CN112904396A - High-precision positioning method and system based on multi-sensor fusion - Google Patents

High-precision positioning method and system based on multi-sensor fusion Download PDF

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CN112904396A
CN112904396A CN202110147478.7A CN202110147478A CN112904396A CN 112904396 A CN112904396 A CN 112904396A CN 202110147478 A CN202110147478 A CN 202110147478A CN 112904396 A CN112904396 A CN 112904396A
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CN112904396B (en
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程敏
刘文博
罗作煌
闫宗涛
李勇兵
徐伟
李奇
朱超
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Shenzhen Yijiahe Technology R & D Co ltd
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    • 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
    • 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
    • G01S19/42—Determining position
    • G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
    • 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
    • G01S17/06—Systems determining position data of a target
    • 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
    • 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
    • G01S19/42—Determining position
    • G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
    • G01S19/49—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an inertial position system, e.g. loosely-coupled

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  • Radar, Positioning & Navigation (AREA)
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Abstract

The invention discloses a high-precision positioning method and a high-precision positioning system based on multi-sensor fusion, which comprise the following steps of: firstly, EKF filtering processing is carried out on a coded disc and IMU data to obtain a high-precision odometer, and then the high-precision odometer enters an SLAM module for fusion; and respectively constructing a coordinate alignment module and a coordinate conversion module, carrying out nonlinear optimization on the SLAM output position and the RTK output in the coordinate alignment module to obtain a coordinate conversion matrix of the coordinate alignment module, and loading the conversion matrix into the coordinate conversion module to convert the RTK-GNSS data and participate in the fusion of the SLAM output. In addition, a high-precision positioning system based on multi-sensor fusion is also provided. According to the invention, multiple sensors are adopted for data fusion, and the characteristic advantages of different sensors are utilized, so that in an environment which is not beneficial to a certain sensor, the data of other sensors are used for complementing, the positioning stability is effectively improved while the positioning accuracy is ensured, and safety accidents are reduced.

