CN115655311A - Ackerman robot odometer calibration method based on scanning matching - Google Patents
Ackerman robot odometer calibration method based on scanning matching Download PDFInfo
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Abstract
The invention provides an ackerman robot odometer calibration method based on scanning matching, which is characterized in that point cloud data of a laser radar and a grid map are matched by using an NDT point cloud matching algorithm to obtain position and pose information of the laser radar at Ts and Te moments; acquiring latest frame wheel type odometer data, and interpolating the odometer data through the starting and stopping time stamps of the laser radar point cloud data to obtain odometer position and pose information at Ts and Te moments; laser radar pose increment Y for calculating Ts to Te time i * And odometer pose increment Y from Ts to Te i (ii) a Solving a transformation equation of the laser radar pose increment and the odometer pose increment by adopting linear least square to obtain an approximate solution
According to an approximate solution
Carrying out odometer calibration; the invention does not preset the motion trail and motion environment of the robot, and the calibration result is more universal; the odometer calibration is not carried out by artificially measuring the wheel distance and the wheel diameter, so that the artificial measurement error is reduced.
Description
Technical Field
The invention relates to the technical field of calibration of the number of robot parameters, in particular to a calibration method of an ackerman type robot odometer based on scanning matching.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
With the increasing maturity And continuous development of the indoor Mapping And positioning technology And the sensor technology of the 2D laser SLAM (Simultaneous Localization And Mapping), robots with various chassis are gradually applied. The current chassis mainly comprises a single steering wheel, double steering wheels, four steering wheels, an Ackerman model, a Mecanum wheel model and the like, and the chassis realizes the mapping and positioning of the AGV by installing sensors such as a laser radar, a gyroscope, a wheel type odometer and the like.
With the development of the robot industry, the precision requirement on the robot is higher and higher at present, but because the precision of the odometer and the laser radar can be influenced by factors such as wheel abrasion caused by mechanical production, installation and configuration and long-term use of the robot, the construction and positioning precision of the robot is further influenced, the running track of the robot is influenced, and the industrial requirement cannot be met, so that the calibration of sensors such as the odometer and the laser radar on the robot is very necessary.
The inventor finds that most of the conventional odometer calibration methods are used for calibrating an odometer of a single-steering-wheel and two-wheel differential robot, the methods for calibrating the odometer of an ackerman model mobile robot and a laser radar sensor are less, and the universality of other odometers and laser radar sensors for the ackerman model mobile robot is poor due to the fact that the motion structure of the ackerman model is greatly different from that of other differential two-wheel robots.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a calibration method of an ackerman type robot odometer based on scanning matching, which is not used for calibrating the odometer by directly calibrating the distance between wheels and the diameter of the wheels of the robot, but is used for obtaining the position and attitude increment of the ackerman type mobile robot at the Ts moment and the Te moment based on an NDT point cloud matching algorithm, calculating the position and attitude increment of the odometer at the Ts moment and the Te moment based on a motion model of the ackerman type mobile robot, obtaining a linear conversion equation by comparing the position and attitude increment of a laser radar and the odometer, and solving a general solution
And finally, calibrating the odometer.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a calibration method of an ackermann robot odometer based on scanning matching.
A calibration method of an ackerman type robot odometer based on scanning matching comprises the following processes:
acquiring current frame laser radar point cloud data, and matching the laser radar point cloud data with a grid map by using an NDT point cloud matching algorithm to obtain laser radar position and pose information at the Ts moment and the Te moment;
acquiring latest frame wheel type odometer data, and interpolating the odometer data through the starting and stopping time stamps of the laser radar point cloud data to obtain odometer position and pose information at the Ts moment and the Te moment;
calculating laser radar pose increment Y from Ts moment to Te moment i * And odometer pose increment Y from Ts moment to Te moment i ；
Using linear least squaresSolving a transformation equation of the laser radar pose increment and the odometer pose increment to obtain an approximate solution
According to an approximate solution
And (5) carrying out odometer calibration.
The invention provides a calibration system of an ackerman type robot odometer based on scanning matching.
