CN114396943A - Fusion positioning method and terminal - Google Patents
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- G—PHYSICS
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- 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
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- G01—MEASURING; TESTING
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- 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
- G01C21/1656—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 with passive imaging devices, e.g. cameras
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- 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
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
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- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
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- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
- G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
Abstract
The invention discloses a fusion positioning method and a terminal; receiving environment information of the monocular camera, and estimating the position and the posture of the monocular camera according to the environment information; receiving first positioning data of an inertia measurement unit, and optimizing the first positioning data according to the position and the posture of the monocular camera; receiving second positioning data of the inertial navigation system, and assisting a global navigation satellite system to carry out multi-frequency multi-mode real-time differential positioning according to the second positioning data to obtain third positioning data; establishing a measurement equation and a state equation according to the first positioning data and the third positioning data; estimating a state quantity error by adopting an extended Kalman filtering algorithm according to the first positioning data, a measurement equation and a state equation, and compensating the first positioning data based on the state quantity error to obtain final positioning data; the advantages of long-term absolute position, IMU and monocular vision short-term accurate position calculation of the global navigation satellite system are integrated, and the positioning accuracy is improved.
Description
Technical Field
The invention relates to the technical field of positioning, in particular to a fusion positioning method and a terminal.
Background
The GNSS positioning continuity and the reliability bring great challenges due to the limited complex environment of the operation site, the satellite signals are frequently interrupted due to shielding and electromagnetic interference, the precision of the floating ambiguity is limited due to frequent reinitialization, and a large positioning deviation is generated. In order to improve the positioning performance in a complex environment, integrated satellite navigation and inertial navigation systems are widely used to provide continuous positioning, velocity measurement, and attitude measurement. However, the main disadvantage of MEMS-IMU is that in the absence of effective control, its navigation errors can quickly diverge in a short time, resulting in an insufficiently accurate positioning.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: a fusion positioning method and a terminal are provided to improve positioning accuracy.
In order to solve the technical problems, the invention adopts the technical scheme that:
a fusion localization method, comprising the steps of:
s1, acquiring environment information from the monocular camera, and estimating the position and the posture of the monocular camera according to the environment information;
s2, acquiring first positioning data from an inertia measurement unit, and optimizing the first positioning data according to the position and the posture of the monocular camera;
s3, second positioning data are obtained from an inertial navigation system, multi-frequency multi-mode real-time differential positioning is carried out on the global navigation satellite system assisted by the second positioning data, and third positioning data are obtained;
s4, establishing a measurement equation and a state equation according to the first positioning data and the third positioning data;
s5, estimating a state quantity error by adopting an extended Kalman filtering algorithm according to the first positioning data, the measurement equation and the state equation, and compensating the first positioning data based on the state quantity error to obtain final positioning data.
In order to solve the technical problem, the invention adopts another technical scheme as follows:
a converged positioning terminal comprising a processor, a memory and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the above approach when executing the computer program.
The invention has the beneficial effects that: the technical scheme of the invention fuses data acquired by a multi-frequency multi-mode real-time differential positioning unit, an inertial measurement unit and a monocular camera by adopting an extended Kalman algorithm, integrates the advantages of long-time absolute position of a global navigation satellite system and IMU (inertial measurement unit) and short-time accurate position estimation of monocular vision to obtain the estimation of the optimal position state, adopts the IMU to recover the measurement scale of the monocular vision, improves the motion tracking performance, limits the error drift of the IMU by using vision measurement, and obtains a position and an attitude with higher precision and credibility, thereby improving the positioning precision.
Drawings
Fig. 1 is a flowchart of a fusion positioning method according to an embodiment of the present invention;
fig. 2 is a structural diagram of a converged positioning terminal according to an embodiment of the present invention;
fig. 3 is a schematic data processing diagram of a fusion positioning method according to an embodiment of the present invention;
description of reference numerals:
1. a converged positioning terminal; 2. a processor; 3. a memory.
Detailed Description
In order to explain technical contents, achieved objects, and effects of the present invention in detail, the following description is made with reference to the accompanying drawings in combination with the embodiments.
