CN110906923A - Vehicle-mounted multi-sensor tight coupling fusion positioning method and system, storage medium and vehicle - Google Patents

Vehicle-mounted multi-sensor tight coupling fusion positioning method and system, storage medium and vehicle Download PDF

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CN110906923A
CN110906923A CN201911192614.3A CN201911192614A CN110906923A CN 110906923 A CN110906923 A CN 110906923A CN 201911192614 A CN201911192614 A CN 201911192614A CN 110906923 A CN110906923 A CN 110906923A
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vehicle
data
imu
laser radar
positioning
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CN110906923B (en
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肖乔木
刘紫扬
熊周兵
陈卓
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Chongqing Changan Automobile Co Ltd
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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
    • G01S17/87—Combinations of systems using electromagnetic waves other than radio waves
    • 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
    • G01S17/87—Combinations of systems using electromagnetic waves other than radio waves
    • G01S17/875—Combinations of systems using electromagnetic waves other than radio waves for determining attitude
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Abstract

The invention discloses a vehicle-mounted multi-sensor tight coupling fusion positioning method, a system, a storage medium and a vehicle, wherein the method comprises the following steps: step 1, acquiring laser radar, GPS, IMU and chassis data, and performing time synchronization and space synchronization on the laser radar, the IMU, the GPS and the chassis data; step 2, tightly coupling the laser radar data and the IMU data, and performing matching calculation to obtain the position and the posture of the vehicle; step 3, performing kinematic calculation on the vehicle chassis data to obtain the vehicle position and posture; step 4, acquiring GPS positioning data, and carrying out UTM conversion on the GPS data to obtain the vehicle position posture; and 5, establishing a Kalman state and an observation model, and performing fusion operation to obtain a final positioning result of the vehicle. The invention can ensure that the automatic driving automobile can still stably obtain the positioning data with higher precision in a higher-speed motion state or in a severe environment.

