CN114323003A - Underground mine fusion positioning method based on UMB, IMU and laser radar - Google Patents
Underground mine fusion positioning method based on UMB, IMU and laser radar Download PDFInfo
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Abstract
The invention provides a method for positioning underground mine fusion based on UMB, IMU and laser radar, which comprises the following steps: constructing a vehicle position and attitude estimation model based on an IMU; constructing an EKF measurement fusion model based on UWB ranging; constructing a matching positioning model of laser radar point cloud; and constructing a fusion positioning model. The underground mine fusion positioning method based on the UMB, the IMU and the laser radar realizes accurate and stable positioning of the underground unmanned vehicle.
Description
Technical Field
The invention belongs to the field of fusion positioning, and particularly relates to a method for fusion positioning of underground mines based on UMB, IMU and laser radar.
Background
The trackless rubber-tyred vehicle for the mine comprises a material vehicle, a loading vehicle, a pickup truck, a patrol vehicle and the like, and most of the vehicles adopt a manual driving mode at present. The unmanned technology is applied to the industrial and mining wells, the equipment intelligence level and centralized management capability of the wells are improved, the production monitoring and risk identification means are improved, underground operating personnel are effectively reduced, the targets of safety under the condition of few people and safety under the condition of no people in the wells are realized, and the economic benefit and the social benefit of the industrial and mining management wells are improved.
The difficulty of unmanned underground mine is accurate underground positioning, and because of no GNSS signals, narrow and long tunnel and dim light, the vehicle is positioned accurately, which causes great trouble, and the difficulty also becomes a technical bottleneck restricting the unmanned underground mine. Ultra Wide Band (UWB) location has characteristics such as positioning accuracy height, low-power consumption, security height, signal penetrability are strong, utilizes the UWB location basic station of installation in the pit, communicates with UWB basic station through on-vehicle UWB equipment, can realize finding range between vehicle and the UWB basic station, provides effective information for accurate positioning in the pit. However, due to the limited number of underground UWB base stations, one UWB base station is usually configured at intervals of 200m-400m along the way, and the observability of the system is weak. The invention realizes the accurate and stable positioning of the underground unmanned vehicle by constructing the UMB and IMU of the vehicle-mounted UWB, the laser radar and the Inertial Measurement Unit (IMU) and the laser radar fusion positioning method.
In view of this, a method for positioning mine and mine fusion based on UMB, IMU and lidar is urgently needed.
Disclosure of Invention
Therefore, the UMB, the IMU and the laser radar fusion positioning method for the vehicle-mounted UWB, the laser radar and the Inertial Measurement Unit (IMU) are constructed, and accurate and stable positioning of the underground unmanned vehicle is achieved.
The UMB, IMU and laser radar fusion positioning method comprises the following steps:
s1, constructing an IMU-based vehicle position and posture estimation model;
s2, constructing an EKF measurement fusion model based on UWB ranging;
s3, constructing a matching positioning model of the laser radar point cloud;
s4, constructing a fusion positioning model based on S1, S2 and S3.
Further, the method for constructing the vehicle position and posture estimation model in step S1 specifically includes the following steps:
s101, calculating a deterministic error of the IMU inertial navigation system, comprising:
error of the platform angle:
wherein the content of the first and second substances,
is the misalignment angle of the inertial navigation system under the navigation coordinate system;
is a rotation angular velocity vector of the earth-fixed system relative to the inertial system;
is a rotation angular velocity vector of the navigation system relative to the ground fixation system; epsilonnIs the gyroscope random error.
Speed error:
wherein f isnThe acceleration vector under the navigation coordinate system; v. ofnThe velocity vector of the carrier under the navigation coordinate system is obtained;
is the accelerometer random error.
Position error:
wherein the content of the first and second substances,
respectively, the latitude, longitude and altitude error change rate of the carrier; rNIs the radius of curvature of the meridian; rEIs the radius of curvature of the meridian;
the velocity errors in the north, west and sky directions, respectively.
S102, establishing a vehicle position and attitude estimation model:
firstly, establishing a state equation of an IMU inertial navigation system:
discretizing to obtain:
Xk+1＝ΦXk+Γwk；
wherein the content of the first and second substances,
x is an inertial navigation state variable;
FINSis an inertial navigation state matrix;
w is an inertial navigation error variable;
g is an inertial navigation error sensitive matrix;
phi is a state transition matrix;
Γ is a discretized input matrix;
setting the EKF filtering updating period as T, calculating to obtain:
Φ＝I+FT；
Γ＝GT；
then, an integral update expression of the state variable is calculated:
after quaternion normalization, recalculating the F and G matrices to obtain:
Pk+1＝(I+FT)Pk(I+FT)T+T2GQGT；
to predict the covariance.
