Online single-steering wheel AGV parameter calibration method and system based on QR code navigation

Abstract

The present invention provides an online single-steering wheel AGV parameter calibration method and system based on QR code navigation. According to the acquired AGV posture relative to the QR code and the QR code number, a first posture increment obtained according to the QR code is obtained; the wheel odometer data is interpolated according to the timestamps when the QR code is detected twice to obtain a second posture increment obtained according to the wheel odometer; the laser radar data is interpolated according to the timestamps when the QR code is detected twice to obtain a third posture increment obtained according to the laser radar; internal parameter calibration is performed according to the first posture increment and the second posture increment, and external parameter calibration is performed according to the first posture increment and the third posture increment; the present invention combines the AGV posture increment obtained by the QR code to perform internal and external parameter calibration, and has a higher accuracy than the strategy of simply using the posture increment obtained by the laser radar for calibration.

Description

Technical Field

[0001] The present invention relates to the field of AGV navigation control technology, and in particular to an online single-steering wheel AGV parameter calibration method and system based on QR code navigation. Background Art

[0002] The statements in this section merely provide background art related to the present invention and do not necessarily constitute prior art.

[0003] For the internal and external parameters of the wheel odometry and lidar, due to factors such as mechanical manufacturing and installation errors, device deformation and wheel wear caused by long-term operation of the robot, the built-in parameters of the wheel odometry and the external parameters between the wheel odometry and lidar will change.

[0004] The inventors found that existing single steering wheel models are all based on offline calibration of lidar, which has large errors, low efficiency and low accuracy, and cannot meet the requirements of real-time high-precision calibration of AGV automation in indoor environments. Summary of the Invention

[0005] In order to address the shortcomings of the existing technology, the present invention provides an online single-steering wheel AGV parameter calibration method and system based on QR code navigation, which combines the AGV posture increments obtained by QR code to calibrate internal and external parameters. Compared with the strategy of simply using the posture increments obtained by lidar for calibration, the method has higher accuracy.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The first aspect of the present invention provides an online single-steering wheel AGV parameter calibration method based on QR code navigation.

[0008] An online single-steering wheel AGV parameter calibration method based on QR code navigation includes the following steps:

[0009] Obtain the AGV's position relative to the QR code and the QR code number obtained from two consecutive detections;

[0010] According to the acquired posture of the AGV relative to the QR code and the number of the QR code, the first posture increment obtained according to the QR code is obtained;

[0011] Interpolate the wheel odometry data according to the timestamps of the two times when the QR code is detected to obtain the second pose increment obtained by the wheel odometry;

[0012] Interpolate the lidar data based on the timestamps of the two times the QR code was detected to obtain the third pose increment obtained by the lidar meter;

[0013] The intrinsic parameter calibration is performed based on the first pose increment and the second pose increment, and the extrinsic parameter calibration is performed based on the first pose increment and the third pose increment.

[0014] The second aspect of the present invention provides an online single-steering wheel AGV parameter calibration system based on QR code navigation.

[0015] An online single-steering wheel AGV parameter calibration system based on QR code navigation, including:

[0016] The data acquisition module is configured to: obtain the position of the AGV relative to the QR code and the QR code number obtained from two consecutive detections;

[0017] The first position increment calculation module is configured to: obtain the first position increment obtained according to the QR code based on the acquired position of the AGV relative to the QR code and the number of the QR code;

[0018] The second pose increment calculation module is configured to interpolate the wheel odometer data according to the timestamps of the two times when the QR code is detected to obtain a second pose increment obtained according to the wheel odometer;

[0019] The third pose increment calculation module is configured to: interpolate the lidar data according to the timestamps when the two QR codes are detected to obtain a third pose increment obtained according to the lidar data;

[0020] The internal and external parameter calibration module is configured to perform internal parameter calibration according to the first pose increment and the second pose increment, and perform external parameter calibration according to the first pose increment and the third pose increment.

[0021] The third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the online single-steering wheel AGV parameter calibration method based on QR code navigation as described in the first aspect of the present invention.

[0022] The fourth aspect of the present invention provides an electronic device, comprising a memory, a processor, and a program stored in the memory and runnable on the processor. When the processor executes the program, the steps in the online single-steering wheel AGV parameter calibration method based on QR code navigation as described in the first aspect of the present invention are implemented.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] 1. The online single-steering wheel AGV parameter calibration method and system based on QR code navigation described in the present invention combines the AGV posture increments obtained by QR code to calibrate internal and external parameters. Compared with the strategy of simply using the posture increments obtained by lidar for calibration, it has a higher accuracy rate.