Referring to fig. 1 and fig. 2, a fusion positioning method includes the steps of:
s1, acquiring environment information from the monocular camera, and estimating the position and the posture of the monocular camera according to the environment information;
s2, acquiring first positioning data from an inertia measurement unit, and optimizing the first positioning data according to the position and the posture of the monocular camera;
s3, second positioning data are obtained from an inertial navigation system, multi-frequency multi-mode real-time differential positioning is carried out on the global navigation satellite system assisted by the second positioning data, and third positioning data are obtained;
s4, establishing a measurement equation and a state equation according to the first positioning data and the third positioning data;
s5, estimating a state quantity error by adopting an extended Kalman filtering algorithm according to the first positioning data, the measurement equation and the state equation, and compensating the first positioning data based on the state quantity error to obtain final positioning data.
From the above description, the beneficial effects of the present invention are: the technical scheme of the invention fuses data acquired by a multi-frequency multi-mode real-time differential positioning unit, an inertial measurement unit and a monocular camera by adopting an extended Kalman algorithm, integrates the advantages of long-time absolute position of a global navigation satellite system and IMU (inertial measurement unit) and short-time accurate position estimation of monocular vision to obtain the estimation of the optimal position state, adopts the IMU to recover the measurement scale of the monocular vision, improves the motion tracking performance, limits the error drift of the IMU by using vision measurement, and obtains a position and an attitude with higher precision and credibility, thereby improving the positioning precision.
Further, the step S1 specifically includes the steps of:
s11, receiving environment information of the monocular camera, and preprocessing the environment information;
s12, extracting characteristic points of Harris angular point characteristics and SIFT scale invariant characteristics from a continuous image of the environmental information, and tracking the characteristic points in adjacent images by adopting an LK sparse optical flow algorithm;
s13, estimating the relative motion of the landmark points and the monocular camera according to the feature points in the adjacent images, and estimating the position and the posture of the monocular camera by combining the relative motion of the landmark points and the monocular camera.
From the above description, after the environment information acquired by the monocular camera is obtained, the position and the posture of the monocular camera can be accurately obtained by tracking the feature points in the adjacent image frames.
Further, the step between the step S12 and the step S13 further includes the steps of:
and S121, checking the correctness of the feature points by an affine checking method, judging whether the number of the feature points is sufficient, removing the feature points with wrong correctness verification, and adding new feature points when the number of the feature points is insufficient.
As can be seen from the above description, after the feature points are extracted, the method of the invention checks the correctness of the feature points by an affine check method, and removes the wrong feature points, so that the position and the posture of the monocular camera determined subsequently are more accurate.
Further, the first positioning data includes first position information, first speed information, and first attitude information;
the step S2 specifically includes the steps of:
s21, receiving the angular velocity, the acceleration and the gravity vector of the three axes of the inertial measurement unit during the static state, integrating the angular velocity, the acceleration and the gravity vector within a preset time to obtain first position information and first velocity information, and obtaining first attitude information according to the update of quaternion;
s22, establishing a position error equation and an attitude error equation according to the first position information, the first speed information, the first attitude information and the position and attitude estimated by the monocular camera within the preset time;
and S23, performing optimization solution on the position error equation and the attitude error equation, and performing optimization compensation on the first position information and the first attitude information according to a calculation result.
From the above description, it can be known that the first position information and the first attitude information calculated by the data collected by the inertial measurement unit are optimally compensated according to the position and attitude information estimated by the monocular camera, that is, the error drift of the inertial measurement unit is limited by using the visual measurement, so as to obtain the position and attitude data with higher precision and credibility.
Further, the calculating of the real-time differential positioning by the global navigation satellite system further comprises the following steps: obtaining a first pseudo-range double-difference model and a first phase double-difference model between a receiver and a satellite according to the collected pseudo-range, carrier and Doppler observed values, and performing whole-cycle ambiguity resolution;
the step S3 specifically includes the steps of:
s31, receiving second positioning data of an inertial navigation system, judging whether the pseudo range, the carrier wave and the Doppler observed value acquired by the global navigation satellite system in the integer ambiguity solution are missing due to signal interruption, if so, carrying out linearization processing on the first pseudo range double difference model and the first phase double difference model at the interruption position according to the second positioning data, estimating an optimal floating ambiguity solution by a least square method in combination with historical ambiguity and covariance, and calculating by an LAMBDA (label-based ambiguity resolution) method to obtain the integer ambiguity solution;
and S32, continuing resolving the multi-frequency multi-mode real-time differential positioning according to the integer ambiguity solution, and finally obtaining the third positioning data.