Further, the step S3 of constructing an EKF measurement fusion model based on UWB ranging specifically includes the following steps:
s201, calculating a position vector r of the UWB tag in a northwest coordinate system with the UWB base station as an origin:
wherein L isU、λU、hULatitude, longitude and altitude of the tag, respectively; l isB、λB、hBLatitude, longitude and altitude of the base station, respectively.
S202, calculating a distance observation value between the UWB tag and the base station:
ρu＝ρtrue+εu；
where ρ istrueRho is the actual distance and the observation distance from the label to the base station respectively; δ r is the distance error vector from the tag to the base station;
a rotation matrix from a body coordinate system to a navigation coordinate system; luA lever arm vector of a tag to an inertial navigation center; phi is an inertial navigation installation angle error vector.
S203, obtaining an observation equation:
Zu＝HuX+Vu。
wherein Z isuIs a UWB observation; huIs an observation sensitivity matrix; vuTo observe the noise.
Further, the observation equation of the combined system based on the position and the heading of the laser radar is
Zlidar＝HlidarX+Vlidar；
Wherein:
calculating a filter updating gain matrix:
K＝Pk+1HT(HPk+1HT+R)-1；
updating the filter state variables:
xk+1＝xk+1|k+K(Zk+1-yk+1)；
updating the filter covariance:
Pk+1＝Pk+1|k-KHPk+1|k。
compared with the prior art, the technical scheme of the invention has the following advantages:
according to the method, an Extended Kalman Filtering (EKF) -based state estimation model is constructed, the position and the attitude of the vehicle are predicted by using the IMU, UWB ranging information and laser radar point cloud matching positioning data are used for updating, the accurate position and the attitude of the vehicle in the underground are estimated in real time, and the problem of accurate positioning in the underground is solved.
Drawings
FIG. 1 is a schematic diagram of a method provided by an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The inertial navigation system has many error sources, mainly including the error of the inertial instrument itself, the installation error and the scale error of the inertial instrument, the initial condition error of the system, the calculation error of the system, the error caused by various interferences, and the like. Inertial navigation errors can be divided into two categories: deterministic errors and random errors. The deterministic error comprises a platform angle error, a speed error and a position error, and the random error mainly comprises gyro drift, zero offset of an accelerometer and the like.
The method described in this embodiment, as shown in fig. 1, includes the following steps:
the platform error angle equation of the inertial navigation system is as follows:
wherein the content of the first and second substances,
is the misalignment angle of the inertial navigation system under the navigation coordinate system;
is a rotation angular velocity vector of the earth-fixed system relative to the inertial system;
is a rotation angular velocity vector of the navigation system relative to the ground fixation system; epsilonnIs the gyroscope random error.
Wherein:
the velocity error equation for an inertial navigation system is described below
By
Can obtain the product
Consider δ gn＝0，δfn＝fp-fnWherein f ispIs the actual output of the accelerometer. Let the measurement error of the accelerometer be
Then
Therefore, it is
The velocity error equation is then obtained as:
wherein f isnThe acceleration vector under the navigation coordinate system; v. ofnThe velocity vector of the carrier under the navigation coordinate system is obtained;
is the accelerometer random error.
By
The available position error equation is:
wherein the content of the first and second substances,
variation of latitude, longitude and altitude errors of carrier respectivelyThe conversion rate; rNIs the radius of curvature of the meridian; rEIs the radius of curvature of the meridian;
the velocity errors in the north, west and sky directions, respectively.
By combining the above formulas, the state equation of the integrated navigation system can be obtained:
based on the jacobian matrix, the following discretized state equation can be established:
Xk+1＝ΦXk+Γwk
where Φ is the available state transition matrix and Γ is the discretized input matrix. Let EKF filter update period be T, then their expressions are:
Φ＝I+FT
Γ＝GT
in order to improve the prediction precision of the state variable, a four-order Runge Kutta method is adopted to solve the differential equation.
