CN114152269B - On-site calibration method for installation parameters of wheel installation inertia measurement unit - Google Patents
On-site calibration method for installation parameters of wheel installation inertia measurement unit Download PDFInfo
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Abstract
The invention discloses a field calibration method for installation parameters of a wheel installation inertial measurement unit, which comprises two stages of coarse calibration and fine calibration; coarse calibration: and (3) in the horizontal road surface, the vehicle starts to be in a static state, the accelerometer output in the static state is collected, then the vehicle moves for a small distance along the straight line on the horizontal road surface, the gyro output in the straight line movement state is collected, and the coarse value of the shaft alignment error matrix is determined by the TRIAD algorithm. Fine calibration stage: the vehicle is enabled to freely run on a horizontal road surface from a standstill, a Kalman filter is established through the kinematic constraint of the vehicle and the output of the inertia measurement unit, IMU output is collected and substituted into an extended Kalman filter, and the eccentricity and the shaft alignment error accurate value of the inertia measurement unit relative to the wheels can be calculated through the Kalman filter, so that the pose of the vehicle can be estimated. The invention is beneficial to reducing the mounting difficulty and the use cost of the IMU of the wheel and improving the positioning precision of the vehicle based on the IMU of the wheel.
Description
Technical Field
The invention belongs to the technical field of inertial navigation, and particularly relates to a field calibration method for installation parameters of a wheel-mounted inertial measurement unit, which is used for improving the positioning accuracy and effect of a vehicle based on the wheel-mounted inertial measurement unit.
Background
Autopilot technology plays an increasingly important role, and vehicle autonomous positioning is a core function of an autopilot vehicle. The inertial navigation has the unique advantages of uninterrupted output information, no external interference and the like, and is widely used for autonomous positioning of vehicles.
The main problem faced by inertial navigation is that the positioning error diverges seriously with time, and in the document [1] ([ 1]Collin J.MEMSIMUCarouseling for Ground Vehicles[J ]. IEEE Transactions on Vehicular Technology,2015,64 (6): 2242-2251), collin proposes a vehicle positioning scheme of installing a Micro-Electro-Mechanical System (MEMS) inertial measurement unit (Initial Measurement Unit, IMU) on a wheel, sensing gravitational acceleration through a triaxial accelerometer to obtain the number of turns of the wheel rotation, realizing the measuring effect of an 'odometer', inhibiting zero drift of a gyro by utilizing the wheel rotation modulation effect, sensing the attitude of the vehicle by the gyro, finally achieving the positioning purpose, and ensuring that the maximum error is not more than 8m in a positioning experiment of nearly 1 km.
Conventional odometers are often installed before the vehicle leaves the factory. The conventional rotation modulation strapdown inertial navigation system needs a complex and precise electromechanical control system, and can effectively inhibit the systematic error of an inertial device. An IMU can be equivalent to the combination of an odometer and a single-axis rotation strapdown inertial navigation system only by being arranged on a wheel, so that the inertial navigation positioning accuracy is greatly improved.