Description

Vehicle-mounted multi-sensor tight coupling fusion positioning method and system, storage medium and vehicle
Technical Field
The invention belongs to the technical field of automobile positioning, and particularly relates to a vehicle-mounted multi-sensor tight coupling fusion positioning method, a system, a storage medium and a vehicle.
Background
The automatic driving automobile relates to various technologies, wherein the core technology positioning is to solve the problem of 'where the automobile is', extremely high requirements are provided for reliability, stability and functional safety, and the positioning precision required by the automatic driving automobile needs to reach centimeter-level positioning. Currently, the positioning technology adopted by an automatic driving automobile can be roughly divided into three types according to the technical principle, wherein the first type is signal-based positioning, namely GNSS positioning, namely a global navigation positioning system; the second type is based on dead reckoning, which represents dead reckoning by means of automobile chassis data, namely, the current position and course are deduced according to the position and course at the previous moment; the third type is positioning based on environmental feature matching, and the representative is a relative positioning mode based on a laser radar and a high-precision map;
the three modes have respective defects, and based on the positioning of the signals, the positioning result is extremely deviated in places where the signals can not be covered, such as tunnels, complex urban environments and the like; positioning based on dead reckoning can accumulate over time to generate integral errors, so that inaccurate positioning is caused; based on the matched positioning, depending on the environmental characteristics, when the environmental change is obvious, the positioning error is large.
Therefore, it is necessary to develop a vehicle-mounted multi-sensor close-coupled fusion positioning method and system.
Disclosure of Invention
The invention aims to provide a vehicle-mounted multi-sensor tight coupling fusion positioning method, a system, a storage medium and a vehicle, which can ensure that the positioning data with higher precision can still be stably obtained in a higher-speed motion state or a severe environment of an automatic driving automobile.
The invention relates to a vehicle-mounted multi-sensor tight coupling fusion positioning method, which comprises the following steps:
step 1, acquiring laser radar, GPS, IMU and chassis data, and performing time synchronization and space synchronization on the laser radar, the IMU, the GPS and the chassis data;
step 2, carrying out tight coupling processing on the laser radar data and the IMU data, and specifically comprising the following steps:
(2-1) judging whether the current iteration times are the first time;
if yes, the IMU state is not updated;
if not, updating the IMU state according to the vehicle pose state error output by the last iteration;
(2-2) integrating the IMU data to obtain a pose state value relative to the IMU;
(2-3) when the laser radar data arrive, performing de-migration calculation on the laser radar data based on the pose data obtained by the IMU to obtain a predicted laser radar pose;
(2-4) extracting and calculating characteristic points of the laser radar data;
(2-5) matching the extracted laser radar characteristic points to a pre-established local map;
(2-6) obtaining a pose measurement value relative to the laser radar according to the matching result;
(2-7) carrying out combined nonlinear optimization, and obtaining MAP state estimation in a local window according to the pose measurement value relative to the laser radar and the pose state value relative to the IMU to obtain a vehicle pose state error;
(2-8) feeding back the optimized vehicle pose state error to the IMU pose state calculation in the step (2-1), and carrying out coordinate transformation to obtain a position and pose value of the vehicle;
step 3, performing kinematic calculation on the vehicle chassis data to obtain a position attitude value of the vehicle;
step 4, acquiring GPS positioning data, and carrying out UTM conversion on the GPS data to obtain a position attitude value of the vehicle;
and 5, establishing a Kalman state and an observation model, taking a vehicle positioning result obtained by matching calculation after the laser data and the IMU are tightly coupled and a vehicle positioning result obtained by converting the GPS into UTM as a positioning observation value, taking a vehicle positioning result obtained by calculating vehicle chassis data as a positioning state measurement value, and carrying out fusion operation on the Kalman state and the observation model to obtain a final positioning result of the vehicle.
Further, in the step 1, spatial synchronization refers to calibration through pre-measured vehicle installation external parameters and internal parameters of the sensor;
the time synchronization refers to calibration based on GPS time service and soft time of an operating system.
Further, step 3 specifically includes acquiring a vehicle wheel pulse signal and a vehicle wheel rotation angle signal, and calculating according to a four-wheel differential motion model.
The invention relates to a vehicle-mounted multi-sensor tight coupling fusion positioning system which comprises four laser radars, a GPS receiving box, an IMU sensor, a controller and a memory, wherein the four laser radars are respectively arranged on the front, the back, the left and the right of a vehicle; the method is characterized in that: the controller is used for realizing the vehicle-mounted multi-sensor close coupling fusion positioning method when the executable program stored in the memory is executed.
The storage medium stores an executable program, and when the executable program is executed by a processor, the vehicle-mounted multi-sensor tight coupling fusion positioning method is realized.
The vehicle adopts the vehicle-mounted multi-sensor tight coupling fusion positioning system.
The invention has the following advantages: by tightly coupling the lidar data and the IMU data, an accurate state estimation value can be output at a higher IMU update frequency, and an error range can be maintained within a receivable range even in the case where the lidar data is degraded for a long period of time. Meanwhile, in order to enhance the robustness and consistency of the positioning system, the rear end is accessed with GPS data and chassis calculation data for fusion positioning, so that a more stable positioning result is obtained. The method has higher calculation efficiency, is reliable and feasible, and ensures that the automatic driving automobile can still stably obtain the positioning data with higher precision in a higher-speed motion state or a severe environment, so that the automatic driving automobile is safer and more reliable.
Drawings
FIG. 1 is a flow chart of the present invention;
fig. 2 is a schematic view of the installation of the sensor of the present invention.
Detailed Description
The invention will be further explained with reference to the drawings.
As shown in fig. 1, a vehicle-mounted multi-sensor tight coupling fusion positioning method includes the following steps:
step 1, acquiring Lidar (namely Lidar), GPS, IMU and chassis data, and performing time synchronization and space synchronization on the Lidar, the IMU, the GPS and the chassis data. The spatial synchronization refers to calibration through pre-measured vehicle installation external parameters and internal parameters of the sensor. The time synchronization refers to calibration based on GPS time service and soft time of an operating system.
Step 2, carrying out tight coupling processing on the laser radar data and the IMU data, and specifically comprising the following steps:
(2-1) judging whether the current iteration times are the first time;
if yes, the IMU state is not updated;
if not, updating the IMU state according to the vehicle pose state error output by the last iteration;
and (2-2) integrating the IMU data to obtain a pose state value relative to the IMU.
