα θ represents the mounting error angle between the visual odometer and the inertial navigation in the carrier coordinate system, respectively;
δS represents the scale error of the visual odometer in the navigation coordinate system;
according to one embodiment of the invention, the Kalman filter model continuous state equation for the combined inertial/visual odometer system is as follows:
Wherein, Is a 19-dimensional state quantity,/>For white noise vectors, F (t) is the state transition matrix.
Wherein F (t) is calculated by an inertial navigation error equation and a visual odometer error equation, and the expression form is as follows:
wherein: the submatrix F 1→F10 is built from the strapdown inertial navigation error equation:
wherein:
Representing a gesture matrix of the inertial navigation system from the carrier coordinate system to the navigation coordinate system;
representing a gesture matrix of the inertial navigation system from a navigation coordinate system to a carrier coordinate system;
v n、Vu、Ve represents the north, the day and the east speeds of an n system calculated by an inertial navigation system;
l, h, lambda represent latitude, altitude and longitude under n system calculated by inertial navigation system;
The specific force output values of the inertial navigation system in the three directions of the b system X, Y, Z are converted into the representative values of the north, the sky and the east of the b system;
r M、RN respectively represents the radius of curvature of a meridian and a mortise circle at the position of inertial navigation at the current moment;
omega ie represents the rotation angular rate of the earth.
The submatrix F O1→FO2 is built up from the visual odometer error equation, and is specifically as follows:
the dimensional error exists in the position output of the visual odometer under the w system, and the installation error is introduced when the visual odometer is converted into the n system, for example, the following steps are included:
Visual odometer measurement Actual displacement size/>The relationship between the theoretical displacement size p O is:
So there are:
the displacement output of the visual odometer in actual measurement is expressed as:
In a practical system, it is difficult to ensure that the carrier coordinate system (b-system) and the camera coordinate system (c-system) completely coincide with each other when the inertial/visual odometer system camera and inertial navigation are installed, and it is assumed that a small amount of installation error angle exists from the c-system to the b-system, namely a roll error angle delta alpha γ and a heading error offset angle exist around the longitudinal axis x b, the vertical axis y b and the transverse axis z b of the inertial navigation respectively And pitch error angle δα θ, noted as:
The installation error matrix between the c system and the b system is recorded as The position error of the visual odometer in the navigation coordinate system is expressed as:
wherein, (δα) ×is an antisymmetric matrix of the installation error angle vector:
the product of δα and δs is a small high order, negligible.
X is the position output of the visual odometer under the camera coordinate system:
thus in the state transition matrix F (t):
after the error model is established, state estimation is performed based on a Kalman filtering method. The observability of the integrated navigation system is related to the motion state of the carrier, and the motion trail of the carrier needs to be satisfied so that the installation error and the scale error can be observed quickly.
According to an embodiment of the invention: the carrier can realize that the installation error and the scale error can be observed under the maneuver with the forward direction, the lateral speed and the vector direction change, and can simulate the S-shaped track motion in terms of the simplicity of the simulation implementation mode. It should be understood by those skilled in the art that the above-described motion profile design may be modified according to practical situations, and the present invention is not limited thereto.
It can be seen that the existing mechanical odometer installation error calibration only considers two angles, the visual odometer outputs three direction positions, the installation error angle of the pitching direction is related, the existing method is mostly used for off-line calibration of collected data, and the method has no convenience for large unmanned aerial vehicles and other devices. The invention realizes the on-line estimation of the installation error and the scale error by means of error modeling and Kalman filtering, greatly improves the accuracy of the estimation result, and has convenience and universality.
Features that are described and/or illustrated above with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments and/or in combination with or instead of the features of the other embodiments.
It should be emphasized that the term "comprises/comprising" when used herein is taken to specify the presence of stated features, integers, steps or components but does not preclude the presence or addition of one or more other features, integers, steps, components or groups thereof.
The method of the invention can be realized by hardware or by combining hardware with software. The present invention relates to a computer readable program which, when executed by a logic means, enables the logic means to carry out the apparatus or constituent means described above, or enables the logic means to carry out the various methods or steps described above. The present invention also relates to a storage medium such as a hard disk, a magnetic disk, an optical disk, a DVD, a flash memory, or the like for storing the above program.