[0025] 2. The online single-steering wheel AGV parameter calibration method and system based on QR code navigation described in the present invention adopts an online calibration strategy and uses the calibration results for subsequent map construction. It can update parameters and correct errors in real time, avoiding errors in mapping and positioning caused by inaccurate wheel odometer parameters, wheel wear, and inaccurate radar installation position relative to the robot center.

[0026] 3. The online single-steering wheel AGV parameter calibration method and system based on QR code navigation described in the present invention assumes that the nominal values ​​of the parameters are known a priori, the estimated adjustment amount is relatively small, the linear error is small, and it can run unattended without human intervention. No instruments need to be pre-calibrated, and the nominal parameters do not need to be used as initial guesses. The global optimal solution exists in a closed form, and the robot's trajectory can be freely selected.

[0027] 4. The online single-steering wheel AGV parameter calibration method and system based on QR code navigation described in the present invention can first calibrate the internal parameters, and then use the calibrated internal parameters to perform more accurate track calculation. The wheel odometer posture inferred by the accurate track calculation is used as the prior posture for radar posture calculation, and the inferred radar posture increment has a higher accuracy rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0029] Figure 1 This is a schematic diagram of the AGV structure provided in Example 1 of the present invention;

[0030] Figure 2 A schematic diagram of the process of the online single-steering wheel AGV parameter calibration method based on QR code navigation provided in Example 1 of the present invention;

[0031] Figure 3 Schematic diagram of dead reckoning for a wheel odometer provided in Example 1 of the present invention;

[0032] Figure 4 Schematic diagram of the posture transformation of the wheel odometer provided in Example 1 of the present invention;

[0033] Figure 5 A schematic diagram of coordinate system conversion provided in Example 1 of the present invention;

[0034] Among them, 1-steering wheel; 2-universal wheel. DETAILED DESCRIPTION

[0035] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0036] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0037] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0038] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0039] Example 1:

[0040] Embodiment 1 of the present invention provides an online single-steering wheel AGV parameter calibration method based on QR code navigation. Figure 1 The following parameters of the single steering wheel AGV shown are estimated:

[0041] (1) Intrinsic parameters: the radius of the steering wheel r1 and the steering wheel angle zero drift β1;

[0042] (2) External parameters: The installation posture parameter of the laser radar is l = [lx, ly, lθ], where lx, ly, and lθ are the x-coordinate, y-coordinate, and direction angle of the laser radar in the AGV coordinate system, respectively.

[0043] The AGV has a steering wheel 1 with both steering and driving capabilities, and the speed and control are achieved by setting the speed and steering angle, and four driven universal wheels 2;

[0044] The AGV reference frame {R} is placed at the center point O of the AGV's code reader. The robot's forward direction is the positive x-axis direction, and the steering wheels are distributed on the AGV's x-axis. The overall posture and speed are logically described by the motion state of the coordinate system's origin.

[0045] The world coordinate system {W} is fixed in the environment, and the lidar coordinate system {S} is fixed to the vehicle body, so its relative posture remains constant over time. Let p = [px, py, pθ] represent the state vector of the position and orientation of the AGV coordinate system {R} in the world coordinate system {W};

[0046] l = [lx, ly, lθ] is the constant relative pose of the sensor coordinate system {S} relative to the AGV coordinate system {R}. The AGV uses QR code positioning and lidar fusion positioning, using lidar positioning in a large area and QR code positioning in cluttered areas where precise positioning is required or where the lidar effect is poor, to achieve precise positioning of the AGV.

