IMU & GPS Fusion Positioning and Barometer Correction Lifting Positioning Control System and Method

Abstract

This invention discloses a hoisting positioning control system and method based on IMU & GPS fusion positioning and barometer correction. Addressing the problem of blind hoisting of precast components, the IMU / GPS fusion method offers higher positioning accuracy compared to GPS alone, and is more suitable for construction situations, better reducing the error caused by environmental factors on site. Furthermore, to address the issue of inaccurate height data obtained from GPS positioning methods and the lack of vertical (Z-axis) correction, making it unsuitable for construction hoisting, this invention incorporates barometer-provided height data to improve the IMU & GPS vertical position algorithm, resulting in more accurate vertical positioning data that better meets the precise positioning requirements of construction hoisting. In addition, this invention further addresses the drawback of IMU positioning drift errors accumulating over time, leading to inaccurate positioning. A zero-velocity correction method filters out the accumulated drift error of the IMU, further improving the accuracy of the positioning system and method.

Description

Technical Field

[0001] This invention relates to the field of building construction technology, and more specifically to a hoisting positioning control system and method based on IMU & GPS fusion positioning and barometer correction. Background Technology

[0002] Currently, blind lifting is a common problem in the construction and hoisting of precast components. Blind lifting increases the difficulty of catching fallen precast components and raises the risk of damage, potentially leading to injury to construction workers. Traditionally, to address blind lifting, communication via walkie-talkie is used on construction sites to direct tower crane operators. This method cannot quickly adjust the position of precast components through communication and suffers from problems such as invisibility and delays. Many studies have been conducted on hoisting techniques. One proposed method to address blind lifting is using laser positioning systems to locate the object being lifted, thereby improving the visibility of tower crane operators. However, due to the high cost of laser systems, they are not currently widely used on construction hoisting sites.

[0003] Because IMU active positioning suffers from long-distance positional shifts, current research on active positioning mainly focuses on improving accuracy by fusing IMU with other positioning methods. Ling Chen proposed a method for pedestrian location tracking by fusing IMU and GPS algorithms; however, this method is unsuitable for construction hoisting problems due to the inaccuracy of GPS altitude data and the lack of vertical (Z-axis) correction.

[0004] Therefore, how to propose a hoisting positioning control system and method based on IMU & GPS fusion positioning and barometer correction is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a hoisting positioning control system and method based on IMU & GPS fusion positioning and barometer correction.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] The hoisting positioning control system based on IMU & GPS fusion positioning and barometer correction includes an integrated positioning module, which is installed on the hoisting object to locate the hoisting object and send positioning information.

[0008] The integrated positioning module includes a LoRa wireless transmission unit, an IMU inertial sensor unit, a GPS positioning unit, a barometer, and a microprocessor;

[0009] The LoRa wireless transmission unit is connected to the IMU inertial sensor unit, the GPS positioning unit, the barometer, and the microprocessor to realize data transmission;

[0010] The IMU inertial sensor unit is used to collect the status information of the hoisted object in real time;

[0011] The GPS positioning unit is used to collect the horizontal position information of the hoisted object in real time.

[0012] The barometer is used to collect the vertical height information of the hoisted object in real time.

[0013] The microprocessor is connected to the IMU inertial sensor unit, the GPS positioning unit, and the barometer. It is used to receive real-time measurement data from the IMU inertial sensor unit, represent the position information of the hoisted object using quaternions, correct errors in the position information, and iteratively update the quaternion representation.

[0014] The hoisting positioning control method based on IMU & GPS fusion positioning and barometer correction, using the spatial coordinate system as the absolute coordinate system W and the IMU's built-in coordinate system as the relative coordinate system R, includes the following steps:

[0015] S1. Deploy the LoRa wireless transmission unit and install the integrated positioning module on the suspended object. The IMU inertial sensor unit collects relative coordinate system data in real time and converts the relative coordinate system data into absolute coordinate system data, thereby obtaining the state matrix X of the suspended object at time k. k =(P k V k O k ) T ;

[0016] Where P k Let V be the displacement vector at time k. k Let O be the velocity vector at time k. k Let K be the Euler angle at time k;

[0017] S2. The zero-velocity test correction method is used to correct the position error of the state matrix at time k, and the state matrix after error correction is obtained.