In the document [1], the MEMSIMU is installed on the axle center of the wheel by using a special fixture, but the center of the IMU shell is not equal to the sensitive center of the accelerometer, and the sensitive center of the accelerometer is difficult to ensure to be exactly positioned on the rotating shaft of the wheel during installation, and the eccentricity exists, so that centripetal acceleration and tangential acceleration are generated due to the rotation of the wheel and act on the accelerometer, and the accurate estimation of the rotating speed of the wheel is affected. Axle alignment errors of the IMU relative to the wheel are also difficult to avoid when installed, which would be equivalent to systematic errors of the IMU affecting vehicle orientation estimation accuracy. Particularly, when the vehicle movement speed is high, the mounting error can make the error of the vehicle positioning model based on the IMU mounted on the wheels be highlighted, and the vehicle positioning precision is further reduced. The special fixture can reduce the installation error to a certain extent, but increases the installation difficulty and the use cost.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provides a field calibration method for mounting parameters of a wheel mounting inertial measurement unit, which can inhibit the influence of a wheel IMU mounting error on the vehicle positioning accuracy and improve the vehicle positioning accuracy based on the wheel mounting IMU.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a method for calibrating installation parameters of a wheel installation inertia measurement unit on site comprises the following steps:
s1, in a rough calibration stage, a vehicle starts to be in a static state on a horizontal road surface and then runs for a small distance along a straight line, and a rough value of an axis alignment error of an inertial measurement unit relative to wheels is obtained through a three-axis attitude determination (TRIaxial Attitude Determination, TRIAD) algorithm;
s2, in a fine calibration stage, the vehicle starts to be in a static state on a horizontal road surface, then freely runs on the horizontal road surface, a Kalman filter is established through the kinematic constraint of the vehicle and the output of the inertia measurement unit, and the eccentricity of the inertia measurement unit relative to the wheels and the accurate value of the shaft alignment error are calculated according to the Kalman filter, and meanwhile the pose of the vehicle is estimated.
Specifically, step S1 includes:
s11, collecting output values of a triaxial accelerometer (accelerometer for short) in a vehicle stop state, and averaging the output values of the accelerometer within 1 second;
s12, collecting output values of a three-axis gyroscope (gyroscope for short) of the vehicle in a straight running state, and averaging the output values of the gyroscope within 1 second;
and S13, calculating the rough value of the axis alignment error matrix of the inertial measurement unit relative to the wheel by adopting a TRIAD algorithm according to the average value obtained in the step S11 and the step S12.
Specifically, in step S13, the formula for calculating the coarse value of the axis alignment error matrix is:
wherein,w 2 ＝[0 g 0] T ；
estimated result for the wheel coordinate system w system +.>Coordinate transformation matrix from the system to the coordinate system b of the inertial measurement unit;for 1 second, average value of output values of gyro, < >>An average value of output values of the accelerometer within 1 second; g is the local gravitational acceleration value.
Specifically, step S2 includes:
s21, establishing a Kalman filter, setting an initial value of the Kalman filter, and taking a result of a coarse calibration stage as an initial value of a shaft alignment error matrix in a fine calibration stage;
s22, reading output data of an inertial measurement unit in the running process of a frame of vehicle according to a time sequence, and substituting the output data into a Kalman filter;
s23, estimating a state variable and a mean square error matrix through a Kalman filter;
s24, feeding back a state, and updating an axis alignment error value;
s25, calculating and positioning the body posture;
s26, repeating the steps S22 to S25 until the data are completely read in the step S22.
Specifically, in step S21, establishing the kalman filter includes establishing a state equation and an observation equation of the extended kalman filter;
the equation of state:
observation equation: z=h (X) +v
Wherein the state variablesAlpha is the wheel angle>For the angular velocity of the wheel of the vehicle,is the angular acceleration of the wheel; the axis alignment error of the inertial measurement unit with respect to the wheel is expressed in terms of Euler angle, i.e. roll angle gamma s Pitch angle theta s And azimuth angle psi s The method comprises the steps of carrying out a first treatment on the surface of the r is the eccentricity between the inertial measurement unit and the wheel; setting the initial variance matrix of the state variables as P (t) 0 )；/>Is Gaussian white noise, and the variance is Q; />f b Outputting for a triaxial accelerometer; v is observation noise, and the variance matrix is R; t is t k =kt, T is the sampling period, and k is a positive integer.
Wherein,
g n ＝[0 0 -g] T
the v system is a vehicle body coordinate system, the origin of coordinates of which is located at the contact point of the wheel and the ground, the n system is a navigation coordinate system which solidifies relative to the earth and coincides with the initial moment v system,for the coordinate transformation matrix from A to B, < ->w AB For the angular velocity of the B-series relative to the A-series, < >>Is the angular velocity w AB Projection under C-line, +.>Are respectively marked as +.> And->a AB Acceleration of B relative to A +.>For acceleration a AB Projection under C, R is the wheel radius.