An ackermann-type robot odometer calibration system based on scan matching, comprising:
a lidar pose information generation module configured to: acquiring current frame laser radar point cloud data, and matching the laser radar point cloud data with a grid map by using an NDT point cloud matching algorithm to obtain laser radar position and pose information at the Ts moment and the Te moment;
an odometer pose information generation module configured to: acquiring latest frame wheel type odometer data, and interpolating the odometer data through the starting and stopping time stamps of the laser radar point cloud data to obtain odometer position and pose information at the Ts moment and the Te moment;
a pose increment calculation module configured to: calculating laser radar pose increment Y from Ts moment to Te moment i * And mileometer pose increment Y from Ts moment to Te moment i ；
An odometer calibration module configured to: solving a transformation equation of the laser radar pose increment and the odometer pose increment by adopting linear least square to obtain an approximate solution
According to an approximate solution
And (5) carrying out odometer calibration.
A third aspect of the invention provides a computer readable storage medium having stored thereon a program which, when executed by a processor, carries out the steps of the method for odometry calibration of an ackermann-type robot based on scan matching according to the first aspect of the invention.
A fourth aspect of the present invention provides an electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, the processor implementing the steps of the scanning matching based ackermann-type robot odometer calibration method according to the first aspect of the present invention when executing the program.
Compared with the prior art, the invention has the beneficial effects that:
1. the ackerman type robot odometer calibration method based on scanning matching adopts laser radar position and orientation data obtained by a point cloud matching algorithm based on NDT orthogonal transformation, and greatly improves positioning accuracy.
2. The ackerman type robot odometer calibration method based on scanning matching does not preset the motion trail and the motion environment of the robot, and the calibration result is more universal; the odometer calibration is not carried out by artificially measuring the wheel distance and the wheel diameter, so that the artificial measurement error is reduced.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a schematic flow chart of a calibration method of an ackermann-type robot odometer based on scan matching according to embodiment 1 of the present invention.
Fig. 2 is a schematic view of an ackermann chassis motion structure model provided in embodiment 1 of the present invention.
Fig. 3 is a schematic view of a process of odometer pose transformation provided in embodiment 1 of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments of the invention may be combined with each other without conflict.
Example 1:
as shown in fig. 1, embodiment 1 of the present invention provides a calibration method for an ackermann-type robot odometer based on scan matching, which performs calibration of the odometer by using data of a lidar and the odometer installed on an ackermann-type mobile robot.
Two points of attention need to be paid to the method using the present embodiment:
1) The odometry data and the lidar data need to maintain consistency of the timestamps;
2) And during calibration, the environment with more characteristics is selected as far as possible, so that the laser radar data obtained by using NDT point cloud matching is more accurate.
Specifically, the method comprises the following steps:
acquiring current frame laser radar point cloud data, and matching the laser radar point cloud data with a grid map by using an NDT point cloud matching algorithm to obtain laser radar position and pose information at the Ts moment and the Te moment;
acquiring the latest frame of wheel-type odometer data, and interpolating the odometer data through the starting and stopping time stamps of the laser radar point cloud data to obtain the position and pose information of the odometer at the Ts moment and the Te moment;
laser radar pose increment Y for calculating time from Ts to Te i * And odometer pose increment Y from Ts moment to Te moment i ；
Solving a transformation equation of the laser radar pose increment and the odometer pose increment by adopting linear least square to obtain an approximate solution
According to an approximate solution
And (5) carrying out odometer calibration.
More specifically, the method comprises the following steps:
s1: laser scanning matching algorithm based on NDT (named data transfer)
Scanning and matching: the laser scanning matching algorithm is used for converting actually scanned laser data into a global coordinate system for matching by comparing the laser pose obtained by the global map with the laser data obtained by the local map of the robot, so as to obtain the pose transformation between a sub-map and the global map; scan matching algorithms sometimes perform scan matching between point clouds by searching for points, lines, triangles, and other features, and here Normal Distribution Transforms (NDT) are used to characterize the distance scan.