[0018] in, Let be the displacement vector at time k after error correction. This is the velocity vector at time k after error correction. These are the Euler angles at time k after error correction;

[0019] S3. Determine whether both the GPS positioning unit and the barometer are available. If both are available, perform Kalman filtering on the data obtained by the GPS positioning unit and the barometer and the corrected data of the IMU inertial sensor unit to update the quaternion representation method in real time. If one of them is unavailable, directly update the quaternion representation on the corrected data obtained by the IMU inertial sensor unit.

[0020] Preferably, the specific content of S1 includes:

[0021] Collect the displacement vector P at time k k =(x k y k , z k The velocity vector V at time k k =(x k ', y k ',z k Euler angles at time k

[0022] Obtain the absolute acceleration at time k And convert it into absolute acceleration at time k. in:

[0023]

[0024] In the above formula, Q k =(q w,k ,q 1,k ,q 2,k ,q 3,k Let Q' be the rotation transformation matrix between the relative coordinate system R and the absolute coordinate system W, expressed as a quaternion. k Q represents k The conjugate matrix, G = (0,0,g), represents the gravitational acceleration vector;

[0025] Quaternions are converted to Euler angles, i.e., O2, using the transformation function Q2Euler(Q). k =Q2Euler(Q);

[0026] Q2Euler(Q)=(atan2(2(q w q1+q2q3),1-2(q1 2 +q2 2 )),arcsin(2(q w q2-q3q1)),atan2(2( q w q3+q1q2),1-2(q2 2 +q2 3 )));

[0027] The formula for updating position over time is as follows:

[0028]

[0029]

[0030] The matrix representation of the above formula is as follows:

[0031]

[0032] Where Ts represents the sensor sampling time interval, and I represents the identity matrix, calculated from... The phase acceleration at time k-1 is used to iterate the above calculation formula by substituting the initial state, thereby updating the position and velocity of the hoisted object in real time and obtaining the state matrix X of the hoisted object at time k in real time. k =(P k V k O k ) T .

[0033] Preferably, the specific content of the zero-velocity test correction method in S2 is as follows: The zero-velocity detection logic value is obtained by simultaneously satisfying three conditions: C1, C2, and C3.

[0034]

[0035] in and These represent the minimum threshold and the maximum threshold, respectively.

[0036]

[0037] in Indicates the local acceleration variance;

[0038]

[0039] in

[0040] In C1, C2, and C3, 1 and 0 represent the stationary state and the moving state, respectively.

[0041] Preferably, the state matrix after error correction in S2 is:

[0042]

[0043] in, Let be the displacement vector at time k after error correction. This is the velocity vector at time k after error correction. O is the Euler angle at time k after error correction. k Let X be the Euler angle at time k without error correction. k-1 The state matrix at time k-1 is... Let O be the phase acceleration at time k, and let A and C be the coefficient matrices; k A and C are expressed as follows:

[0044]

[0045] Preferably, the process in S3 of performing Kalman filtering on the data acquired by the GPS positioning unit and the barometer, along with the corrected data from the IMU inertial sensor unit, includes:

[0046] Longitude and latitude coordinates are obtained via GPS, and altitude data (h) is obtained via barometer. pi Then, the longitude and latitude coordinates are converted into global coordinates to obtain the position matrix D. k =(x gps y gps h pi ) T , where x gps y gps The global coordinates are obtained by transforming the latitude and longitude coordinates acquired by GPS, h pi Altitude data obtained from a barometer;

[0047] Calculate state prediction:

[0048]

[0049] Among them, K k For Kalman gain, and: Here is the covariance matrix for the state estimation; H k It is the transformation matrix from the state variables of position coordinates to the measurement. D k The measured values ​​of the position coordinates, ∑ k，g The covariance of the prior estimate at time k is... covariance, R k The noise covariance is obtained from the observed data.

[0050]

[0051] To eliminate the drift error in global intra-frame acceleration caused by quaternion errors, the quaternions are updated as follows:

[0052] Q k =ΔQ k-1 *Q k-1

[0053] in:

[0054] In the formula, Q k-1 Let Q be the quaternion at time k-1. k Let ΔQ be the quaternion at time k. k-1 Let be the quaternion representing the transformation of the relative coordinate system from time t-1 to t; the measurement model is... Where z k For predictive measurement, n k For environmental noise, R k R is the covariance matrix of the environmental noise. k =E(n) k n k T ), H k Given a measurement matrix, its noise covariance matrix R k =E(n) k n k T ).