Description

High-precision positioning method and system based on multi-sensor fusion
Technical Field
The invention relates to a high-precision positioning method and system based on multi-sensor fusion, which are used for solving the problem of how to realize accurate positioning in the cruising process of various mobile robots or intelligent vehicles.
Background
The single navigation system has respective disadvantages, such as: GNSS positioning is carried out in places where signals cannot be covered, such as tunnels, complex urban environments and the like, and the positioning result is extremely deviated; the motion estimation positioning can accumulate along with time to generate integral errors, so that the positioning is inaccurate; SLAM (synchronous positioning and mapping) positioning mainly depends on environmental characteristics, and when environmental changes are obvious, positioning errors are large. Based on this, more and more multi-sensor fusion positioning schemes are beginning to be applied in positioning systems of mobile robots or vehicles.
However, in the existing multi-sensor fusion positioning scheme, the following technical problems exist: on one hand, IMU and chassis data are directly used for the SLAM module to predict the pose, and when any data of the IMU or the chassis code disc is wrong, the SLAM system is likely to be crashed, so that the unstable positioning phenomenon is caused.
On the other hand, in a high-precision positioning system (the positioning precision is required to be 1-2cm), a GNSS system in a non-RTK working mode or a GNSS without precise calibration is converted into a SLAM map coordinate system, which may cause a large error of the system, and even may cause a positioning system error to be larger than an unfused positioning error.
Therefore, the invention provides a high-precision positioning method and system based on multi-sensor fusion, on one hand, EKF is used for carrying out primary processing on IMU and coded disc data, and then EKF is transmitted into an SLAM module for pose prediction, so that the stability of the SLAM system is improved; on the other hand, by constructing an error model of SLAM positioning data and GNSS positioning data, a nonlinear optimization problem is solved, a high-precision coordinate transformation matrix is obtained, and the final fusion output can realize stable and high-precision positioning output.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides a high-precision positioning method and system based on multi-sensor fusion, which can effectively improve the positioning stability and the positioning precision, reduce the deployment difficulty and cost, ensure stable positioning operation in various environments and greatly reduce safety accidents.
The technical scheme is as follows: in order to achieve the above purpose, the invention provides a high-precision positioning method and system based on multi-sensor fusion, comprising the following steps:
s1, acquiring laser radar, GNSS, IMU and code disc data, and performing time synchronization on the laser radar, IMU, GNSS and code disc data;
s2, obtaining position state estimation of the carrier according to the IMU and the code disc data, and then entering the SLAM module for fusion to perform pose optimization;
an S3 initialization stage, wherein a coordinate alignment module is adopted to carry out nonlinear optimization on the SLAM output position and the GNSS output position to obtain a coordinate conversion matrix;
and S4, after the initialization is finished, performing coordinate conversion on the GNSS data according to the obtained coordinate conversion matrix, and fusing the GNSS data with the SLAM output position to obtain high-precision positioning information.
Further, the fusion process in step S2 specifically includes:
s2.1, an EKF fusion module is constructed, the input of the EKF fusion module is IMU and coded disc data, and the state variables are as follows:
x＝[p,q,v,w,a]
wherein the states p, q, v, w, a are respectively: position, attitude, velocity, angular velocity and acceleration, then constructing a motion model (the model is determined according to an actual motion model) F (x) according to the shape of the chassis, and then a prediction model is provided:
Figure BDA0002931149100000021
k is time data
And then obtaining the following data by taking the IMU and the chassis data as observation data:
Figure BDA0002931149100000022
wherein, yk、zkRespectively representing IMU observation data and chassis code disc observation data, then calculating Kalman gain K according to an EKF principle, and fusing a prediction state and an observation state to obtain final output;
and S2.2, taking the position state estimation obtained in the S2.1 as an SLAM predicted value, using laser radar data to combine with a local map to perform pose optimization, and using IMU integral data to the rear end of the SLAM as constraint.
Further, the initialization process in step S3 specifically includes:
s3.1, setting the amount to be calculated: conversion matrix from ENU coordinate system converted from GNSS to SLAM map coordinate system
Figure BDA0002931149100000023
And GNSS antenna to carrier external reference
Figure BDA0002931149100000024
Selecting a fixed window of size n (configurable);
s3.2 in the window size range, collecting the position of the carrier in the SLAM map coordinate system from the SLAM module