The integral update expression of the state variable is as follows:
after the state prediction is completed, the quaternion is required to be standardized, the F and G matrixes are recalculated, and then the covariance is predicted by adopting the following formula
Pk+1＝(I+FT)Pk(I+FT)T+T2GQGT
An EKF measurement fusion model of UWB ranging is constructed below;
the position vector r of the UWB tag in the North-West coordinate system with the UWB base station as the origin can be expressed as
Wherein L isU、λU、hULatitude, longitude and altitude of the tag, respectively; l isB、λB、hBLatitude, longitude and altitude of the base station, respectively.
The position vector r of the UWB tag in the North-West coordinate system with the UWB base station as the origin can be expressed as
The observed value of the distance between the vehicle-mounted UWB tag and the base station can be expressed as
The observed value of the distance between the vehicle-mounted UWB tag and the base station can be expressed as
Where ρ istrueRho is the actual distance and the observation distance from the label to the base station respectively; δ r is the distance error vector from the tag to the base station;
a rotation matrix from a body coordinate system to a navigation coordinate system; luA lever arm vector of a tag to an inertial navigation center; phi is an inertial navigation installation angle error vector.
The UWB distance measurement may be expressed as
ρu＝ρtrue+εu
Therefore, the observation equation can be expressed as
Zu＝HuX+Vu
Wherein Z isuIs a UWB observation; huIs an observation sensitivity matrix; vuTo observe the noise.
Constructing a laser radar point cloud matching positioning model
After the data of the laser radar sensor is obtained, invalid point elimination and point cloud filtering are firstly needed to obtain effective point cloud information, the effective point cloud information is transmitted to a laser point cloud positioning module to serve as an input item of real-time scanning positioning, an IMU data processing module carries out data synchronization and pre-integration processing to obtain transformation information of the IMU sensor in adjacent moments and serves as scanning matching initial value input of the laser point cloud positioning module, a map provided by a first party is loaded by a map loading module, and IMU observed quantity in a laser point cloud positioning module [ t, t + delta t ] time sequence is input. The observation model of the IMU is:
after the point cloud information of the current scanning and the IMU pre-integration information are obtained, scanning matching between the current scanning frame and the existing map can be achieved, data constraint of the laser point cloud is obtained, then the data constraint is fused with the IMU pre-integration result, pose optimization is carried out by using an optimization method based on Unscented Kalman Filtering (UKF), and finally a laser point cloud positioning result is obtained.
And constructing a laser radar/IMU fusion positioning model.
The laser radar point cloud and the three-dimensional point cloud map are matched, so that the position and attitude data of the vehicle under a geodetic coordinate system can be obtained, the position comprises longitude, latitude and height, and the heading of the vehicle can be extracted according to the attitude data.
The observation equation of the combined system based on the position and the course of the laser radar is
Zlidar＝HlidarX+Vlidar
Wherein
Equation of position observation
In a lidar/IMU fusion positioning system, the real-time position given by the inertial navigation system is the geographic latitude longitude and altitude, but contains errors. Is provided with LI、λIAnd hIRespectively latitude, longitude and altitude of the inertial navigation output, L, lambda and h respectively are true latitude, longitude and altitude, then
The lidar point cloud matching also gives the real-time position of the vehicle, and the main error source of the lidar point cloud matching is the error in the point cloud matching process. Is provided with Llidar、λlidar、hlidarLatitude, longitude and altitude of the laser radar matching output respectively, then
In the formula, NN、NW、NUThe position matching errors of the laser radar in the north, west and sky directions are obtained.
When the position difference between the INS and the GPS is taken as the observed quantity, the observation equation can be expressed by the following equation
Represents the above formula as
ZP＝HPX+VP
In the formula
VP＝[NN NW NU]T
Distance measurement noise VPAs white noise processing.
Calculating a course observation equation:
the real azimuth angle of the vehicle is psi, and the inertial navigation resolving error is delta psiIError angle delta phi obtained by laser radar point cloud matchingGThen, the azimuth angles of the inertial navigation and the laser radar are respectively:
ψI＝ψ+δψI
ψG＝ψ+δψG
the azimuthal observations of the system are:
Zψ＝ψI-ψM＝δψI-δψM＝HψX+Vψ
wherein:
According to the UWB and laser radar observation equations constructed in the foregoing, a filtering update gain matrix can be calculated.
K＝Pk+1HT(HPk+1HT+R)-1
The filter state variables are updated.
Xk+1＝Xk+1|k+K(Zk+1-yk+1)
The filter covariance is updated.
Pk+1＝Pk+1|k-KHPk+1|k
According to the state equation, the observation equation and the fusion processing model, the fusion positioning of UWB, laser radar and IMU can be realized.