[0055] Preferably, the specific steps for converting longitude and latitude coordinates to global coordinates include: using the conversion formula provided by Dupree to convert longitude and latitude into global coordinates.

[0056]

[0057]

[0058] (lon0, lat0) is the origin of the geographic coordinates, (lon1, lat1) is the geographic coordinates of a specific point, a is the equatorial radius of the Earth, and c is the polar radius of the Earth.

[0059] Preferably, if one of the steps in S3 is unavailable, the corrected data acquired by the IMU inertial sensor unit is directly updated and iterated using quaternions:

[0060]

[0061]

[0062]

[0063] As can be seen from the above technical solutions, compared with the prior art, this invention discloses a hoisting positioning control system and method based on IMU & GPS fusion positioning and barometer correction. Addressing the current problem of blind hoisting of precast components, the IMU / GPS fusion method offers higher positioning accuracy compared to GPS alone, and is more suitable for construction situations, better reducing the error impact of GPS due to on-site environmental factors. Simultaneously, addressing the issue of inaccurate height data obtained from GPS positioning methods and the lack of vertical (Z-axis) fusion correction, making it unsuitable for construction hoisting, this invention incorporates barometer-provided height data to improve the algorithm for IMU & GPS vertical position, thereby making the vertical position positioning data more accurate and better meeting the precise positioning requirements of construction hoisting. Furthermore, this invention further addresses the drawback of IMU positioning drift errors accumulating over time, leading to inaccurate positioning, by using a zero-velocity correction method to filter out the accumulated drift error of the IMU, further improving the accuracy of the positioning system and method. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0065] Figure 1 The attached figure is a flowchart illustrating the hoisting positioning control method based on IMU & GPS fusion positioning and barometer correction provided by the present invention. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] This invention discloses a hoisting positioning control system based on IMU & GPS fusion positioning and barometer correction, including an integrated positioning module installed on the hoisting object. The integrated positioning module positions the hoisting object and sends positioning information.

[0068] The integrated positioning module includes a LoRa wireless transmission unit, an IMU inertial sensor unit, a GPS positioning unit, a barometer, and a microprocessor;

[0069] The LoRa wireless transmission unit is connected to the IMU inertial sensor unit, GPS positioning unit, barometer and microprocessor to realize data transmission;

[0070] The IMU (Inertial Measurement Unit) is used to collect real-time status information of the hoisted object;

[0071] The GPS positioning unit is used to collect the horizontal position information of the hoisted object in real time.

[0072] The barometer is used to collect real-time height information of the object being hoisted in the vertical direction;

[0073] The microprocessor is connected to the IMU inertial sensor unit, GPS positioning unit, and barometer. It is used to receive real-time measurement data from the IMU inertial sensor unit, represent the position information of the hoisted object using quaternions, correct errors in the position information, and iteratively update the quaternion representation.

[0074] The hoisting positioning control method based on IMU & GPS fusion positioning and barometer correction, using the spatial coordinate system as the absolute coordinate system W and the IMU's built-in coordinate system as the relative coordinate system R, includes the following steps:

[0075] S1. Deploy the LoRa wireless transmission unit, install the integrated positioning module on the suspended object, and use the IMU inertial sensor unit to collect relative coordinate system data in real time, converting the relative coordinate system data into absolute coordinate system data to obtain the state matrix X of the suspended object at time k. k =(P k V k O k ) T ;

[0076] Where P k Let V be the displacement vector at time k. k Let O be the velocity vector at time k. k Let K be the Euler angle at time k;

[0077] S2. The zero-velocity test correction method is used to correct the position error of the state matrix at time k, and the state matrix after error correction is obtained.

[0078] in, Let be the displacement vector at time k after error correction. This is the velocity vector at time k after error correction. These are the Euler angles at time k after error correction;

[0079] S3. Determine whether both the GPS positioning unit and the barometer are available. If both are available, perform Kalman filtering on the data obtained by the GPS positioning unit and the barometer and the corrected IMU inertial sensor unit data to update the quaternion representation method in real time. If one of them is unavailable, directly update the quaternion representation on the corrected data obtained by the IMU inertial sensor unit.