[0047] like Figure 2 As shown, the calibration method includes the following steps:

[0048] S1: After detecting the QR code, the relative position of the AGV relative to the QR code and the QR code number are calculated based on the information read by the code reader;

[0049] S2: querying the pre-stored pose information of the corresponding numbered QR code in the world coordinate system according to the QR code number;

[0050] S3: Since the position of the AGV relative to the QR code read by the code reader is not in the same coordinate system, it is necessary to determine whether the currently detected QR code is the same as the last detected QR code;

[0051] If yes, the AGV poses read twice by the QR code are based on the same coordinate system, and the pose increment d can be directly calculated. k (i.e. the first pose increment); otherwise, the position information of the AGV relative to the QR code needs to be converted to the world coordinate system before the pose increment d can be calculated. k (i.e. first position increment);

[0052] S4: Interpolate the wheel odometer data based on the timestamps of the two QR code detections and calculate the AGV pose increment r between the two timestamps based on the wheel odometer k (i.e., the second pose increment);

[0053] S5: Using the first pose increment d k and the second pose increment r k Calculate internal reference;

[0054] S6: interpolating the lidar data to obtain interpolated lidar data, and calculating the lidar pose corresponding to the current moment based on the interpolated lidar data;

[0055] S7: Calculate the LiDAR pose increment s based on the LiDAR pose between the two timestamps of the QR code detection. k (i.e., the third posture increment);

[0056] S8: Using the first pose increment d k and the third pose increment s kCalculate external parameters.

[0057] Specifically, the AGV posture is calculated based on the detection results of the QR code, and the first posture increment d is obtained. k ,include:

[0058] (1) Arrange the QR code in advance and store the pose information of the QR code in the world coordinate system;

[0059] (2) When the AGV passes near the QR code, the dedicated industrial AGV positioning QR code reader can obtain the position of the AGV center relative to the QR code. At the same time, the reader can also read the QR code number and other information to obtain the position of the QR code in the world coordinate system. Combined with the position of the AGV relative to the QR code, the position of the AGV in the world coordinate system can be obtained. k ;

[0060] (3) Calculate the robot's posture q when the QR code is detected for the kth time k The robot's position q relative to the last time the QR code was detected k-1 The pose increment d k .

[0061] Dead Reckoning for Wheel Odometry, including:

[0062] like Figure 3 As shown in the figure, the steering wheels are distributed on the x-axis of the AGV, and the AGV rotation center is O. The movement of the AGV can be understood as a rotational motion with a radius R around the instantaneous center O'. When it moves in a straight line, the radius R = ∞, and the instantaneous center of motion is at infinity. The wheel angle and wheel speed determine the movement direction of the AGV.

[0063] The kinematic model of a single-steering-wheel AGV is:

[0064]

[0065] Where, v x is the component of the AGV linear velocity in the x direction of the AGV coordinate system, ω is the AGV angular velocity, v1 is the linear velocity of the steering wheel, α1 is the steering angle of the steering wheel, and L is the distance between the steering wheel and the center of the AGV.

[0066] Due to the existence of zero drift β1, the value fed back by the steering wheel steering mechanism sensor cannot reflect the actual steering angle of the current steering wheel, thus causing an erroneous estimation of the robot's motion state. After taking zero drift into account, formula (1) is transformed into:

[0067]

[0068] The calculation process of wheel odometer posture transformation is as follows Figure 4As shown, the wheel odometer data is obtained, and the timestamps when the QR code is detected twice are set as the first timestamp and the second timestamp;

[0069] Determine whether the timestamp of the maximum wheel odometer data obtained is greater than the timestamp when the QR code was detected last time. If not, return to obtain new wheel odometer data; if so, proceed to the next step;

[0070] Discretize the wheel odometer data, interpolate the discretized wheel odometer data according to the first timestamp and the second timestamp, and calculate the second pose increment r obtained by the wheel odometer between the first timestamp and the second timestamp k .

[0071] Internal reference calibration, including:

[0072] like Figure 5 The figure shows the conversion diagram of each coordinate system. The relative angle increment calculated by the wheel odometer between two adjacent frames of data is Equal to [t k-1 ,t k ]Use QR code to get rotation increment at time interval

[0073] Direction increment The expression evaluates to:

[0074]

[0075] Assume that the steering angle α1(t) is k ,t k+1 ] is constant, the above formula can be transformed into:

[0076]

[0077] Where, For the time interval [t k ,t k+1 ]The rotation angle of the inner steering wheel drive wheel can be converted from the feedback encoder data.

[0078] Separate the calibration parameters from the input data and put them into the matrix X:

[0079]

[0080] According to the given time [t k ,t k+1 ], the feedback data of n wheel odometer sensors (steering angle α1(t), driving wheel rotation angle θ1) are obtained from equations (4) and The linear system AX = b can be obtained, where A is the matrix form of the k wheel odometry sensor data:

[0081]

[0082] Where, Indicates t k and t k+1 The driving wheel rotation angle derived from the wheel odometry data between the timestamps, Indicates t k and t k+1 The steering angle derived from the wheel odometry data between the timestamps.