According to the above description, when the micro signal of the global navigation satellite system is interrupted and lost, the positioning data of the inertial navigation system is linearly processed, and the integer ambiguity solution is calculated by the least square method and the LAMBDA method, so that the defect that the global navigation satellite system is easily unlocked by the signal affected by the environment is overcome, and the positioning accuracy is improved.
Further, the steps between S31 and S32 further include the steps of:
s311, carrying out correctness verification on the integer ambiguity solution, carrying out statistical analysis on hypothesis test statistics according to historical integer ambiguity solutions stored when global navigation satellite system signals are normal, and calculating to obtain an ambiguity threshold value under a preset confidence level;
and S312, judging whether the integer ambiguity solution falls into the range of the ambiguity threshold, if so, checking the integer ambiguity solution correctly, otherwise, checking the integer ambiguity solution incorrectly, and calculating the integer ambiguity solution by adopting a pseudo-range measurement method.
From the above description, the present invention also adopts a hypothesis testing method to determine whether ambiguity solution is available, so as to improve the reliability of ambiguity solution.
Further, the first positioning data includes first position information, first speed information, and first attitude information;
the step S4 specifically includes the steps of:
s41, constructing a second pseudorange double-difference model according to the first position information, respectively calculating a first double-difference pseudorange, a second double-difference pseudorange, a first double-difference pseudorange rate and a second double-difference pseudorange rate according to the first pseudorange double-difference model and the second pseudorange double-difference model, and constructing a measurement equation according to an error between the first double-difference pseudorange and the second double-difference pseudorange and an error between the first double-difference pseudorange rate and the second double-difference pseudorange rate as observed quantities of data fusion;
s42, establishing a state equation according to the first position information, the first speed information and the first posture information, wherein the state equation comprises a position equation, a speed equation and a posture equation.
Further, the state quantity errors include a speed error, an attitude error, and a position error;
the step S5 specifically includes the steps of:
s51, estimating the speed error, the attitude error and the position error by adopting an extended Kalman filter algorithm according to the first position information, the first speed information, the first attitude information, the measurement equation, a position equation, a speed equation and an attitude equation;
and S52, respectively performing compensation optimization on the first position information, the first speed information and the first attitude information according to the position error, the speed error and the attitude error to obtain final positioning data.
According to the technical scheme, the positioning data of the inertial measurement unit, the positioning data of the global navigation satellite system and the measurement equation and the state equation of the inertial measurement unit are optimized based on monocular vision, each error of the state quantity is estimated by adopting an extended Kalman filtering algorithm, and the position, speed and attitude information obtained by the inertial measurement unit is compensated, so that high-precision information is obtained, and the positioning accuracy is improved.
Further, the step S5 is followed by the step of:
and S6, updating the final positioning data to the first positioning data of the inertial measurement unit.
As can be seen from the above description, the position, velocity, and attitude information with higher accuracy obtained finally is updated to the initial values estimated by the inertial measurement unit, so that the influence of the drift of the inertial measurement unit is reduced.
Referring to fig. 2, a converged positioning terminal includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor, and when the processor executes the computer program, the steps of the above scheme are implemented.
The fusion positioning method and the terminal are suitable for a scene needing positioning, but because the operation site is complex, the global navigation satellite system is often shielded and subjected to electromagnetic interference to generate satellite signal interruption, so that the positioning data is not accurate enough.
Referring to fig. 1 and fig. 3, a first embodiment of the present invention is:
a fusion localization method, comprising the steps of:
s1, acquiring environment information from the monocular camera, and estimating the position and the posture of the monocular camera according to the environment information;
the step S1 specifically includes the steps of:
s11, receiving environment information of the monocular camera, and preprocessing the environment information;
s12, extracting characteristic points of Harris angular point characteristics and SIFT scale invariant characteristics from a continuous image of the environmental information, and tracking the characteristic points in adjacent images by adopting an LK sparse optical flow algorithm;
the method between the step S12 and the step S13 further comprises the steps of:
s121, checking the correctness of the feature points by an affine checking method, judging whether the number of the feature points is sufficient, removing the feature points with wrong correctness checking, and adding new feature points when the number of the feature points is insufficient;
s13, estimating the relative motion of the landmark points and the monocular camera according to the feature points in the adjacent images, and estimating the position and the posture of the monocular camera by combining the relative motion of the landmark points and the monocular camera.