Specifically, in step S23, the method for estimating the state variable and the mean square error matrix by using the kalman filter is as follows:
from X (t) k/k-1 )＝ΦX(t k-1 ) A predicted state variable X, represented by the formula P (t k/k-1 )＝ΦP(t k-1 )Φ T +ΓQΓ T Predicting a mean square error matrix;
from the formulaUpdate h (X), by the formula->Updating jacobian matrix H J ；
The state variable X and the mean square error matrix P are estimated by:
K＝P(t k/k-1 )H J T [H J P(t k/k-1 )H J T +R] -1
X(t k )＝X(t k/k-1 )+K[Z-H J X(t k/k-1 )]
P(t k )＝[I-KH J ]P(t k/k-1 )。
specifically, in step S24, the formulaUpdate->And will update +.>As a next round of circulation +.>I.e. < ->Will gamma s 、θ s Sum phi s And setting 0.
Specifically, in step S25, the vehicle body posture is defined byUpdating; vehicle position is defined by Updating.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, the on-site calibration method is utilized to calibrate the mounting parameters of the IMU of the wheel, eliminate the influence of the mounting errors of the IMU of the wheel on the positioning accuracy of the vehicle, and improve the positioning accuracy of the vehicle based on the IMU of the wheel. In addition, no special fixture is needed when the IMU is installed on the wheel, and the IMU is not needed to be installed on the axle center of the wheel, so that the installation difficulty and the use cost are reduced.
Drawings
FIG. 1 is a flow chart of a method for calibrating installation parameters of a wheel-mounted inertial measurement unit in situ according to the present invention.
Fig. 2 is a schematic view of an IMU mounted on a wheel in accordance with an embodiment of the invention.
FIG. 3 is a diagram illustrating a coordinate system and a partial variable definition according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a precise calibration result of an IMU mounting parameter of a wheel according to an embodiment of the invention.
Fig. 5 is a schematic diagram of a vehicle positioning result according to an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made more apparent and fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by one of ordinary skill in the art without undue burden on the person of ordinary skill in the art based on embodiments of the present invention, are within the scope of the present invention.
The embodiment provides a method for calibrating installation parameters of a wheel installation inertial measurement unit on site, which comprises the following steps:
firstly, the MEMSIMU is fixed on the right wheel of the vehicle by using the magic tape, and the trolley is driven on a horizontal road surface in the whole field calibration process. The computer collects IMU data through Bluetooth, and runs an IMU installation parameter on-site calibration program, the model of the IMU is LPMS-B2, and the sampling rate is 50Hz.
As shown in fig. 2 and 3, the IMU coordinate system is denoted as b-system, whose origin is located at the IMU center (precisely, the accelerometer sensitivity center); the wheel coordinate system is a w system, the x axis of the w system is perpendicular to the wheel rotating shaft, the IMU center points to the wheel center, the z axis points to the right side of the vehicle body along the direction of the wheel rotating shaft, and the origin of the z axis is positioned at the wheel center; the coordinate system of the vehicle body is v system, three axial directions of the v system point to the right, the front and the upper parts of the vehicle in sequence, and the origin point is positioned at the contact point between the wheels provided with the IMU and the ground; the navigation coordinate system n is solidified relative to the earth, and the n is overlapped with the initial moment v; the radius of the wheel is recorded as R, and the distance (namely the eccentricity) between the center of the IMU and the rotating shaft of the wheel is recorded as R.
As shown in fig. 1, the installation parameter on-site calibration procedure is divided into two stages of coarse calibration and fine calibration, and the specific steps are as follows:
step S1: coarse calibration is carried out, the vehicle is in a static state on a horizontal road surface, accelerometer output in the static state is collected, then the vehicle moves for a small distance along a straight line on the horizontal road surface, gyro output in the straight line movement state is collected, and a coarse value of an axis alignment error matrix is determined by a TRIAD algorithm.