[0080] It should be further explained that: the absolute coordinate system W is located on the Earth's surface, with due east as the X-axis, due north as the Y-axis, and the vertical direction upwards from the ground as the Z-axis. The absolute coordinate system follows the right-hand rule. Meanwhile, the IMU's built-in coordinate system is used as the relative coordinate system R. Obviously, during positioning, the data collected by the IMU in the relative coordinate system needs to be converted to data in the absolute coordinate system, which requires the use of a coordinate transformation matrix. The IMU used in this embodiment calculates its position based on a strapdown IMU navigation algorithm. It uses a built-in microprocessor to run Kalman filtering to fuse sensor data from the accelerometer, gyroscope, and magnetometer, directly obtaining relatively accurate orientation information. This device can directly obtain the orientation data from the inertial sensors, represented as a quaternion, denoted as Q = (q... w The transformation matrix (q1, q2, q3) is represented using unit quaternions. This representation avoids frame locking issues and simplifies the transformation and computation processes.

[0081] To further implement the above technical solution, the specific content of S1 includes:

[0082] Collect the displacement vector P at time k k =(x k y k , z k The velocity vector V at time k k =(x k ', y k ',z k Euler angles at time k

[0083] Obtain the absolute acceleration at time k And convert it into absolute acceleration at time k. in:

[0084]

[0085] In the above formula, Q k =(q w,k ,q 1,k ,q 2,k ,q 3,k Let Q' be the rotation transformation matrix between the relative coordinate system R and the absolute coordinate system W, expressed as a quaternion.k Q represents k The conjugate matrix, G = (0,0,g), represents the gravitational acceleration vector;

[0086] Quaternions are converted to Euler angles, i.e., O2, using the transformation function Q2Euler(Q). k =Q2Euler(Q);

[0087] Q2Euler(Q)=(atan2(2(q w q1+q2q3),1-2(q1 2 +q2 2 )),arcsin(2(q w q2-q3q1)),atan2(2( q w q3+q1q2),1-2(q2 2 +q2 3 )));

[0088] The formula for updating position over time is as follows:

[0089]

[0090]

[0091] The matrix representation of the above formula is as follows:

[0092]

[0093] Where Ts represents the sensor sampling time interval, and I represents the identity matrix, calculated from... The phase acceleration at time k-1 is used to iterate the above calculation formula by substituting the initial state, thereby updating the position and velocity of the hoisted object in real time and obtaining the state matrix X of the hoisted object at time k in real time. k =(P k V k O k ) T .

[0094] To further implement the above technical solution, the specific content of the zero-velocity verification correction method in S2 is as follows: The zero-velocity detection logic value is obtained by simultaneously satisfying three conditions: C1, C2, and C3.

[0095]

[0096] in and These represent the minimum threshold and the maximum threshold, respectively.

[0097]

[0098] in Indicates the local acceleration variance;

[0099]

[0100] in

[0101] In C1, C2, and C3, 1 and 0 represent the stationary state and the moving state, respectively.

[0102] It needs further explanation that IMU-based positioning algorithms have significant drawbacks. The obtained position is accurate only for a short period, but over long distances, accelerometer and gyroscope errors accumulate and drift, resulting in large displacement drift after two integrations and inaccurate positioning. To address this issue, a method called Zero Velocity Detection and Correction (ZUPTs) exists to correct position errors. This method assumes that the velocity should be zero during the stationary phase. By detecting the stationary phase and setting the velocity to zero for that phase, some drift errors can potentially be corrected. This method offers improved accuracy compared to the basic position calculation algorithm. A ZUPT correction algorithm proposed by Jimenezetal is used. This algorithm utilizes information sources (accelerometer and gyroscope) and a first-order low-pass filter to implement a multi-condition attitude detection algorithm. In this embodiment, the fusion algorithm uses this algorithm to detect the zero-velocity phase.

[0103] To further implement the above technical solution, the state matrix after error correction in S2 is as follows:

[0104]

[0105] in, Let be the displacement vector at time k after error correction. This is the velocity vector at time k after error correction. O is the Euler angle at time k after error correction. k Let X be the Euler angle at time k without error correction. k-1 The state matrix at time k-1 is... Let O be the phase acceleration at time k, and let A and C be the coefficient matrices; k A and C are expressed as follows:

[0106]

[0107] To further implement the above technical solution, S3 includes the following Kalman filtering of the data acquired by the GPS positioning unit and the barometer with the corrected IMU inertial sensor unit data:

[0108] Longitude and latitude coordinates are obtained via GPS, and altitude data (h) is obtained via barometer.pi Then, the longitude and latitude coordinates are converted into global coordinates to obtain the position matrix D. k =(x gps y gps h pi ) T , where x gps y gps The global coordinates are obtained by transforming the latitude and longitude coordinates acquired by GPS, h pi Altitude data obtained from a barometer;

[0109] Calculate state prediction:

[0110]

[0111] Among them, K k For Kalman gain, and: Here is the covariance matrix for the state estimation; H k It is the transformation matrix from the state variables of position coordinates to the measurement. D k The measured values ​​of the position coordinates, ∑ k，g The covariance of the prior estimate at time k is... covariance, R k The noise covariance is obtained from the observed data.