In this embodiment, the environment map is recognized by the monocular camera, and information collected by the monocular camera is read and preprocessed, including filtering, correction, and grayscale processing. Extracting Harris angular point characteristics and SIFT scale invariant characteristics from a continuous section of adjacent frame images, tracking the adjacent frame characteristics by adopting an LK sparse optical flow algorithm, simultaneously detecting the correctness of characteristic points by adopting an affine test method, removing the characteristic points with tracking errors, and adding new characteristic points when the number of the characteristic points is insufficient. And then estimating a landmark point and the relative motion of the monocular camera according to the feature points in the adjacent images, and estimating the position and the posture of the monocular camera by combining the landmark point and the relative motion of the monocular camera to obtain the visual odometer. The visual odometer includes position, attitude, landmark point coordinates, and relative motion.
S2, acquiring first positioning data from an inertia measurement unit, and optimizing the first positioning data according to the position and the posture of the monocular camera;
the first positioning data comprises first position information, first speed information and first posture information;
the step S2 specifically includes the steps of:
s21, receiving the angular velocity, the acceleration and the gravity vector of the three axes of the inertial measurement unit during the static state, integrating the angular velocity, the acceleration and the gravity vector within a preset time to obtain first position information and first velocity information, and obtaining first attitude information according to the update of quaternion;
s22, establishing a position error equation and an attitude error equation according to the first position information, the first speed information, the first attitude information and the position and attitude estimated by the monocular camera within the preset time;
and S23, performing optimization solution on the position error equation and the attitude error equation, and performing optimization compensation on the first position information and the first attitude information according to a calculation result.
In this embodiment, the MEMS-IMU (inertial measurement unit, IMU) may measure the angular velocity and acceleration of the three axes and the gravity vector at rest, integrate them in a short time to obtain the position and velocity information, and obtain the attitude information according to the update of the quaternion. And between continuous frame images, acquiring the position, the speed and the rotation relative to an inertial space by adopting IMU data, establishing an error equation by combining the position and the attitude estimated by the monocular camera, and performing optimization solution on the error equation to compensate an integral term of the IMU, so as to obtain optimized first position information and first attitude information.
Wherein, the quaternion updating equation is as follows:
and estimating the position, the speed and the rotation between two corresponding adjacent images according to the data acquired by the monocular camera and the data acquired by the IMU.
Wherein the content of the first and second substances,
representing the position of a world coordinate system k frame image IMU;
representing the speed of a world coordinate system k frame image IMU; Δ tkThe time step of k frame images;
represents the acceleration of the IMU measurement;
representing an acceleration deviation; gwRepresents the acceleration of gravity;
representing the angular velocity of the IMU measurement;
indicating an angular velocity deviation;
representing the quaternion of the IMU under k frame images.
Namely, pre-integrating data acquired by the corresponding IMU between two adjacent images:
wherein the content of the first and second substances,
showing the relative displacement and relative velocity estimated between the k frame image and the k +1 frame image,
and
for the pre-integral term, the relative displacement, speed and rotation between two image frames are reflected:
wherein the content of the first and second substances,
representing a left-hand matrix form of a quaternion multiplication operation.
Establishing an equation according to a pre-integral term:
wherein, I3×1Representing a 3 by 1 unit matrix;
representing a rotation matrix of the camera system to the IMU system;
representing the displacement calculated for k +1 frame images of the camera.
Expressed as:
among multiple frame images, can be expressed as solving a least squares equation:
wherein argmin represents taking the minimum value; Γ denotes all image frames in the sliding window.
In this way, the motion speed of each frame image can be obtained
The gravity vector is
And a visual scale factor S. And calculating an attitude angle according to the included angle between the gravity vector and each axis of the sensor.
The estimation of the IMU is as follows:
xk+1＝Axk
xkposition velocity acceleration at time K:
xk＝[L B H vE vN vU ae an au]；
wherein subscript E, E denotes the ENU coordinate system E direction; l B H vE vN vU ae an auLongitude, latitude, height, E-direction speed, N-direction speed, U-direction speed, E-direction acceleration, N-direction acceleration and U-direction acceleration.