Step S2: and (3) performing fine calibration to enable the vehicle to freely run on a horizontal road surface from a stationary state, collecting IMU output, substituting the IMU output into an extended Kalman filter, estimating an axis alignment error matrix and an eccentricity, and simultaneously estimating the position of the vehicle.
Specifically, step S1 includes:
step S11: the vehicle keeps in a static state on a horizontal road surface, collects the output of the accelerometer, averages the output of the accelerometer collected within 1 second to reduce the influence of noise, and records the result as
Step S12: the vehicle runs along a straight line for a short distance on a horizontal road surface, acquires gyro output, averages the gyro output acquired within 1 second to reduce noise influence, and records the result as
Step S13: according to the average value obtained in the step S11 and the step S12, calculating to obtain a rough value of an axis alignment error matrix of the inertial measurement unit relative to the wheel by adopting a TRIAD algorithm;
as shown in fig. 3, an intermediate coordinate system w 'is defined by rotating the w system along the z-axis, and the y-axis of the w' system is upward along the vertical direction; on a horizontal road surface, the vehicle is stationary at the beginning time, the local gravity acceleration is g, and the following conditions are satisfied:
recording deviceIs a coordinate transformation matrix from A system to B system. Record w AB For the angular velocity of the B-series relative to the A-series, < >>Is the angular velocity w AB Projection under C-line, +.>Are respectively marked as +.>And->Let a be the wheel rotation angle (as shown in fig. 3), the angular velocity w of the wheel relative to the body vw The method meets the following conditions:
the output of the gyroscope is recorded asWherein i is an inertial coordinate system. The MEMS gyroscope has relatively low precision, the influence of the rotation of the earth and the IMU linear motion on the gyroscope output can be ignored, and the gyroscope output is approximately +.>In the rough calibration stage, the vehicle runs on the horizontal road surface in a straight line, and w is satisfied vw ＝w nv And then (I) is added with>Average output of gyro->The method meets the following conditions:
obtaining by adopting TRIAD algorithmThe calculation formula is as follows:
wherein,w 2 ＝[0 g 0] T the method comprises the steps of carrying out a first treatment on the surface of the g is the local gravitational acceleration value.
Specifically, step S2 includes:
s21, establishing a Kalman filter, setting an initial value of the Kalman filter, setting an initial value of a state variable X, and setting an initial variance matrix of the state variable as P (t) 0 ) Setting state noiseSetting the variance matrix of the observed noise V as R, wherein the variance is Q; and taking the result of the coarse calibration stage as the center axis alignment error matrix of the fine calibration stage>Initial value of (1), i.e. let-> As an initial value of the fine calibration stage;
establishing a Kalman filter comprises establishing a state equation and an observation equation of the extended Kalman filter;
the equation of state:
observation equation: z=h (X) +v
Wherein the state variablesAlpha is the wheel angle>For the angular velocity of the wheel of the vehicle,is the angular acceleration of the wheel; shaft alignment error matrix>Conversion to Euler angle, i.e. roll angle gamma s Pitch angle theta s Azimuth angle psi s The method comprises the steps of carrying out a first treatment on the surface of the r is the eccentricity between the inertial measurement unit and the wheel; />f b Outputting for a triaxial accelerometer; t is t k =kt, T is the sampling period, k is a positive integer;
after coarse calibration, gamma s And theta s Is of small angle, psi s For large angle, a large azimuth angle and small horizontal angle posture transformation matrix model is adopted,the method meets the following conditions:
the vehicle runs on the horizontal road surface, and meets the following conditions:
satisfy->Since the vehicle is driving on a level road, the pitch angle rate is theoretically absent, thus meeting +.>
Accelerometers are mainly affected by gravitational acceleration, motion acceleration due to rotation of the wheels relative to the body, and acceleration of the body relative to the earth.
