CN105371840B - A kind of inertia/visual odometry/laser radar Combinated navigation method - Google Patents

A kind of inertia/visual odometry/laser radar Combinated navigation method Download PDF

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CN105371840B
CN105371840B CN201510727853.XA CN201510727853A CN105371840B CN 105371840 B CN105371840 B CN 105371840B CN 201510727853 A CN201510727853 A CN 201510727853A CN 105371840 B CN105371840 B CN 105371840B
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error
speed
matrix
visual odometry
laser radar
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CN105371840A (en
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孙伟
李海军
郭元江
徐海刚
李群
郑辛
张忆欣
刘冲
裴玉锋
原润
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Beijing Automation Control Equipment Institute BACEI
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
    • G01S17/50—Systems of measurement based on relative movement of target
    • G01S17/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders

Abstract

The invention belongs to air navigation aids, and in particular to a kind of inertia/visual odometry/laser radar Combinated navigation method.It includes: the foundation of (1) state model, (2) visual odometry based on characteristic information tests the speed, (3) acquisition of the foundation of measurement equation and measuring value, (4) Kalman filtering, (5) correct systematic error.Effect of the invention is: this patent uses the autonomous navigation technology of machine vision, and monocular camera passes through the difference of before and after frames image, the speed of carrier can be measured in the case where known distance；Laser radar can accurately measure the distance of observation point, then measure the speed of carrier, and the speed and inertial navigation velocity composition obtained using measurement is navigated, and can finally realize that high-precision is navigated under without extraneous reference information input condition.