The value of A is:
wherein R isMIs the curvature radius of the mortise-tenon unitary ring, RNΔ t is the interval between the last time and the current time, which is the radius of curvature of the meridian.
And the attitude calculation adopts a quaternion updating method, and then an attitude angle is calculated according to the quaternion:
wherein gamma, theta and psi are respectively the attitude of pitch, roll and courseAngle, quaternion q ═ q (q)0，q1，q2，q3)。
S3, second positioning data are obtained from an inertial navigation system, multi-frequency multi-mode real-time differential positioning is carried out on the global navigation satellite system assisted by the second positioning data, and third positioning data are obtained;
the global navigation satellite system further comprises the following steps when resolving the real-time differential positioning: obtaining a first pseudo-range double-difference model and a first phase double-difference model between a receiver and a satellite according to the collected pseudo-range, carrier and Doppler observed values, and performing whole-cycle ambiguity resolution;
the step S3 specifically includes the steps of:
s31, receiving second positioning data of an inertial navigation system, judging whether the pseudo range, the carrier wave and the Doppler observed value acquired by the global navigation satellite system in the integer ambiguity solution are missing due to signal interruption, if so, carrying out linearization processing on the first pseudo range double difference model and the first phase double difference model at the interruption position according to the second positioning data, estimating an optimal floating ambiguity solution by a least square method in combination with historical ambiguity and covariance, and calculating by an LAMBDA (label-based ambiguity resolution) method to obtain the integer ambiguity solution;
the steps between S31 and S32 further comprise the steps of:
s311, carrying out correctness verification on the integer ambiguity solution, carrying out statistical analysis on hypothesis test statistics according to historical integer ambiguity solutions stored when global navigation satellite system signals are normal, and calculating to obtain an ambiguity threshold value under a preset confidence level;
and S312, judging whether the integer ambiguity solution falls into the range of the ambiguity threshold, if so, checking the integer ambiguity solution correctly, otherwise, checking the integer ambiguity solution incorrectly, and calculating the integer ambiguity solution by adopting a pseudo-range measurement method.
And S32, continuing resolving the multi-frequency multi-mode real-time differential positioning according to the integer ambiguity solution, and finally obtaining the third positioning data.
In this embodiment, in GNSS (global navigation satellite system) solution, a pseudo-range and a phase double difference model between a receiver and a satellite are obtained by using a pseudo-range, a carrier, and a doppler observation value as raw data. In the integer ambiguity solution, based on the characteristic that GNSS signals are easily interfered by the outside and INS (inertial navigation system) is not easily interfered by the outside, position information output by the INS is used for assisting the GNSS to fix the integer ambiguity, when the GNSS information is interrupted, a pseudo range and phase double difference model is subjected to linearization processing at the position calculated by the INS, the optimal floating ambiguity solution is estimated by a least square method in combination with historical ambiguity and covariance, then an LAMBDA method is adopted to calculate an integer ambiguity vector, and correctness is verified.
Specifically, upon initiation of the system in the presence of GNSS signal disruptions, the INS provides high-rate navigation information output including position, velocity, and attitude (PVA). Since the base station of the multi-frequency GNSS is fixed and the geographical position is known, after the base station and the rover station receive the multi-frequency GNSS signals together and the rover station receives the base station information, the position of the rover station can be determined by using a positioning method of RTK (real time differential positioning). According to the RTK principle, double-difference pseudo range and carrier phase observation are formed on the premise that a reference value carrier phase observation value received by a mobile station is available. And calculating double-difference floating-point fuzzy solutions and corresponding covariance optimal solutions by using least square adjustment to obtain position constraint. And then, resolving ambiguity by adopting an LAMBDA method, carrying out hypothesis test to verify the correctness of the ambiguity solution, namely correctness check, storing the ambiguity resolved by the LAMBDA when the GNSS signal is normal, then calculating a reasonable threshold under a certain confidence level condition according to the statistical analysis of general hypothesis test statistics, and determining whether the searched ambiguity should be accepted or not according to the comparison with the threshold. If the correctness check is passed, the current data is used for the subsequent steps, otherwise, the pseudorange measurements are used.