Figure BDA0002931149100000025
And attitude
Figure BDA0002931149100000026
k is time data;
s3.3 synchronously acquiring latitude and longitude information of GNSS antenna from GNSS system and converting the latitude and longitude information into ENU coordinate system position
Figure BDA0002931149100000027
S3.4 at any k time, the position of the GNSS antenna in the SLAM map coordinate system can be obtained through the SLAM output position and the attitude as follows:
Figure BDA0002931149100000031
the position of the GNSS antenna in the SLAM map coordinate system is obtained from the GNSS measurement values as follows:
Figure BDA0002931149100000032
then a linear model exists:
Figure BDA0002931149100000033
wherein n iskAnd (3) constructing and solving a least square problem for the noise at the time k:
Figure BDA0002931149100000034
s3.5 repeating steps S3.2 to S3.4 until the quantity to be optimized changes value twice continuously
Figure BDA0002931149100000035
And
Figure BDA0002931149100000036
less than the threshold epsilon is considered to be converged.
Further, the step S4 specifically includes:
s4.1 after the initialization is finished, according to the obtained conversion matrix
Figure BDA0002931149100000037
And radix Ginseng
Figure BDA0002931149100000038
Obtained by converting longitude and latitude output by GNSS system at moment i into ENU coordinate system
Figure BDA0002931149100000039
Conversion to coordinates in SLAM map coordinate system:
Figure BDA00029311491000000310
and S4.2, taking the conversion result of the GNSS data and the SLAM output pose as input, and performing fusion operation to obtain a final positioning result.
The GNSS antenna is in communication connection with the processor through the GNSS module, and the processor is used for receiving transmission data of the laser radar, the GNSS module, the chassis code disc and the inertial navigation measuring unit and processing the transmission data to obtain high-precision positioning information according to the high-precision positioning method based on multi-sensor fusion.
Has the advantages that: compared with the prior art, the high-precision positioning method and system based on multi-sensor fusion provided by the invention have the following advantages:
(1) the EKF is used for preprocessing the chassis coded disc and IMU data, and then the EKF is transmitted into the SLAM module for pose prediction, so that the stability of the SLAM system is effectively improved, and the SLAM positioning precision is greatly improved;
(2) solving a nonlinear optimization problem by constructing an error model of SLAM positioning data and GNSS positioning data to obtain a high-precision coordinate transformation matrix, so that the final fusion output can realize stable high-precision positioning output;
(3) the calibration module only needs to receive the positioning data of the single-antenna RTK-GNSS system, and compared with a part of coordinate alignment method which needs to use RTK double-antenna to obtain the orientation angle, the calibration module effectively reduces the system cost, improves the positioning precision and enhances the convenience of hardware deployment.
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The present invention will be further described and illustrated with reference to the following drawings.
FIG. 1 is a diagram of the hardware connections of the preferred embodiment of the present invention;
fig. 2 is a schematic diagram of fusion positioning according to a preferred embodiment of the present invention.
Detailed Description
The following description of the preferred embodiments of the present invention with reference to the accompanying drawings will more clearly and completely illustrate the technical solutions of the present invention.
Examples
Fig. 1 shows a preferred embodiment of a high-precision positioning system based on multi-sensor fusion, which can be used for an inspection robot. In hardware installation, a GNSS module is required to be installed in the robot, an RTK-GNSS receiving antenna is additionally installed at the highest position of the robot, and the RTK-GNSS receiving antenna is connected with the GNSS module in the robot through an antenna feeder line; a GNSS base station is deployed at a far end, a GNSS antenna and a GNSS module are also arranged on the base station, and GNSS differential data are provided for the GNSS module in the robot through network communication; a laser radar is arranged in the middle of the robot and is connected with a robot manual control machine through a network cable; an IMU is installed at a proper position in the robot and is connected with the industrial personal computer robot through RS 232; a high-precision encoder (namely a chassis code disc) is arranged in a robot motor control module, and information of the encoder is collected in an ECU (electronic control unit) and is connected with a robot controller through RS 232.
In the algorithm, EKF filtering processing is carried out on a coded disc and IMU data to obtain a high-precision odometer, and then the high-precision odometer enters an SLAM module for fusion, so that the positioning precision can be greatly improved, and a tightly coupled inertial navigation state can be provided; in the aspect of RTK fusion, a coordinate alignment module and a coordinate conversion module are respectively constructed, in the coordinate alignment module, the SLAM output position and RTK output are used for carrying out nonlinear optimization to obtain a coordinate conversion matrix of the RTK-GNSS data, the conversion matrix is loaded through the coordinate conversion module, the RTK-GNSS data are converted and then participate in the fusion of the SLAM output, the pose fusion with higher precision can be realized, and the output jitter is reduced.