The method adopts 1 vehicle-mounted UWB device, a plurality of UWB base stations, 1 IMU and 1 multi-line laser radar, and adopts the laser radar to carry out point cloud matching to obtain position and attitude data on the basis of constructing an underground high-precision map. By constructing a state equation with an IMU as a core and establishing a UWB and laser radar observation equation, the fusion calculation of the accurate position, speed and attitude of the unmanned vehicle under the underground mine environment without GNSS signals is realized, and accurate and stable navigation positioning information can be provided for the underground mine unmanned driving system.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (4)
1. A method for positioning mine and mine fusion based on UMB, IMU and laser radar is characterized by comprising the following steps:
s1, constructing an IMU-based vehicle position and posture estimation model;
s2, constructing an EKF measurement fusion model based on UWB ranging;
s3, constructing a matching positioning model of the laser radar point cloud;
s4, constructing a fusion positioning model based on S1, S2 and S3.
2. The method for positioning mine and mine fusion based on UMB, IMU and lidar according to claim 1, wherein the method for constructing the vehicle position and attitude estimation model in step S1 specifically comprises the following steps:
s101, calculating a deterministic error of the IMU inertial navigation system, comprising:
error of the platform angle:
wherein the content of the first and second substances,
is the misalignment angle of the inertial navigation system under the navigation coordinate system;
is a rotation angular velocity vector of the earth-fixed system relative to the inertial system;
is a rotation angular velocity vector of the navigation system relative to the ground fixation system; epsilonnIs the gyroscope random error.
Speed error:
wherein f isnThe acceleration vector under the navigation coordinate system; v. ofnThe velocity vector of the carrier under the navigation coordinate system is obtained;
is the accelerometer random error.
Position error:
wherein the content of the first and second substances,
respectively, the latitude, longitude and altitude error change rate of the carrier; rNIs the radius of curvature of the meridian; rEIs the radius of curvature of the meridian; delta vN、δvW、δvUThe velocity errors in the north, west and sky directions, respectively.
S102, establishing a vehicle position and attitude estimation model:
firstly, establishing a state equation of an IMU inertial navigation system:
discretizing to obtain:
Xk+1＝ΦXk+Γwk；
wherein the content of the first and second substances,
x is an inertial navigation state variable;
FINSis an inertial navigation state matrix;
w is an inertial navigation error variable;
g is an inertial navigation error sensitive matrix;
phi is a state transition matrix;
Γ is a discretized input matrix;
setting the EKF filtering updating period as T, calculating to obtain:
Φ＝I+FT；
Γ＝GT；
then, an integral update expression of the state variable is calculated:
after quaternion normalization, recalculating the F and G matrices to obtain:
Pk+1＝(I+FT)Pk(I+FT)T+T2GQGT；
where Q is the process noise matrix. The covariance can be predicted according to the above equation.
3. The method for positioning fusion of underground mine based on UMB, IMU and lidar as claimed in claim 2, wherein the step S3 of constructing EKF measurement fusion model based on UWB ranging specifically comprises the following steps:
s201, calculating a position vector r of the UWB tag in a northwest coordinate system with the UWB base station as an origin:
wherein L isU、λU、hULatitude, longitude and altitude of the tag, respectively; l isB、λB、hBLatitude, longitude and altitude of the base station, respectively.
S202, calculating a distance observation value between the UWB tag and the base station:
ρu＝ρtrue+εu；
where ρ istrueRho is the actual distance and the observation distance from the label to the base station respectively; δ r is the distance error vector from the tag to the base station;
a rotation matrix from a body coordinate system to a navigation coordinate system; luA lever arm vector of a tag to an inertial navigation center; phi is an inertial navigation installation angle error vector.
S203, obtaining an observation equation:
Zu＝HuX+Vu。
wherein Z isuIs a UWB observation; huIs an observation sensitivity matrix; vuTo observe the noise.
4. The UMB, IMU and lidar based well-mine fusion positioning method according to claim 3, wherein the specific process of step S4 is as follows:
the observation equation of the combined system based on the position and the course of the laser radar is
Zlidar＝HlidarX+Vlidar；
Wherein:
calculating a filter updating gain matrix:
K＝Pk+1HT(HPk+1HT+R)-1；
updating the filter state variables:
xk+1＝xk+1|k+K(Zk+1-yk+1)；
updating the filter covariance:
Pk+1＝Pk+1|k-KHPk+1|k；
then, a fusion model is obtained according to the state equation in the step S1, the observation equation in the step S2.
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