Description

A kind of inertia/visual odometry/laser radar Combinated navigation method
Technical field
The invention belongs to air navigation aids, and in particular to a kind of inertia/visual odometry/laser radar integrated navigation side Method.
Background technique
As aircraft long-time, the ability of long voyage are continuously available enhancing, to the precision and independence of navigation system It is required that being also continuously improved.The shortcomings that pure-inertial guidance system is accumulated at any time due to its intrinsic navigation error, because without The needs of practical application can be fully met.It solves the problems, such as that this approach has two classes: the first, improving the essence of inertial navigation system itself Degree.New material, new process, new technology are relied primarily upon, the precision or the high-precision inertia of development of new of inertia device are improved Device.But it needs to spend a large amount of manpower financial capacity, and the raising of inertia device precision is limited.The second, using integrated navigation Technology.Mainly using certain additional navigation information sources outside inertia system, to improve the precision of inertia system, by soft Part technology improves navigation accuracy.However, there are many available combined information, if all combined these information, although smart Degree, which can achieve, even more than to be required, but calculation amount is huge, can not use in practice completely, if only selected part information Combination, then choosing the type of information, combined sequencing, specific combination all generates huge shadow to the precision of result It rings.Calculation amount can be not only still taken into account without a kind of perfect combination of comparison in the prior art, but also is reduced to the greatest extent Combined content.
Summary of the invention
The present invention in view of the drawbacks of the prior art, provides a kind of inertia/visual odometry/laser radar integrated navigation side Method.
The present invention is implemented as follows: a kind of inertia/visual odometry/laser radar Combinated navigation method, including under State step:
(1) inertia/visual odometry/laser radar integrated navigation system state model foundation, such as following formula
In formula: X (t) is above system state vector；W (t) is system white noise；Coefficient matrix F (t) and G (t) is according to accidentally Eikonal equation is sought,
X (t)=[δ Vn, δ Vu, δ Ve, δ L, δ h, δ λ, φn, φu, φe, ▽x, ▽y, ▽z, εx, εy, εz, δ Vn_ov, δ Ve_OV]
δVn,δVu,δVeRespectively indicate Strapdown Inertial Navigation System north orientation, velocity error from day to, east orientation；
δ L, δ h, δ λ respectively indicate the latitude error, height error, longitude error of Strapdown Inertial Navigation System；
φn,φu,φeRespectively indicate Strapdown Inertial Navigation System navigational coordinate system Nei Bei, day, eastern three directions misalignment；
▽x, ▽y, ▽zRespectively indicate the accelerometer zero in tri- directions X, Y, Z in Strapdown Inertial Navigation System carrier coordinate system Partially；
εx,εy,εzRespectively indicate the gyroscopic drift in tri- directions X, Y, Z in Strapdown Inertial Navigation System carrier coordinate system；
δVn_ov,δVe_OVRespectively indicate the velocity error of visual odometry north orientation, east orientation；
(2) visual odometry based on characteristic information tests the speed
A) suitable matching area is chosen
The geometric center position of image is chosen as matching area；
B) image feature information is extracted
By the image of present frame, previous frame image in matching area intercept out, to after interception image carry out SIFT feature is extracted,
C) Feature Points Matching
It is matched with K-D tree rapid characteristic points, current frame image is matched with the characteristic point in previous frame image, is obtained A series of matching pair,
D) same characteristic point calculating speed is selected
The speed for calculating characteristic point divided by the time to the different distance of upper acquisition in matching is obtained using laser radar；
E) current frame speed is exported
Each characteristic point can calculate a speed, export after these speed are handled；
(3) acquisition of the foundation of measurement equation and measuring value
Kalman filter measurement equation form is as follows:
Z=HX+V
Measuring value Z is the difference of speed that inertial navigation system and visual odometry provide respectively, both actually error Difference:
V is to measure noise in formula, is thought of as white noise,
As available from the above equation H gusts it is as follows:
(4) Kalman filtering
According to inertia/visual odometry/laser radar integrated navigation system equation and measurement equation, Kalman's filter is calculated State Matrix of shifting of a step when wave period arrives, calculation formula are as follows:
In formula:
TnFor period of navigating, NTnFor the Kalman filtering period,For in a Kalman filtering period, i-th of navigation The sytem matrix in period, I are unit battle array,
State one-step prediction
State estimation
Filtering gain matrix
One-step prediction error covariance matrix
Estimation error variance battle array
Pk=[I-KkHk]Pk,k-1
Wherein,For a step status predication value,For state estimation matrix, Φk,k-1For state Matrix of shifting of a step, HkFor measurement matrix, ZkFor measurement, KkFor filtering gain matrix, RkFor observation noise battle array, Pk,k-1For one-step prediction error variance Battle array, PkFor estimation error variance battle array, Γk,k-1Battle array, Q are driven for system noisek-1For system noise acoustic matrix；
Using the equation of step (1) and this step, a series of error amounts can be calculated；
(5) systematic error is corrected
System output value is corrected with step (4) calculated error amount.
A kind of inertia/visual odometry/laser radar Combinated navigation method as described above, wherein the step (2) e) processing of step middle finger is RANSAC method or average.
Effect of the invention is: this patent uses the autonomous navigation technology of machine vision, and monocular camera passes through before and after frames figure The difference of picture can measure the speed of carrier in the case where known distance；Laser radar can accurately measure observation The distance of point, then measures the speed of carrier, and the speed and inertial navigation velocity composition obtained using measurement is navigated, finally can be without outer Realize that high-precision is navigated under boundary's reference information input condition.
Specific embodiment
A kind of inertia/visual odometry/laser radar Combinated navigation method, includes the following steps:
(1) inertia/visual odometry/laser radar integrated navigation system state model foundation, such as following formula
In formula: X (t) is above system state vector；W (t) is system white noise；Coefficient matrix F (t) and G (t) is according to accidentally Eikonal equation is sought.
X (t)=[δ Vn,δVu,δVe,δL,δh,δλ,φn,φu,φe,▽x,▽y,▽z,εx,εy,εz,δVn_ov,δVe_OV]
δVn,δVu,δVeRespectively indicate Strapdown Inertial Navigation System north orientation, velocity error from day to, east orientation；
δ L, δ h, δ λ respectively indicate the latitude error, height error, longitude error of Strapdown Inertial Navigation System；
φn,φu,φeRespectively indicate Strapdown Inertial Navigation System navigational coordinate system Nei Bei, day, eastern three directions misalignment；
▽x,▽y,▽zRespectively indicate the accelerometer zero in tri- directions X, Y, Z in Strapdown Inertial Navigation System carrier coordinate system Partially；
εx,εy,εzRespectively indicate the gyroscopic drift in tri- directions X, Y, Z in Strapdown Inertial Navigation System carrier coordinate system；
δVn_ov,δVe_OVRespectively indicate the velocity error of visual odometry north orientation, east orientation；
(2) visual odometry based on characteristic information tests the speed
A) suitable matching area is chosen
In order to improve the range that tests the speed of visual odometry, the speed exported in conjunction with inertial navigation, posture information is needed to select to close Suitable position is as matching area.As matching area, area size is 30*30 picture for the general geometric center position for choosing image Element.
B) image feature information is extracted
By the image of present frame, previous frame image in matching area intercept out, to after interception image carry out SIFT feature is extracted.
C) Feature Points Matching
Suitable threshold value is set, the matching of K-D tree rapid characteristic points is applicable in, by the spy in current frame image and previous frame image Sign point is matched, and obtains a series of matching pair.
D) same characteristic point calculating speed is selected
By the speed calculation formula of visual odometry it is found that the calculating of distance and speed is for same point, therefore It needs to find the same characteristic point in current frame image, previous frame image.This feature point and camera are obtained using laser radar Distance, then information of the matching in by this feature point in current frame image and previous frame image calculates this feature point With the relative velocity of camera.
E) current frame speed is exported
Each characteristic point can calculate a speed, by these speed carry out certain processing (such as RANSAC method or It is average), finally export the result that tests the speed of visual odometry.
(3) acquisition of the foundation of measurement equation and measuring value
Kalman filter measurement equation form is as follows:
Z=HX+V
Measuring value Z is the difference of speed that inertial navigation system and visual odometry provide respectively, both actually error Difference:
V is to measure noise in formula, is thought of as white noise.
As available from the above equation H gusts it is as follows:
(4) Kalman filtering
According to inertia/visual odometry/laser radar integrated navigation system equation and measurement equation, Kalman's filter is calculated State Matrix of shifting of a step when wave period arrives, calculation formula are as follows:
In formula:
TnFor period of navigating, NTnFor the Kalman filtering period,For in a Kalman filtering period, i-th of navigation The sytem matrix in period, I are unit battle array.
State one-step prediction
State estimation
Filtering gain matrix
One-step prediction error covariance matrix
Estimation error variance battle array
Pk=[I-KkHk]Pk,k-1
Wherein,For a step status predication value,For state estimation matrix, Φk,k-1For state Matrix of shifting of a step, HkFor measurement matrix, ZkFor measurement, KkFor filtering gain matrix, RkFor observation noise battle array, Pk,k-1For one-step prediction error variance Battle array, PkFor estimation error variance battle array, Γk,k-1Battle array, Q are driven for system noisek-1For system noise acoustic matrix.
(5) systematic error is corrected
System output value is corrected with step (4) calculated error amount.