V is recorded AB The speed of the B series relative to the A series,is the velocity v AB Projection under C. The IMU is mounted on the non-steering wheel of the vehicle, and is derived from the kinematic characteristics of the vehicle: />
Record a AB The acceleration of the B system relative to the A system,for acceleration a AB Projection under C. Acceleration a of the vehicle body relative to the earth nv From centripetal acceleration a nvc And tangential acceleration a nvt Composition, a nv ＝a nvc +a nvt The method comprises the following steps:
because the vehicle runs on the horizontal road surface, the pitch angle speed of the vehicle is 0, thereby meeting the requirements ofHere, the
The IMU generates centripetal acceleration and tangential acceleration relative to the center of the wheel due to wheel rotation, satisfying:
if the IMU center is at the negative half axis of the y axis of the w system, r is more than 0; if the IMU center is at the positive half axis of the y-axis of the w system, r <0.
In addition, the accelerometer is also subjected to the coriolis acceleration generated by the rotation of the earth or steering of the vehicle body, and the acceleration component generated by the lever arm effect, which can be ignored because of the small acceleration. Accelerometer output f b The relationship between motion acceleration approximately satisfies:
in the formula g n Projection of gravity acceleration vector in n system, g n ＝[0 0 -g] T 。
S22, reading output data of an inertial measurement unit in the running process of a frame of vehicle according to a time sequence, and substituting the output data into a Kalman filter;
s23, estimating a state variable and a mean square error matrix through a Kalman filter;
from X (t) k/k-1 )＝ΦX(t k-1 ) A predicted state variable X, represented by the formula P (t k/k-1 )＝ΦP(t k-1 )Φ T +ΓQΓ T Predicting a mean square error matrix;
from the formulaUpdate h (X), by the formula->Updating jacobian matrix H J ；
The state variable X and the mean square error matrix P are estimated by:
K＝P(t k/k-1 )H J T [H J P(t k/k-1 )H J T +R] -1
X(t k )＝X(t k/k-1 )+K[Z-H J X(t k/k-1 )]
P(t k )＝[I-KH J ]P(t k/k-1 )。
s24, feeding back a state, and updating an axis alignment error value;
from the formulaUpdate->And will update +.>As a next round of circulation +.>I.e.Will gamma s 、θ s Sum phi s And setting 0.
S25, calculating and positioning the body posture;
body posture is defined byUpdating; the wheel angle and the wheel radius are multiplied to obtain the vehicle mileage, and the vehicle position is represented by +.>Updating.
S26, repeating the steps S22 to S25 until the data are completely read in the step S22.
As shown in FIG. 4, the final estimated value of the eccentricity is identical to the measured value of the ruler in FIG. 4 (a), and the Euler angle is set by the axis alignment error matrix in FIG. 4 (b)And (5) transforming to obtain the product. As shown in fig. 5, the positioning result of the vehicle in this embodiment is far greater than the positioning accuracy achieved by a general vehicle-mounted MEMSIMU, and the validity of the calibration result is reflected from the side.