S4, establishing a measurement equation and a state equation according to the first positioning data and the third positioning data;
the step S4 specifically includes the steps of:
s41, constructing a second pseudorange double-difference model according to the first position information, respectively calculating a first double-difference pseudorange, a second double-difference pseudorange, a first double-difference pseudorange rate and a second double-difference pseudorange rate according to the first pseudorange double-difference model and the second pseudorange double-difference model, and constructing a measurement equation according to an error between the first double-difference pseudorange and the second double-difference pseudorange and an error between the first double-difference pseudorange rate and the second double-difference pseudorange rate as observed quantities of data fusion;
s42, establishing a state equation according to the first position information, the first speed information and the first posture information, wherein the state equation comprises a position equation, a speed equation and a posture equation.
In this embodiment, the GNSS and the IMU are tightly combined to establish a measurement equation and a state equation. A pseudo-range double-difference model of inertial navigation can be constructed based on the position calculated by the IMU, double-difference pseudo-range and double-difference pseudo-range rate are obtained by respectively calculating GNSS and IMU data, and an error between the double-difference pseudo-range and the double-difference pseudo-range rate is used as observed quantity of data fusion to construct a measurement equation.
S5, estimating a state quantity error by adopting an extended Kalman filtering algorithm according to the first positioning data, the measurement equation and the state equation, and compensating the first positioning data based on the state quantity error to obtain final positioning data.
The state quantity errors comprise speed errors, attitude errors and position errors;
the step S5 specifically includes the steps of:
s51, estimating the speed error, the attitude error and the position error by adopting an extended Kalman filter algorithm according to the first position information, the first speed information, the first attitude information, the measurement equation, a position equation, a speed equation and an attitude equation;
and S52, respectively performing compensation optimization on the first position information, the first speed information and the first attitude information according to the position error, the speed error and the attitude error to obtain final positioning data.
The step S5 is followed by the step of:
and S6, updating the final positioning data to the first positioning data of the inertial measurement unit.
In this embodiment, the state quantity error is an attitude error, a velocity error, a position error, a gyroscope error, an accelerometer error, a clock error equivalent distance error, and a clock drift equivalent pseudo range rate error. Based on the IMU data after monocular vision optimization, the GNSS and the measurement equation and the state equation of the IMU, the extended Kalman filtering algorithm is adopted to estimate each error of the state quantity, and finally, the position, the speed and the attitude are compensated to obtain information with higher precision. Meanwhile, the obtained high-precision position, speed and posture are used for updating the IMU calculation initial value, and the drift influence of the IMU is reduced.
The state equation comprises an IMU error state equation and a GNSS error state equation, and the state equation formed by attitude error, speed error, position error, gyroscope error and accelerometer error is taken as an example, and is as follows:
wherein, WIFor random noise, XIIn the state:
wherein the content of the first and second substances,
respectively showing longitude error, latitude error, height error, E-direction speed error, N-direction speed error, U-direction speed error, E-axis misalignment angle error, N-axis misalignment angle error, U-axis misalignment angle error, x-axis acceleration error, y-axis acceleration error, z-axis acceleration error, x-axis angular speed error, y-axis angular speed error and z-axis angular speed error in sequence.
GIFor the noise transformation matrix:
wherein zero (3, 3) represents a 3X3 0 th order matrix,
a rotation matrix representing a carrier coordinate system to a world coordinate system;
FIfor the state transition matrix:
wherein R isMIs the curvature radius of the mortise-tenon unitary ring, RNRadius of curvature of meridian, wieIs ECEF based on the spin angular velocity of the earthe、fn、fuThe sum of the acceleration and the error in the directions e, n, and u, respectively.
The equations of the clock error equivalent distance error and the clock drift equivalent pseudorange rate error are as follows:
wherein:
XG＝[δtu δtru]T
WG＝[wtu wtru]T
wherein t isuIs the clock difference equivalent distance, truIs clock drift equivalent pseudo range rate, betatruFor error correlation time, wtu、wtruIs random white noise.
The measurement equation, namely the observation equation comprises an IMU error observation equation and a pseudo-range rate error observation equation, wherein the IMU observation equation is as follows:
ZI＝HIXI+VI
wherein Z isITo observe the vector, HIFor the observation matrix, VITo observe the error:
wherein eye (3, 3) represents a 3X3 unit matrix, and zero (3, 3) represents a 3X3 0-order matrix.