And (2-3) when the laser radar data arrives, performing de-migration calculation on the laser radar data based on the pose data obtained by the IMU to obtain the predicted laser radar pose.
And (2-4) carrying out feature point extraction calculation on the laser radar data.
And (2-5) matching the extracted laser radar characteristic points into a pre-established local map.
(2-6) obtaining a pose measurement value relative to the laser radar according to the matching result;
(2-7) carrying out combined nonlinear optimization, and obtaining MAP state estimation in a local window according to the pose measurement value relative to the laser radar and the pose state value relative to the IMU to obtain a vehicle pose state error;
(2-8) feeding back the optimized vehicle pose state error to the IMU pose state calculation in the step (2-1), and carrying out coordinate transformation to obtain a position and pose value of the vehicle;
step 3, performing kinematic calculation on the vehicle chassis data to obtain a position attitude value of the vehicle; the method specifically comprises the following steps: and acquiring a vehicle wheel pulse signal and a vehicle wheel turning angle signal, and calculating according to a four-wheel differential motion model.
And 4, acquiring GPS positioning data, and carrying out UTM conversion on the GPS data, namely carrying out conversion from longitude and latitude to a local positioning coordinate according to the GPS data.
And 5, establishing a Kalman state and an observation model, taking a vehicle positioning result obtained by matching calculation after the laser data and the IMU are tightly coupled and a vehicle positioning result obtained by converting the GPS into UTM as a positioning observation value, taking a vehicle positioning result obtained by calculating vehicle chassis data as a positioning state measurement value, and carrying out fusion operation on the Kalman state and the observation model to obtain a final positioning result of the vehicle.
In the present embodiment, the first and second electrodes are,
(a) the state of the IMU represents:
Figure BDA0002293950690000041
Figure BDA0002293950690000042
wherein the content of the first and second substances,
Figure BDA0002293950690000043
is the state vector of the IMU and,
Figure BDA0002293950690000044
in the form of a position vector, the position vector,
Figure BDA0002293950690000045
in the form of a velocity vector, the velocity vector,
Figure BDA0002293950690000046
in order to be the attitude vector,
Figure BDA0002293950690000047
is the zero-offset error vector of the acceleration,
Figure BDA0002293950690000048
is the zero-offset error vector of the acceleration,
Figure BDA0002293950690000049
a pose homogeneous transformation matrix is obtained;
(b) updating the state of the IMU:
Figure BDA00022939506900000410
Figure BDA00022939506900000411
Figure BDA00022939506900000412
wherein p isjIs the position value at time j, piIs the position value at time i, vkSpeed value at time k, Δ t being the difference between two times, gWIs a value of gravitational acceleration, RkIs the attitude rotation matrix at the time of the k-th time,
Figure BDA0002293950690000051
is the acceleration value at the time point k,
Figure BDA0002293950690000052
the acceleration noise value at time k.
(c) And (3) carrying out de-migration calculation on the laser radar data:
in the embodiment, the IMU measurement value is adopted to eliminate the intra-frame offset of the laser radar, firstly, the laser radar is assumed to be in linear motion in the process of collecting the laser radar data, and then linear interpolation is carried out according to the pose of the vehicle when the collection of the S1 frame is started and the pose when the collection of the S1 frame is ended.
(d) Extracting characteristic points of laser radar data:
the points of one scanning are classified by curvature values, the points with the curvature larger than the threshold value of the characteristic point are edge points, and the points with the curvature smaller than the threshold value of the characteristic point are plane points. In order to distribute the feature points evenly in the environment, one scan is divided into 4 independent sub-regions, each providing at most 2 edge points and 4 planes. In addition, unstable characteristic points (blemishes) were excluded.
(e) In order to merge the pre-integration values of the IMU, the position and attitude of the lidar are constrained using relative measurements of the lidar, and a local map is established prior to finding the correspondence of the points, because for calculating an accurate correspondence, the points of a single frame of data are not dense enough, the local map contains N discrete time-stamped feature points, and the coordinate system is consistent with the lidar.
(f) After the local map is established, calculating the matching of the laser radar key frame and the local map, outputting the pose data of the optimized key frame, and then updating the output positioning data according to the incremental laser radar pose:
pj＝pi+Δp
Figure BDA0002293950690000053
wherein p isjIs the position value at time j, piIs the position value at time i, deltap is the position increment value between two times,
qjis the attitude angle at time j, σ θzAs an attitude angle incremental value, qiIs the attitude angle at time i.
(g) The joint nonlinear optimization is to obtain an optimized state, a fixed delay smoother and marginalization are used, the fixed delay smoother keeps N states in a window for smooth calculation, and the calculated state value is fed back to the IMU state estimation of the first step to prevent the IMU state from diverging.
In this embodiment, the four-wheel differential motion model specifically includes:
Angt＝Angt-1+at
Odom_xt＝Odom_xt-1+dt*cos(Angt)
Odom_yt＝Odom_yt-1+dt*cos(Angt)
wherein, AngtIs the vehicle heading angle, Odom _ xtIs the longitudinal X-axis position of the vehicle, Odom _ ytFor the transverse Y-axis position of the vehicle, dtSubtracting the left wheel walking distance from the right wheel walking distance;
in this embodiment, the kalman state and the observation model are specifically:
Figure BDA0002293950690000061
Pt＝FPt-1FT+Q；
Kt＝PtHT(HPtHT+R)-1；
Figure BDA0002293950690000062
Ptt＝(I-KtH)Pt；
wherein the content of the first and second substances,
Figure BDA0002293950690000063
for the predicted state vector at time t,
Figure BDA0002293950690000064
is the predicted state vector at time t-1, F is the state transition matrix, FTIs a transposed matrix of F, R is an attitude rotation matrix, B is a control matrix, and represents a control quantity ut-1The influence on the current state, Q is the error matrix, H is the state observation matrix, HTTransposed matrix of H, KtIs a Kalman gain coefficient, ztIs the observation vector at time t, PtFor co-operation at time tVariance matrix, Pt-1Is the covariance matrix at time t-1,
Figure BDA0002293950690000065
for fusing the corrected state vector, P, at time tttAnd (4) fusing and correcting the covariance matrix at the time t, wherein I is an identity matrix.
As shown in fig. 2, in the present embodiment, an on-vehicle multi-sensor close-coupled fusion positioning system includes four laser radars respectively installed on the front, back, left, and right of a vehicle, a GPS receiving box and an IMU sensor installed on the vehicle, a controller for receiving laser radar data, GPS data, IMU data, and chassis data, and a memory for storing an executable program; the method is characterized in that: the controller is used for realizing the vehicle-mounted multi-sensor close coupling fusion positioning method in the embodiment by executing the executable program stored in the memory.
In this embodiment, a storage medium stores an executable program, and when the executable program is executed by a processor, the method for positioning in a vehicle-mounted multi-sensor tight coupling fusion manner as described in this embodiment is implemented.
In this embodiment, a vehicle adopts the vehicle-mounted multi-sensor tight coupling fusion positioning system described in this embodiment.