In this embodiment, according to the actual application scenario, only the coarse calibration method or only the fine calibration method may be selected, or both the coarse calibration method and the fine calibration method may be adopted to calibrate the installation parameters of the inertial measurement unit for wheel installation. In this embodiment, the coarse calibration calculation formula based on the TRIAD algorithm may be replaced by an algorithm with other similar principles; the state equation and the observation equation of the kalman filter for fine calibration in this embodiment may have other description modes, for example, euler angles are represented by quaternions; the extended kalman filter may also be replaced by other kalman filters, such as unscented kalman filters.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. The on-site calibration method for the installation parameters of the wheel installation inertia measurement unit is characterized by comprising the following steps of:
s1, in a rough calibration stage, obtaining a rough value of an axis alignment error of an inertial measurement unit relative to a wheel by adopting a TRIAD algorithm;
s2, in a fine calibration stage, a Kalman filter is established through the kinematic constraint of the vehicle and the output of the inertia measurement unit, and the eccentricity of the inertia measurement unit relative to the wheel and the accurate value of the shaft alignment error are calculated according to the Kalman filter, and meanwhile the pose of the vehicle is estimated;
the step S2 comprises the following steps:
s21, establishing a Kalman filter, setting an initial value of the Kalman filter, and taking a result of a coarse calibration stage as an initial value of a shaft alignment error matrix in a fine calibration stage;
s22, reading output data of a frame of inertial measurement unit according to a time sequence, and substituting the output data into a Kalman filter;
s23, estimating a state variable and a mean square error matrix through a Kalman filter;
s24, feeding back a state, and updating an axis alignment error value;
s25, calculating and positioning the body posture;
s26, repeating the steps S22 to S25 until the data are completely read in the step S22;
in step S21, establishing the kalman filter includes establishing a state equation and an observation equation of the kalman filter;
the equation of state:
observation equation: z=h (X) +v
Wherein the state variablesAlpha is the wheel angle>For the angular speed of the wheel>Is the angular acceleration of the wheel; the axis alignment errors of the inertial measurement unit with respect to the wheel are expressed in terms of Euler angles, respectivelyRoll angle gamma s Pitch angle theta s Azimuth angle psi s The method comprises the steps of carrying out a first treatment on the surface of the r is the eccentricity between the inertial measurement unit and the wheel; setting the initial variance matrix of the state variables as P (t) 0 )；/>Is Gaussian white noise, and the variance is Q; />f b Outputting for a triaxial accelerometer; v is observation noise, and the variance matrix is R; t is t k =kt, T is the sampling period, k is a positive integer;
wherein,
g n ＝[0 0 -g] T
the v system is a vehicle body coordinate system, the origin of coordinates of the v system is positioned at the contact point of the wheel and the ground, the w system is a wheel coordinate system,the estimated result of the w system, the IMU coordinate system, and the navigation coordinate system, which is solidified relative to the earth and coincides with the initial time v system,/-, are given by>For the coordinate transformation matrix from A to B, < ->w AB For the angular velocity of the B-series relative to the A-series, < >>Is the angular velocity w AB Projection under C-line, +.>Are respectively marked as +.>And->a AB Acceleration of B relative to A +.>For acceleration a AB Projection under C, R is the wheel radius, g is the local gravitational acceleration value.
2. The method for in-situ calibration of installation parameters of a wheel-mounted inertial measurement unit according to claim 1, wherein step S1 comprises:
s11, collecting output values of the triaxial accelerometer in a vehicle stop state, and averaging the output values of the triaxial accelerometer within 1 second;
s12, collecting output values of the three-axis gyroscope in a straight running state of the vehicle, and averaging the output values of the three-axis gyroscope within 1 second;
and S13, calculating the rough value of the axis alignment error matrix of the inertial measurement unit relative to the wheel by adopting a TRIAD algorithm according to the average value obtained in the step S11 and the step S12.
3. The method according to claim 2, wherein in step S13, the formula for calculating the rough value of the axle alignment error matrix is:
wherein,w 2 ＝[0 g 0] T ；
estimated result for the wheel coordinate system w system +.>Coordinate transformation matrix from the system to the coordinate system b of the inertial measurement unit; />Is the average value of the output values of the triaxial gyroscope within 1 second, < >>The average value of the output values of the triaxial accelerometer within 1 second; g is the local gravitational acceleration value.
4. The method for in-situ calibration of installation parameters of a wheel-mounted inertial measurement unit according to claim 1, wherein in step S23, the method for estimating the state variable and the mean square error matrix by using a kalman filter is as follows:
from X (t) k/k-1 )＝ΦX(t k-1 ) A predicted state variable X, represented by the formula P (t k/k-1 )＝ΦP(t k-1 )Φ T +ΓQΓ T Predicting a mean square error matrix P;
from the formulaUpdate h (X), by the formula->Updating jacobian matrix H J ；
The state variable X and the mean square error matrix P are estimated by:
K＝P(t k/k-1 )H J T [H J P(t k/k-1 )H J T +R] -1
X(t k )＝X(t k/k-1 )+K[Z-H J X(t k/k-1 )]
P(t k )＝[I-KH J ]P(t k/k-1 )。
5. the method for in-situ calibration of installation parameters of a wheel-mounted inertial measurement unit of claim 1, wherein in step S24, the method comprises the steps ofUpdate->And will update +.>As a next round of circulation +.>I.e. < ->Will gamma s 、θ s Sum phi s And setting 0.