NDT algorithm: the basic idea of the NDT algorithm is to firstly perform rasterization processing on point clouds to be registered, divide the point clouds to grids with specified sizes, construct a probability distribution function of each grid in a normal distribution mode, and then optimize and solve optimal transformation parameters to enable the probability density distribution of source point clouds to be maximum so as to realize the optimal matching between the two point clouds.
The detailed steps of the NDT algorithm are as follows:
(1) Firstly, dividing a space occupied by source point cloud into two-dimensional grids with specified sizes, and then calculating normal distribution parameters (mean value q and variance sigma) of each grid;
wherein: Σ represents the covariance matrix, q represents the mean of all grids, and the space occupied is divided into two-dimensional grids with n points, denoted as { x } i ,i＝1,2,...,n}。
(2) Calculating a probability distribution model of the grid;
(3) Performing coordinate transformation on all points in the point cloud P to be registered according to the following formula, and transforming the point cloud to be matched into a reference coordinate system;
wherein: defining the transformation parameters between the source point cloud and the point cloud to be matched as p = [ t = x t y θ] T Before the first matching, each component in the transformation parameters may be set to 0, and for any point p in the point cloud to be registered, the transformation parameters p may be used to convert the point p into a point p' in the source point cloud.
(4) Calculating the probability of each conversion point falling in the corresponding grid according to the normal distribution parameters, namely determining the normal distribution of each grid;
(5) Calculating NDT registration confidence score (p): calculating the probability of the corresponding points falling in the corresponding grid cells and summing to obtain the matching confidence score (p) of the two point clouds;
wherein: x' i ＝T(x i And p) i is the mapping point number of the coordinate transformation.
(6) Optimization was performed using a Hessian matrix: as a part of a scanning matching algorithm, the maximization of score (p) is guaranteed as far as possible, the aim is to obtain the gradient of s and a Hessian matrix, the matching confidence score (p) can be optimized by a gradient descent method or a Newton method, and the Newton method is used for optimizing an objective function;
let b = x' i -q i Then there is
According to the chain-type derivation rule and the vector and matrix derivation formula, the gradient of s can be obtained:
solving about variable p i 、p j Second order partial derivative of (1):
according to the transformation equation, the vector of the second derivative of the transformation parameter p by b can be expressed as:
(7) And (4) jumping to the step (3) to continue downward execution until convergence is reached, so that the laser radar pose of the Ackerman model mobile robot at the Ts moment and the Te moment based on NDT point cloud matching is obtained
The pose increment from the Ts moment to the Te moment can be calculated through the laser radar poses at the Ts moment and the Te moment:
s2: ackerman structure kinematics model
The most common indoor robot is a two-wheel differential structure robot, which uses two rear wheels as power wheels and a universal wheel in front as a front wheel. The ackerman model mobile robot and the two-wheel differential chassis structure robot are mainly different in that the ackerman front wheels are two common one-way wheels, and are not universal wheels or omnidirectional wheels of the two-wheel differential model mobile robot.
The motion of the ackermann model mobile robot comprises two major core components: (1) the rotating structure of the front wheel is used for controlling the steering of the front wheel; (2) and a differential for driving the rear wheels and controlling differential motion of the rear wheels.
The turning model of the Ackermann mobile robot can be represented as shown in figure 2 by the turning characteristics of the Ackermann mobile robot, wherein the wheelbase between the front axle and the rear axle of the Ackermann model is L, the distance between the two rear wheels of the vehicle is 2d, the angle beating value of the two front wheels of the vehicle is theta, and the linear velocity of the rear wheel on the left side of the vehicle is v L V, the linear speed of the right rear wheel of the vehicle R The linear velocity of the vehicle is ν, the angular velocity is ω, the turning radius is R, the center c of the rear axle is the base coordinate system of the ackermann model mobile robot, and the point O is the instantaneous rotation center of the ackermann model mobile robot.