As shown in fig. 2, the fusion localization process specifically includes the following steps:
s1 obtaining the laser radar, GNSS, IMU and code disc data, and time synchronizing the laser radar, IMU, GNSS and code disc data.
S2, an EKF fusion module is constructed, the input of the EKF fusion module is IMU and chassis code disc data, and the state variables are as follows:
x＝[p,q,v,w,a]
wherein the states p, q, v, w, a are respectively: position, attitude, velocity, angular velocity and acceleration, and then constructing a motion model F (x) according to the chassis form, so that a prediction model is provided:
Figure BDA0002931149100000051
k is time data;
and then obtaining the following data by taking the IMU and the chassis data as observation data:
Figure BDA0002931149100000052
wherein, yk、zkRespectively representing IMU observation data and chassis code disc observation data, specifically acceleration a, angular velocity w, velocity v and wheel steering angle theta;
and then, calculating Kalman gain K according to an EKF principle, fusing a prediction state with an observation state to obtain final output, further taking the position state estimation obtained by an EKF module as an SLAM prediction value, performing pose optimization by using laser radar data in combination with a local map, and using IMU integral data as constraint at the rear end of the SLAM.
An S3 initialization stage, wherein a coordinate alignment module is adopted to carry out nonlinear optimization on the SLAM output position and the GNSS output position to obtain a coordinate conversion matrix;
s3.1, setting the amount to be calculated: conversion matrix from ENU coordinate system converted from GNSS to SLAM map coordinate system
Figure BDA0002931149100000053
And GNSS antenna to carrierExternal reference of body
Figure BDA0002931149100000054
Selecting a fixed window with the size of n;
s3.2 in the window size range, collecting the position of the carrier in the SLAM map coordinate system from the SLAM module
Figure BDA0002931149100000055
And attitude
Figure BDA0002931149100000056
k is time data;
s3.3 synchronously acquiring latitude and longitude information of GNSS antenna from GNSS system and converting the latitude and longitude information into ENU coordinate system position
Figure BDA0002931149100000057
S3.4 at any k time, the position of the GNSS antenna in the SLAM map coordinate system can be obtained through the SLAM output position and the attitude as follows:
Figure BDA0002931149100000058
the position of the GNSS antenna in the SLAM map coordinate system is obtained from the GNSS measurement values as follows:
Figure BDA0002931149100000061
then a linear model exists:
Figure BDA0002931149100000062
wherein n iskAnd (3) constructing and solving a least square problem for the noise at the time k:
Figure BDA0002931149100000063
s3.5 repeating steps S3.2 to S3.4 until the quantity to be optimized changes value twice continuously
Figure BDA0002931149100000064
And
Figure BDA0002931149100000065
less than the threshold epsilon is considered to be converged.
S4 after the initialization is finished, according to the obtained conversion matrix
Figure BDA0002931149100000066
And radix Ginseng
Figure BDA0002931149100000067
Obtained by converting longitude and latitude output by GNSS system at moment i into ENU coordinate system
Figure BDA0002931149100000068
Conversion to coordinates in SLAM map coordinate system:
Figure BDA0002931149100000069
and (3) taking the GNSS data conversion result and the SLAM output pose as input, and outputting in a final fusion module (a common EKF or UKF).
The invention can use a vision camera to replace a laser radar to carry out ranging positioning, can also omit an IMU to carry out auxiliary positioning, but can reduce the stability of the system, and can also adopt an RAC (real time array calibration) positioning scheme to replace an RTK-GNSS positioning scheme.
The invention is used for solving the technical problem of unstable positioning of a mobile robot or a vehicle in the process of inspection, adopts multiple sensors for data fusion, utilizes the characteristic advantages of different sensors, such as the fact that an RTK-GNSS system can provide absolute accurate positioning information, a chassis code disc is not easily interfered by the external environment, the positioning precision of a laser radar is high, and the like, and uses other sensor data to complement the sensors in the environment which is not beneficial to the sensors, thereby greatly improving the positioning stability and reducing safety accidents while ensuring the positioning precision.
The above detailed description merely describes preferred embodiments of the present invention and does not limit the scope of the invention. Without departing from the spirit and scope of the present invention, it should be understood that various changes, substitutions and alterations can be made herein by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims and their equivalents. The scope of the invention is defined by the claims.