[0083]

[0084] in, t k and t k+1 The rotational component of the robot pose increment obtained by QR code positioning between timestamps.

[0085] If there are more than two independent equations, the linear system is overdetermined. Therefore, in order to obtain the value of X that better meets the given conditions, it is necessary to calculate the minimum error ‖AX-b‖ 2 , the general solution X * =arg min‖AX-b‖ 2 , can be solved by calculating the Moore-Penrose pseudo-inverse of matrix A, and the calculated internal parameters are:

[0086]

[0087] represents the general solution X * The element in the i-th row of , 1≤i≤2.

[0088] External parameter calibration, including:

[0089] After solving the intrinsic parameter calibration, the intrinsic parameters can be used to calibrate the extrinsic parameters. Because when the lidar calculates its own position through the positioning algorithm, it needs the wheel odometry information to provide a priori position. The priori position obtained using the calibrated intrinsic parameters is more accurate, making the lidar positioning more accurate.

[0090] s k d k The relationship between and l can be expressed using the standard composition operator on the special Euclidean Lie algebra se(2) and inverse operator To express:

[0091]

[0092] but Figure 5 The posture transformation relationship can be expressed as:

[0093]

[0094] Where, k represents the kth time the QR code is detected, s k The lidar data is used to obtain the lidar pose increment through the lidar positioning algorithm.

[0095] make:

[0096]

[0097] Construct a sum-of-squares function:

[0098]

[0099] Where, is the kth frame data error The position component The least squares method is used to solve (lx, ly, lθ) to minimize E, thereby calibrating the external parameters (lx, ly, lθ).

[0100] Example 2:

[0101] Embodiment 2 of the present invention provides an online single-steering wheel AGV parameter calibration system based on QR code navigation, including:

[0102] The data acquisition module is configured to: obtain the position of the AGV relative to the QR code and the QR code number obtained from two consecutive detections;

[0103] The first position increment calculation module is configured to: obtain the first position increment obtained according to the QR code based on the acquired position of the AGV relative to the QR code and the number of the QR code;

[0104] The second pose increment calculation module is configured to interpolate the wheel odometer data according to the timestamps of the two times when the QR code is detected to obtain a second pose increment obtained according to the wheel odometer;

[0105] The third pose increment calculation module is configured to: interpolate the lidar data according to the timestamps when the two QR codes are detected to obtain a third pose increment obtained according to the lidar data;

[0106] The internal and external parameter calibration module is configured to perform internal parameter calibration according to the first pose increment and the second pose increment, and perform external parameter calibration according to the first pose increment and the third pose increment.

[0107] The working method of the system is the same as the online single-steering wheel AGV parameter calibration method based on QR code navigation provided in Example 1, and will not be repeated here.

[0108] Example 3:

[0109] Embodiment 3 of the present invention provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, the steps of the online single-steering wheel AGV parameter calibration method based on QR code navigation as described in Embodiment 1 of the present invention are implemented.

[0110] Example 4:

[0111] Embodiment 4 of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and runnable on the processor. When the processor executes the program, the steps in the online single-steering wheel AGV parameter calibration method based on QR code navigation as described in Embodiment 1 of the present invention are implemented.

[0112] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) containing computer-usable program code.

[0113] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0114] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0115] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0116] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0117] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. An online single-steering wheel AGV parameter calibration method based on QR code navigation, characterized in that: The following processes are included: Obtain the AGV's position relative to the QR code and the QR code number obtained from two consecutive detections; According to the acquired posture of the AGV relative to the QR code and the number of the QR code, the first posture increment obtained according to the QR code is obtained; Interpolate the wheel odometry data according to the timestamps of the two times when the QR code is detected to obtain the second pose increment obtained by the wheel odometry; Interpolate the lidar data based on the timestamps of the two times the QR code was detected to obtain the third pose increment obtained by the lidar meter; Perform internal calibration based on the first and second pose increments, and perform external calibration based on the first and third pose increments; The internal parameters include: the radius of the steering wheel and the steering wheel angle zero drift, and the external parameters include: the position parameters of the laser radar in the AGV coordinate system.