[0112]

[0113] To eliminate the drift error in global intra-frame acceleration caused by quaternion errors, the quaternions are updated as follows:

[0114] Q k =ΔQ k-1 *Q k-1

[0115] in:

[0116] In the formula, Q k-1 Let Q be the quaternion at time k-1. k Let ΔQ be the quaternion at time k. k-1 Let be the quaternion representing the transformation of the relative coordinate system from time t-1 to t; the measurement model is... Where z k For predictive measurement, n k For environmental noise, R k R is the covariance matrix of the environmental noise. k =E(n) k n k T ), H kGiven a measurement matrix, its noise covariance matrix R k =E(n) k n k T ).

[0117] To further implement the above technical solution, the specific steps for converting longitude and latitude coordinates to global coordinates include: using the conversion formula provided by Dupree to convert longitude and latitude to global coordinates.

[0118]

[0119]

[0120] (lon0, lat0) is the origin of the geographic coordinates, (lon1, lat1) is the geographic coordinates of a specific point, a is the equatorial radius of the Earth, and c is the polar radius of the Earth.

[0121] To further implement the above technical solution, if one of the steps in S3 is unavailable, the corrected data acquired by the IMU inertial sensor unit will be directly updated and iterated using quaternions:

[0122]

[0123]

[0124]

[0125] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0126] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A hoisting positioning control method based on IMU & GPS fusion positioning and barometer correction, characterized in that, An application is provided for a hoisting positioning control system based on IMU & GPS fusion positioning and barometer correction. The system includes an integrated positioning module, which is installed on the hoisted object. The integrated positioning module locates the hoisted object and sends positioning information. The integrated positioning module includes a LoRa wireless transmission unit, an IMU inertial sensor unit, a GPS positioning unit, a barometer, and a microprocessor; The LoRa wireless transmission unit is connected to the IMU inertial sensor unit, the GPS positioning unit, the barometer, and the microprocessor to realize data transmission; The IMU inertial sensor unit is used to collect the status information of the hoisted object in real time; The GPS positioning unit is used to collect the horizontal position information of the hoisted object in real time. The barometer is used to collect the vertical height information of the hoisted object in real time. The microprocessor is connected to the IMU inertial sensor unit, the GPS positioning unit, and the barometer. It is used to receive real-time measurement data from the IMU inertial sensor unit, represent the position information of the hoisted object using quaternions, correct errors in the position information, and iteratively update the quaternion representation. The control method, based on the control system, uses the spatial coordinate system as the absolute coordinate system W and the IMU's built-in coordinate system as the relative coordinate system R. The method includes the following steps: S1. Deploy the LoRa wireless transmission unit and install the integrated positioning module on the suspended object. The IMU inertial sensor unit collects relative coordinate system data in real time and converts the relative coordinate system data into absolute coordinate system data, thereby obtaining the state matrix X of the suspended object at time k. k =(P k V k O k ) T ; Where P k Let V be the displacement vector at time k. k Let O be the velocity vector at time k. k Let K be the Euler angle at time k; S2. The zero-velocity test correction method is used to correct the position error of the state matrix at time k, and the state matrix after error correction is obtained. ; in, Let be the displacement vector at time k after error correction. This is the velocity vector at time k after error correction. These are the Euler angles at time k after error correction; S3. Determine whether both the GPS positioning unit and the barometer are available. If both are available, perform Kalman filtering on the data obtained by the GPS positioning unit and the barometer and the corrected data of the IMU inertial sensor unit to update the quaternion representation method in real time. If one of them is unavailable, directly update the quaternion representation method on the corrected data obtained by the IMU inertial sensor unit. The specific content of the zero-velocity test correction method described in S2 is as follows: The zero-velocity detection logic value is obtained by simultaneously satisfying three conditions: C1, C2, and C3. in , and These represent the minimum threshold and the maximum threshold, respectively. in , Indicates the local acceleration variance; in ; In C1, C2, and C3, 1 and 0 represent the stationary state and the moving state, respectively.