Claims (5)

1. A high-precision positioning method based on multi-sensor fusion is characterized by comprising the following steps:
s1, acquiring laser radar, GNSS, IMU and code disc data, and performing time synchronization on the laser radar, IMU, GNSS and code disc data;
s2, obtaining position state estimation of the carrier according to the IMU and the code disc data, and then entering the SLAM module for fusion to perform pose optimization;
an S3 initialization stage, wherein a coordinate alignment module is adopted to carry out nonlinear optimization on the SLAM output position and the GNSS output position to obtain a coordinate conversion matrix;
and S4, after the initialization is finished, performing coordinate conversion on the GNSS data according to the obtained coordinate conversion matrix, and fusing the GNSS data with the SLAM output position to obtain high-precision positioning information.
2. The method according to claim 1, wherein the fusion process in step S2 specifically includes:
s2.1, an EKF fusion module is constructed, the input of the EKF fusion module is IMU and coded disc data, and the state variables are as follows:
x＝[p,q,v,w,a]
wherein the states p, q, v, w, a are respectively: position, attitude, velocity, angular velocity and acceleration, and then constructing a motion model F (x) according to the chassis form, so that a prediction model is provided:
Figure FDA0002931149090000011
k is time data
And then obtaining the following data by taking the IMU and the chassis data as observation data:
Figure FDA0002931149090000012
wherein, yk、zkRespectively representing IMU observation data and chassis code disc observation data, then calculating Kalman gain K according to an EKF principle, and fusing a prediction state and an observation state to obtain final output;
and S2.2, taking the position state estimation obtained in the S2.1 as an SLAM predicted value, using laser radar data to combine with a local map to perform pose optimization, and using IMU integral data to the rear end of the SLAM as constraint.
3. The multi-sensor fusion-based high-precision positioning method according to claim 2, wherein the initialization process in step S3 specifically includes:
s3.1, setting the amount to be calculated: conversion matrix from ENU coordinate system converted from GNSS to SLAM map coordinate system
Figure FDA0002931149090000013
And GNSS antenna to carrier external reference
Figure FDA0002931149090000014
Selecting a fixed window with the size of n;
s3.2 in the window size range, collecting the position of the carrier in the SLAM map coordinate system from the SLAM module
Figure FDA0002931149090000021
And attitude
Figure FDA0002931149090000022
k is time data;
s3.3 synchronously acquiring latitude and longitude information of GNSS antenna from GNSS system and converting the latitude and longitude information into ENU coordinate system position
Figure FDA0002931149090000023
S3.4 at any k time, the position of the GNSS antenna in the SLAM map coordinate system can be obtained through the SLAM output position and the attitude as follows:
Figure FDA0002931149090000024
the position of the GNSS antenna in the SLAM map coordinate system is obtained from the GNSS measurement values as follows:
Figure FDA0002931149090000025
then a linear model exists:
Figure FDA0002931149090000026
wherein n iskAnd (3) constructing and solving a least square problem for the noise at the time k:
Figure FDA0002931149090000027
s3.5 repeating steps S3.2 to S3.4 until the quantity to be optimized changes value twice continuously
Figure FDA0002931149090000028
And
Figure FDA0002931149090000029
less than the threshold epsilon is considered to be converged.
4. The multi-sensor fusion-based high-precision positioning method according to claim 1, wherein the step S4 specifically includes:
s4.1 after the initialization is finished, according to the obtained conversion matrix
Figure FDA00029311490900000210
And radix Ginseng
Figure FDA00029311490900000211
Obtained by converting longitude and latitude output by GNSS system at moment i into ENU coordinate system
Figure FDA00029311490900000212
Conversion to coordinates in SLAM map coordinate system:
Figure FDA00029311490900000213
and S4.2, taking the conversion result of the GNSS data and the SLAM output pose as input, and performing fusion operation to obtain a final positioning result.
5. A high-precision positioning system based on multi-sensor fusion is characterized by comprising a laser radar, a GNSS antenna, a GNSS module, a chassis code disc, an inertial navigation measuring unit and a processor, wherein the laser radar, the GNSS antenna, the GNSS module, the chassis code disc, the inertial navigation measuring unit and the processor are arranged on a carrier, the GNSS antenna is in communication connection with the processor through the GNSS module, the processor is used for receiving transmission data of the laser radar, the GNSS module, the chassis code disc and the inertial navigation measuring unit, and high-precision positioning information is obtained through processing according to the high-precision positioning method based on multi-sensor fusion of any one of claims 1 to 4.
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CN113821023B (en) * 2021-08-05 2024-05-24 百工汇智(上海)工业科技有限公司 Scribing method for intelligent terrace scribing robot
CN114383608A (en) * 2021-12-21 2022-04-22 国家管网集团川气东送天然气管道有限公司 Multi-sensor fusion rotation positioning method and computer readable storage medium
CN115235475A (en) * 2022-09-23 2022-10-25 成都凯天电子股份有限公司 MCC-based EKF-SLAM back-end navigation path optimization method
CN115235475B (en) * 2022-09-23 2023-01-03 成都凯天电子股份有限公司 MCC-based EKF-SLAM back-end navigation path optimization method
CN117405118A (en) * 2023-10-16 2024-01-16 西安工业大学 Multi-sensor fusion mapping method, system, equipment and storage medium
CN117250623A (en) * 2023-11-20 2023-12-19 山东新坐标智能装备有限公司 Positioning method, system and mobile robot for fusion of laser radar and complementary positioning
CN117250623B (en) * 2023-11-20 2024-02-27 苏州新坐标智能装备有限公司 Positioning method, system and mobile robot for fusion of laser radar and complementary positioning

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