Claims (2)

1. a kind of inertia/visual odometry/laser radar Combinated navigation method, which is characterized in that include the following steps:
(1) inertia/visual odometry/laser radar integrated navigation system state model foundation, such as following formula
In formula: X (t) is above system state vector；W (t) is system white noise；Coefficient matrix F (t) and G (t) are according to error side Journey is sought,
δVn, δ Vu, δ VeRespectively indicate Strapdown Inertial Navigation System north orientation, velocity error from day to, east orientation；
δ L, δ h, δ λ respectively indicate the latitude error, height error, longitude error of Strapdown Inertial Navigation System；
φn, φu, φeRespectively indicate Strapdown Inertial Navigation System navigational coordinate system Nei Bei, day, eastern three directions misalignment；
Respectively indicate the accelerometer bias in tri- directions X, Y, Z in Strapdown Inertial Navigation System carrier coordinate system；
εx, εy, εzRespectively indicate the gyroscopic drift in tri- directions X, Y, Z in Strapdown Inertial Navigation System carrier coordinate system；
δVn_ov, δ Ve_0VRespectively indicate the velocity error of visual odometry north orientation, east orientation；
(2) visual odometry based on characteristic information tests the speed
A) suitable matching area is chosen
The geometric center position of image is chosen as matching area；
B) image feature information is extracted
By the image of present frame, previous frame image in matching area intercept out, it is special to carry out SIFT to the image after interception Sign point extracts,
C) Feature Points Matching
It is matched with K-D tree rapid characteristic points, current frame image is matched with the characteristic point in previous frame image, obtains a system The matching pair of column,
D) same characteristic point calculating speed is selected
The speed for calculating characteristic point divided by the time to the different distance of upper acquisition in matching is obtained using laser radar；
E) current frame speed is exported
Each characteristic point can calculate a speed, export after these speed are handled；
(3) acquisition of the foundation of measurement equation and measuring value
Kalman filter measurement equation form is as follows:
Z=HX+V
Measuring value Z is the difference for the speed that inertial navigation system and visual odometry provide respectively, actually the difference of the two error:
V is to measure noise, as white noise in formula,
As available from the above equation H gusts it is as follows:
(4) Kalman filtering
According to inertia/visual odometry/laser radar integrated navigation system equation and measurement equation, Kalman filtering week is calculated State Matrix of shifting of a step when phase arrives, calculation formula are as follows:
In formula:
TnFor period of navigating, NTnFor the Kalman filtering period,For in a Kalman filtering period, i-th of navigation period Sytem matrix, I be unit battle array,
State one-step prediction
State estimation
Filtering gain matrix
One-step prediction error covariance matrix
Estimation error variance battle array
Pk=[I-KkHk]PK, k-1
Wherein,For a step status predication value,For state estimation matrix, Φk,k-1For state Matrix of shifting of a step, HkFor Measurement matrix, ZkFor measurement, KkFor filtering gain matrix, RkFor observation noise battle array, Pk,k-1For one-step prediction error covariance matrix, PkFor estimation error variance battle array, Γk,k-1Battle array, Q are driven for system noisek-1For system noise acoustic matrix；
Using the equation of step (1) and this step, a series of error amounts can be calculated；
(5) systematic error is corrected
System output value is corrected with step (4) calculated error amount.
2. a kind of inertia/visual odometry/laser radar Combinated navigation method as described in claim 1, it is characterised in that: The e of the step (2)) processing of step middle finger is RANSAC method or average.
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