The pseudo-range and pseudo-range rate error observation equation is as follows:
ZG＝HGXG+UG+VG
wherein the content of the first and second substances,
wherein, deltaxFor the difference between the position of the moving carrier estimated by the IMU and the satellite positions derived from the satellite ephemeris,
the difference between the velocity of the moving carrier estimated by the IMU and the satellite velocity obtained from the satellite ephemeris, VGThe pseudo range and the pseudo range rate observation error.
The method for estimating the state of the mobile terminal by using the extended Kalman filter is characterized in that the method comprises the following steps:
(1) and (3) carrying out linearization processing on the system model:
wherein f isk-1Representing a non-linear function; omegak-1Representing process noise; x is the number ofkThe true value at the time k is the true value,
is an estimated value of the state at the time k-1,
is the prediction error value at time k-1.
First order Taylor expansion:
wherein the content of the first and second substances,
representing the partial derivative of the non-linear function to state x,
representing the partial derivative of the non-linear function on the error w.
(2) And (3) performing one-step prediction on the time k through the state value of the time k-1:
the error generated by the one-step state prediction is:
the covariance matrix of the state prediction error is:
wherein, Pk-1|k-1State covariance matrix, Q, representing time k-1k-1Representing the error covariance at time k-1 and the superscript T representing the transpose.
(3) The linearized equation for the measurement at time k is:
wherein z iskRepresents the observed quantity, hkRepresenting an observation function, vkWhich is indicative of the observed noise,
representing the partial derivative of the observation function to state x,
representing the partial derivative of the observation function with respect to the noise v.
(4) One-step prediction of k-time measurement:
the measurement prediction error is:
the covariance matrix of the state prediction error and the measurement prediction error is:
(5) the update formula of the state filtering is as follows:
the state update formula:
covariance update formula:
wherein I represents a unit matrix.
The kalman gain K at time K is:
referring to fig. 2, the second embodiment of the present invention is:
a converged positioning terminal 1, comprising a processor 2, a memory 3 and a computer program stored in the memory 3 and operable on the processor 2, wherein the processor 2 implements the steps of the first embodiment when executing the computer program.
The main principle of the method is that data acquired by a multi-frequency multi-mode real-time differential positioning unit, an inertial measurement unit and a monocular camera are fused by adopting an extended Kalman algorithm to improve positioning accuracy.
In summary, the fusion positioning method and the terminal provided by the invention fuse the data acquired by the multi-frequency multi-mode real-time differential positioning, the inertial measurement unit and the monocular camera by using the extended kalman algorithm, integrate the advantages of the long-time absolute position of the global navigation satellite system and the short-time accurate calculation position of the IMU and the monocular vision to obtain the estimation of the optimal position state, recover the measurement scale of the monocular vision by using the IMU, improve the motion tracking performance, limit the error drift of the IMU by using the vision measurement, and obtain the position and the attitude with higher precision and credibility, thereby improving the positioning precision.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent changes made by using the contents of the present specification and the drawings, or applied directly or indirectly to the related technical fields, are included in the scope of the present invention.
Claims (10)
1. A fusion positioning method is characterized by comprising the following steps:
s1, receiving environment information of the monocular camera, and estimating the position and the posture of the monocular camera according to the environment information;
s2, receiving first positioning data of an inertia measurement unit, and optimizing the first positioning data according to the position and the posture of the monocular camera;
s3, receiving second positioning data of the inertial navigation system, and assisting the global navigation satellite system to perform multi-frequency multi-mode real-time differential positioning according to the second positioning data to obtain third positioning data;
s4, establishing a measurement equation and a state equation according to the first positioning data and the third positioning data;
s5, estimating a state quantity error by adopting an extended Kalman filtering algorithm according to the first positioning data, the measurement equation and the state equation, and compensating the first positioning data based on the state quantity error to obtain final positioning data.
2. The fusion positioning method according to claim 1, wherein the step S1 specifically includes the steps of:
s11, receiving environment information of the monocular camera, and preprocessing the environment information;
s12, extracting characteristic points of Harris angular point characteristics and SIFT scale invariant characteristics from a continuous image of the environmental information, and tracking the characteristic points in adjacent images by adopting an LK sparse optical flow algorithm;
s13, estimating the relative motion of the landmark points and the monocular camera according to the feature points in the adjacent images, and estimating the position and the posture of the monocular camera by combining the relative motion of the landmark points and the monocular camera.