Claims (6)

1. A vehicle-mounted multi-sensor tight coupling fusion positioning method comprises the following steps:
step 1, acquiring laser radar, GPS, IMU and chassis data, and performing time synchronization and space synchronization on the laser radar, the IMU, the GPS and the chassis data;
step 2, carrying out tight coupling processing on the laser radar data and the IMU data, and specifically comprising the following steps:
(2-1) judging whether the current iteration times are the first time;
if yes, the IMU state is not updated;
if not, updating the IMU state according to the vehicle pose state error output by the last iteration;
(2-2) integrating the IMU data to obtain a pose state value relative to the IMU;
(2-3) when the laser radar data arrive, performing de-migration calculation on the laser radar data based on the pose data obtained by the IMU to obtain a predicted laser radar pose;
(2-4) extracting and calculating characteristic points of the laser radar data;
(2-5) matching the extracted laser radar characteristic points to a pre-established local map;
(2-6) obtaining a pose measurement value relative to the laser radar according to the matching result;
(2-7) carrying out combined nonlinear optimization, and obtaining MAP state estimation in a local window according to the pose measurement value relative to the laser radar and the pose state value relative to the IMU to obtain a vehicle pose state error;
(2-8) feeding back the optimized vehicle pose state error to the IMU pose state calculation in the step (2-1), and carrying out coordinate transformation to obtain a position and pose value of the vehicle;
step 3, performing kinematic calculation on the vehicle chassis data to obtain a position attitude value of the vehicle;
step 4, acquiring GPS positioning data, and carrying out UTM conversion on the GPS data to obtain a position attitude value of the vehicle;
and 5, establishing a Kalman state and an observation model, taking a vehicle positioning result obtained by matching calculation after the laser data and the IMU are tightly coupled and a vehicle positioning result obtained by converting the GPS into UTM as a positioning observation value, taking a vehicle positioning result obtained by calculating vehicle chassis data as a positioning state measurement value, and carrying out fusion operation on the Kalman state and the observation model to obtain a final positioning result of the vehicle.
2. The vehicle-mounted multi-sensor close-coupled fusion positioning method according to claim 1, characterized in that: in the step 1, spatial synchronization refers to calibration through pre-measured vehicle installation external parameters and internal parameters of a sensor;
the time synchronization refers to calibration based on GPS time service and soft time of an operating system.
3. The vehicle-mounted multi-sensor close-coupled fusion positioning method according to claim 1 or 2, characterized in that: and step 3 specifically, acquiring a vehicle wheel pulse signal and a vehicle wheel corner signal, and calculating according to a four-wheel differential motion model.
4. A vehicle-mounted multi-sensor tight coupling fusion positioning system comprises four laser radars, a GPS receiving box, an IMU sensor, a controller and a memory, wherein the four laser radars are respectively arranged on the front, the back, the left and the right of a vehicle; the method is characterized in that: the controller is used for realizing the vehicle-mounted multi-sensor close-coupling fusion positioning method according to any one of claims 1 to 3 by executing the executable program stored in the memory.
5. A storage medium storing an executable program which, when executed by a processor, implements the in-vehicle multi-sensor close-coupled fusion positioning method according to any one of claims 1 to 4.
6. A vehicle, characterized in that: the vehicle-mounted multi-sensor tightly-coupled fusion positioning system of claim 5 is adopted.
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