6. The method for in-situ calibration of installation parameters of a wheel-mounted inertial measurement unit according to claim 1, wherein in step S25, the body posture is determined byUpdating; vehicle position is defined by-> Updating.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106595652A (en) * | 2016-11-30 | 2017-04-26 | 西北工业大学 | Vehicle MCA (motion constraints aided) backtracking type aligning-on-the-move method |
| CN107063241A (en) * | 2017-03-16 | 2017-08-18 | 上海联适导航技术有限公司 | Front-wheel angle measuring system based on double GNSS antennas and single shaft MEMS gyro |
| CN110873575A (en) * | 2020-01-17 | 2020-03-10 | 立得空间信息技术股份有限公司 | Mileage measurement method based on inertial sensor |
| CN111678514A (en) * | 2020-06-09 | 2020-09-18 | 电子科技大学 | Vehicle-mounted autonomous navigation method based on carrier motion condition constraint and single-axis rotation modulation |
| CN111912426A (en) * | 2020-08-10 | 2020-11-10 | 电子科技大学 | Low-cost odometer design method based on MEMS IMU |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9026263B2 (en) * | 2011-11-30 | 2015-05-05 | Alpine Electronics, Inc. | Automotive navigation system and method to utilize internal geometry of sensor position with respect to rear wheel axis |
| US10520317B2 (en) * | 2016-06-02 | 2019-12-31 | Maliszewski Family 2001 Trust | In-situ wheel position measurement using inertial measurement units (IMUs) |
| CN111780756A (en) * | 2020-07-20 | 2020-10-16 | 北京百度网讯科技有限公司 | Vehicle dead reckoning method, device, equipment and storage medium |
-
2021
- 2021-11-09 CN CN202111320679.9A patent/CN114152269B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106595652A (en) * | 2016-11-30 | 2017-04-26 | 西北工业大学 | Vehicle MCA (motion constraints aided) backtracking type aligning-on-the-move method |
| CN107063241A (en) * | 2017-03-16 | 2017-08-18 | 上海联适导航技术有限公司 | Front-wheel angle measuring system based on double GNSS antennas and single shaft MEMS gyro |
| CN110873575A (en) * | 2020-01-17 | 2020-03-10 | 立得空间信息技术股份有限公司 | Mileage measurement method based on inertial sensor |
| CN111678514A (en) * | 2020-06-09 | 2020-09-18 | 电子科技大学 | Vehicle-mounted autonomous navigation method based on carrier motion condition constraint and single-axis rotation modulation |
| CN111912426A (en) * | 2020-08-10 | 2020-11-10 | 电子科技大学 | Low-cost odometer design method based on MEMS IMU |
Non-Patent Citations (5)
| Title |
|---|
| Design and Performance of Wheel-mounted MEMS IMU for Vehicular Navigation;Oleg Mezentsev et al.;《 2019 IEEE International Symposium on Inertial Sensors and Systems》;1-6 * |
| Self-calibration algorithm for an IMU in two-wheeled vehicles: design and experimental validation;Gelmini, Simone et al.;《IEEE》;1751-1756 * |
| 基于不完整双矢量观测的三轴姿态确定算法;谭彩铭等;《仪器仪表学报》;第39卷(第11期);140-146 * |
| 基于车轮安装惯性测量单元的车载组合导航;陈映秋等;《中国惯性技术学报》;第26卷(第6期);799-804 * |
| 车载LiDAR中基于霍尔开关的里程计构建;蔡海永;钟若飞;曾凡阳;吕冰;;测绘技术装备(第01期);45-48 * |
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