2. The online single-steering wheel AGV parameter calibration method based on QR code navigation according to claim 1 is characterized in that: According to the acquired AGV posture relative to the QR code and the QR code number, the first position increment obtained according to the QR code is obtained, including: Determine the position of the QR code in the world coordinate system according to the QR code number; When the two detected QR codes are the same, the first position increment obtained according to the QR code is obtained based on the posture of the AGV relative to the QR code obtained from the two detections and the posture of the QR code in the world coordinate system; When the two detected QR codes are not the same, the postures of the AGV relative to the QR code obtained from the two detections are converted to the world coordinate system to obtain the first posture increment obtained according to the QR code.

3. The online single-steering wheel AGV parameter calibration method based on QR code navigation according to claim 1 is characterized in that: The wheel odometry data is interpolated based on the timestamps of the two QR code detections to obtain the second pose increment obtained based on the wheel odometry, including: Get the wheel odometer data and set the timestamps when the QR code is detected twice as the first timestamp and the second timestamp; Determine whether the timestamp of the maximum wheel odometer data obtained is greater than the timestamp when the QR code was detected last time. If not, return to obtain new wheel odometer data; if so, proceed to the next step; The wheel odometry data is discretized, the discretized wheel odometry data is interpolated according to the first timestamp and the second timestamp, and a second pose increment obtained according to the wheel odometry between the first timestamp and the second timestamp is calculated.

4. The online single-steering wheel AGV parameter calibration method based on QR code navigation as claimed in claim 3 is characterized in that: According to the second posture increment, a second relative angle increment is obtained, and the second relative angle increment is equal to a rotation increment obtained using the QR code between two adjacent QR code timestamps.

5. The online single-steering wheel AGV parameter calibration method based on QR code navigation as claimed in claim 4 is characterized in that: The internal parameters include: the radius r1 of the steering wheel and the steering wheel angle zero drift β1; in, represents the general solution X * The i-th row element, 1≤i≤2, X * =argmin‖AX-b‖ 2 , X * To calculate the minimum error ‖AX-b‖ 2 The general solution obtained; t k and t k+1 The rotational component of the robot pose increment obtained by QR code positioning between timestamps, Indicates t k and t k+1 The driving wheel rotation angle derived from the wheel odometry data between the timestamps, Indicates t k and t k+1 The steering angle derived from the wheel odometry data between timestamps, where L is the distance between the steering wheel and the center of the AGV.

6. The online single-steering wheel AGV parameter calibration method based on QR code navigation as claimed in claim 1, characterized in that: The external parameters include: the position parameters l = (lx, ly, lθ) of the laser radar in the AGV coordinate system; Perform extrinsic calibration based on the first and third pose increments, including: Construct a sum-of-squares function: Use the least squares method to solve and minimize E, and get (lx, ly, lθ), is the error of the k-th frame lidar data The position component T stands for transpose.

7. The online single-steering wheel AGV parameter calibration method based on QR code navigation according to claim 6 is characterized in that: Among them, s k is the third pose increment, d k is the first pose increment.

8. An online single-steering wheel AGV parameter calibration system based on QR code navigation, characterized in that: The following processes are included: The data acquisition module is configured to: obtain the position of the AGV relative to the QR code and the QR code number obtained from two consecutive detections; The first position increment calculation module is configured to: obtain the first position increment obtained according to the QR code based on the acquired position of the AGV relative to the QR code and the number of the QR code; The second pose increment calculation module is configured to interpolate the wheel odometer data according to the timestamps of the two times when the QR code is detected to obtain a second pose increment obtained according to the wheel odometer; The third pose increment calculation module is configured to: interpolate the lidar data according to the timestamps when the two QR codes are detected to obtain a third pose increment obtained according to the lidar data; The internal and external parameter calibration module is configured to perform internal parameter calibration based on the first pose increment and the second pose increment, and perform external parameter calibration based on the first pose increment and the third pose increment; the internal parameters include: the radius of the steering wheel and the steering wheel angle zero drift, and the external parameters include: the pose parameters of the laser radar in the AGV coordinate system.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps of the online single-steering wheel AGV parameter calibration method based on QR code navigation are implemented as described in any one of claims 1 to 7.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps in the online single-steering wheel AGV parameter calibration method based on QR code navigation as described in any one of claims 1 to 7 are implemented.

Images