2. The hoisting positioning control method based on IMU & GPS fusion positioning and barometer correction according to claim 1, characterized in that, The specific content of S1 includes: Collect the displacement vector P at time k k = (x k y k , z k The velocity vector V at time k k = (x k ',y k ',z k Euler angle O at time k k =(φ k θ k , ψ k ); Obtain the absolute acceleration at time k And convert it into absolute acceleration at time k. ,in: In the above formula, Q k =(q w,k ,q 1,k ,q 2,k ,q 3,k Let be the rotation transformation matrix between the relative coordinate system R and the absolute coordinate system W, expressed using quaternions. Q represents k The conjugate matrix, G=(0,0,g), represents the gravitational acceleration vector; Quaternions are converted to Euler angles, i.e., O2, using the transformation function Q2Euler(Q). k =Q2Euler(Q); Q2Euler(Q)=(atan2(2(q w (q1+q2q3),1−2(q1) 2 +q2 2 arcsin(2(q) w q2−q3q1)),atan2(2(q w (q3+q1q2),1−2(q2) 2 +q2 3 ))); The formula for updating position over time is as follows: The matrix representation of the above formula is as follows: Where Ts represents the sensor sampling time interval, and I represents the identity matrix, calculated from... The phase acceleration at time k-1 is used to iterate the above calculation formula by substituting the initial state, thereby updating the position and velocity of the hoisted object in real time and obtaining the state matrix X of the hoisted object at time k in real time. k =(P k V k O k ) T .

3. The hoisting positioning control method based on IMU & GPS fusion positioning and barometer correction according to claim 1, characterized in that, The state matrix after error correction in S2 is: ； in, Let be the displacement vector at time k after error correction. This is the velocity vector at time k after error correction. O is the Euler angle at time k after error correction. k Let X be the Euler angle at time k without error correction. k-1 The state matrix at time k-1 is... Let O be the phase acceleration at time k, and let A and C be the coefficient matrices; k A and C are expressed as follows: 。 4. The hoisting positioning control method based on IMU & GPS fusion positioning and barometer correction according to claim 3, characterized in that, The process in S3 involving Kalman filtering of the data acquired by the GPS positioning unit and the barometer with the corrected data from the IMU inertial sensor unit includes: Longitude and latitude coordinates are obtained via GPS, and altitude data (h) is obtained via barometer. pi Then, the longitude and latitude coordinates are converted into global coordinates to obtain the position matrix D. k = (x gps y gps h pi ) T , where x gps y gps The global coordinates are obtained by transforming the latitude and longitude coordinates acquired by GPS, h pi Altitude data obtained from a barometer; Calculate state prediction: X k = +K k *(D k -H k ) Among them, K k For Kalman gain, and: K k = (H) k H k T +R k ) -1 , Here is the covariance matrix for the state estimation; H k For the measurement matrix, D k These are the measured values ​​of the position coordinates. The covariance of the prior estimate at time k is... covariance; =(I-K k H k ) (I-K k H k ) T +R k To eliminate the drift error in global intra-frame acceleration caused by quaternion errors, the quaternions are updated as follows: Q k =ΔQ k−1 ∗Q k-1 in: In the formula, Q k-1 Let Q be the quaternion at time k-1. k Let k be the quaternion at time k. Let be the quaternion representing the transformation of the relative coordinate system from time t-1 to t; the measurement model is... , where z k For predictive measurement, n k For environmental noise, R k R is the covariance matrix of the environmental noise. k =E(n k， n k T ), H k This is the measurement matrix.

5. The hoisting positioning control method based on IMU & GPS fusion positioning and barometer correction according to claim 4, characterized in that, The specific steps for converting longitude and latitude coordinates to global coordinates include: using the conversion formula provided by Dupree to convert longitude and latitude to global coordinates. φ = 2π−0.5(lat0+lat1)π / 180, (lon0,lat0) is the origin of the geographic coordinates, (lon1,lat1) is the geographic coordinates of a specific point, a is the equatorial radius of the Earth, and c is the polar radius of the Earth.

6. The hoisting positioning control method based on IMU & GPS fusion positioning and barometer correction according to claim 5, characterized in that, If one of them is unavailable in S3, the corrected data obtained by the IMU inertial sensor unit will be directly updated and iterated using quaternions: ； ΔQ k-1 = ; 。

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