3. The fusion positioning method as claimed in claim 2, further comprising, between the step S12 and the step S13:
and S121, checking the correctness of the feature points by an affine checking method, judging whether the number of the feature points is sufficient, removing the feature points with wrong correctness verification, and adding new feature points when the number of the feature points is insufficient.
4. The fused positioning method according to claim 1, wherein the first positioning data comprises first position information, first velocity information and first attitude information;
the step S2 specifically includes the steps of:
s21, receiving the angular velocity, the acceleration and the gravity vector of the three axes of the inertial measurement unit during the static state, integrating the angular velocity, the acceleration and the gravity vector within a preset time to obtain first position information and first velocity information, and obtaining first attitude information according to the update of quaternion;
s22, establishing a position error equation and an attitude error equation according to the first position information, the first speed information, the first attitude information and the position and attitude estimated by the monocular camera within the preset time;
and S23, performing optimization solution on the position error equation and the attitude error equation, and performing optimization compensation on the first position information and the first attitude information according to a calculation result.
5. The fused positioning method according to claim 1, wherein the global navigation satellite system performs the solution of real-time differential positioning, and further comprising the steps of: obtaining a first pseudo-range double-difference model and a first phase double-difference model between a receiver and a satellite according to the collected pseudo-range, carrier and Doppler observed values, and performing whole-cycle ambiguity resolution;
the step S3 specifically includes the steps of:
s31, receiving second positioning data of an inertial navigation system, judging whether the pseudo range, the carrier wave and the Doppler observed value acquired by the global navigation satellite system in the integer ambiguity solution are missing due to signal interruption, if so, carrying out linearization processing on the first pseudo range double difference model and the first phase double difference model at the interruption position according to the second positioning data, estimating an optimal floating ambiguity solution by a least square method in combination with historical ambiguity and covariance, and calculating by an LAMBDA (label-based ambiguity resolution) method to obtain the integer ambiguity solution;
and S32, continuing resolving the multi-frequency multi-mode real-time differential positioning according to the integer ambiguity solution, and finally obtaining the third positioning data.
6. The fusion positioning method as claimed in claim 5, further comprising, between the steps S31 and S32:
s311, carrying out correctness verification on the integer ambiguity solution, carrying out statistical analysis on hypothesis test statistics according to historical integer ambiguity solutions stored when global navigation satellite system signals are normal, and calculating to obtain an ambiguity threshold value under a preset confidence level;
and S312, judging whether the integer ambiguity solution falls into the range of the ambiguity threshold, if so, checking the integer ambiguity solution correctly, otherwise, checking the integer ambiguity solution incorrectly, and calculating the integer ambiguity solution by adopting a pseudo-range measurement method.
7. The fused positioning method according to claim 5, wherein the first positioning data comprises first position information, first velocity information and first attitude information;
the step S4 specifically includes the steps of:
s41, constructing a second pseudorange double-difference model according to the first position information, respectively calculating a first double-difference pseudorange, a second double-difference pseudorange, a first double-difference pseudorange rate and a second double-difference pseudorange rate according to the first pseudorange double-difference model and the second pseudorange double-difference model, and constructing a measurement equation according to an error between the first double-difference pseudorange and the second double-difference pseudorange and an error between the first double-difference pseudorange rate and the second double-difference pseudorange rate as observed quantities of data fusion;
s42, establishing a state equation according to the first position information, the first speed information and the first posture information, wherein the state equation comprises a position equation, a speed equation and a posture equation.
8. The fusion positioning method according to claim 7, wherein the state quantity errors comprise a velocity error, an attitude error and a position error;
the step S5 specifically includes the steps of:
s51, estimating the speed error, the attitude error and the position error by adopting an extended Kalman filter algorithm according to the first position information, the first speed information, the first attitude information, the measurement equation, a position equation, a speed equation and an attitude equation;
and S52, respectively performing compensation optimization on the first position information, the first speed information and the first attitude information according to the position error, the speed error and the attitude error to obtain final positioning data.
9. The fusion positioning method according to claim 1, further comprising, after the step S5, the steps of:
and S6, updating the final positioning data to the first positioning data of the inertial measurement unit.
10. A converged positioning terminal, comprising a processor, a memory and a computer program stored in the memory and executable on the processor, characterised in that the processor, when executing the computer program, implements the steps of a converged positioning method according to any one of claims 1 to 9.
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