The many features and advantages of the embodiments are apparent from the detailed specification, and thus, it is intended by the appended claims to cover all such features and advantages of the embodiments which fall within the true spirit and scope thereof. Further, since numerous modifications and changes will readily occur to those skilled in the art, it is not desired to limit the embodiments of the invention to the exact construction and operation illustrated and described, and accordingly, all suitable modifications and equivalents may be resorted to, falling within the scope thereof.
The invention is not described in detail in a manner known to those skilled in the art.
Claims (5)
1. A method of inertial/visual odometer installation error estimation based on filtering, the method comprising:
Respectively calculating three axial positions calculated by a visual mileage calculation under a world coordinate system and three axial position information calculated by inertial navigation under a navigation coordinate system;
converting the calculated position of the visual odometer under the world coordinate system into the navigation coordinate system, and acquiring the position information of the visual odometer under the navigation coordinate system;
Constructing an observed quantity Z k according to the difference value between the position information calculated by inertial navigation under the navigation coordinate system and the position information of the visual odometer under the navigation coordinate system; calculating a measurement matrix H k;
Determining an error model;
Estimating a systematic error based on the error model, the observed quantity Z k and the measurement matrix H k by using a Kalman filtering method, and correcting the inertial navigation system error according to an error estimation result obtained by filtering;
wherein:
Constructing an observed quantity Z k according to the difference value between the position information calculated by inertial navigation under the navigation coordinate system and the position information of the visual odometer under the navigation coordinate system by the following steps:
Where p n represents the true value of the inertial navigation position, δp n represents the position error of the inertial navigation, True value representing position of visual odometer under navigation coordinate system,/>Representing a position error of the visual odometer;
Position error of visual odometer is obtained by
Wherein,
δαγ、/>Delta alpha θ is the roll error angle, heading error offset angle and pitch error angle existing around the inertial navigation longitudinal axis x b, the vertical axis y b and the horizontal axis z b respectively; δS is the scale error of the visual odometer,/> For the position information of the visual odometer in the camera coordinate system,/>Respectively representing the position output of the visual odometer in the x, y and z directions under the camera coordinate system,/>The gesture transfer matrix from the geographic coordinate system to the navigation coordinate system;
The determining an error model includes:
determining a state variable of the inertial navigation combined system;
determining a state equation of the inertial navigation combined system;
The following system state variables are used:
wherein:
δVn＝(δVN,δVU,δVE)
δPn＝(δL,δH,δλ)
εb＝(εbx,εby,εbz)
δV N,δVU,δVE represents the north, the sky and the east speed errors of the inertial navigation system under the navigation coordinate system respectively;
δL, δH, δλ respectively represent latitude, altitude, and longitude errors of the inertial navigation system under a navigation coordinate system;
Respectively representing north, sky and east misalignment angles of an inertial navigation system under a navigation coordinate system;
Epsilon bx,εby,εbz respectively represents gyro drift of the inertial navigation system in the X, Y, Z direction under a carrier coordinate system;
the accelerometer zero offset of the inertial navigation system in the X, Y, Z direction under the carrier coordinate system is respectively represented;
Respectively representing the position errors of the visual odometer in the north, the sky and the east directions of a navigation coordinate system;
respectively representing the installation error angle between the visual odometer and the inertial navigation under the carrier coordinate system;
δs represents the dimensional error of the visual odometer in the navigational coordinate system.
2. The method of claim 1, wherein the true value of the position of the visual odometer in the navigational coordinate system is obtained by
Wherein,For the gesture transfer matrix from the geographic coordinate system to the navigation coordinate system,/>For the transformation matrix of the camera coordinate system to the geographic coordinate system,/>For initializing the pose transfer matrix between the world coordinate system and the camera coordinate system established at the successful moment,And calculating the calculated position for the visual mileage under the world coordinate system.
3. The method of claim 1, wherein the measurement matrix H k is constructed based on the error equation coefficient relationship by:
4. the method of claim 1, wherein the equation of state is obtained using the following formula:
Wherein, Is a state variable,/>For white noise vectors, F (t) is the state transition matrix.
5. The method of claim 4, wherein the state transition matrix is obtained using the following equation:
Wherein, the submatrix F 1→F10 is established by a strapdown inertial navigation error equation, and the F O1→FO2 is established by a visual odometer error equation, as follows:
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