The angular velocity and linear velocity of the Ackermann model mobile robot can be obtained from the model and parameters given in figure 2 in the two back wheels of the Ackermann model mobile robot during the analysis process and the two wheels of the differential chassis;
the front two wheels of the Ackerman model mobile robot are driven by a steering engine, and the angle value of the steering engine is calculated by the following equation;
finally obtaining a front wheel steering engine angle value:
the kinematic model of the ackermann model mobile robot is thus obtained as follows:
combining the robot kinematics model information, according to a formula:
to the machineCarrying out pose estimation by motion iterative integration of a human from a start time Ts to a cut-off time Te, wherein theta oTs Steering angle at time Ts, theta oTe Steering angle at time Te, v oTe Linear velocity of vehicle at time Te, x oTe Odometer x-coordinate, y, for time Te oTe Y coordinate of odometer at time Te, x oTs Odometer x coordinate at time Ts, y oTs Odometer y coordinate at time Ts, d Te Half the distance between the two rear wheels of the vehicle at time Te, d Ts At time Ts, ω is the angular velocity of the robot, and v represents the velocity of the robot.
The odometer pose increment can be obtained by the odometer pose from the start time Ts to the end time Te:
the process of odometer pose transformation of the ackermann model mobile robot is shown in fig. 3.
S3: ackerman type robot odometer calibration based on scanning matching
Laser radar data pose increment in a certain time obtained by the first part and the second part
And odometer pose increment
And (3) expressing a linear transformation equation by adopting the laser radar data and the pose increment of the odometer:
Y i * ＝X*Y i
wherein the matrix X can be used
To indicate that is
The following system of equations can then be derived:
now, as long as the matrix X is solved, the odometer pose increment can be converted into the odometer increment with the laser data precision through a linear conversion equation, and the equation set is written into a matrix form:
can use
To represent the frame data, each frame data forms one such equation set, and the odometer calibration receives n frame data to form an over-determined equation set, which may be used
Is shown, wherein:
because the overdetermined system of equations has no unique solution, an approximate solution can be calculated using linear least squares
Which corresponds to the projection of matrix X onto vector space a, so it is the closest solution.
Example 2:
the embodiment 2 of the invention provides an ackerman type robot odometer calibration system based on scanning matching, which comprises:
a lidar pose information generation module configured to: acquiring current frame laser radar point cloud data, and matching the laser radar point cloud data with a grid map by using an NDT point cloud matching algorithm to obtain laser radar position and pose information at the Ts moment and the Te moment;
an odometer pose information generation module configured to: acquiring latest frame wheel type odometer data, and interpolating the odometer data through the starting and stopping time stamps of the laser radar point cloud data to obtain odometer position and pose information at the Ts moment and the Te moment;
a pose delta calculation module configured to: calculating laser radar pose increment Y from Ts moment to Te moment i * And odometer pose increment Y from Ts moment to Te moment i ；
An odometer calibration module configured to: solving a transformation equation of the laser radar pose increment and the odometer pose increment by adopting linear least square to obtain an approximate solution
According to an approximate solution
And (5) carrying out odometer calibration.
The working method of the system is the same as the calibration method of the ackermann robot odometer based on scanning matching provided by the embodiment 1, and the detailed description is omitted here.
Example 3:
embodiment 3 of the present invention provides a computer-readable storage medium having stored thereon a program which, when executed by a processor, implements the steps in the scan matching based ackermann robot odometer calibration method according to embodiment 1 of the present invention.
Example 4:
embodiment 4 of the present invention provides an electronic device, which includes a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the calibration method for the ackermann-type robot odometer based on scan matching according to embodiment 1 of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A calibration method of an ackerman type robot odometer based on scanning matching is characterized in that:
the method comprises the following steps:
acquiring current frame laser radar point cloud data, and matching the laser radar point cloud data with a grid map by using an NDT point cloud matching algorithm to obtain laser radar position and pose information at the Ts moment and the Te moment;
acquiring latest frame wheel type odometer data, and interpolating the odometer data through the starting and stopping time stamps of the laser radar point cloud data to obtain odometer position and pose information at the Ts moment and the Te moment;
calculating laser radar pose increment Y from Ts moment to Te moment i * And odometer pose increment Y from Ts moment to Te moment i ；
Solving a transformation equation of the laser radar pose increment and the milemeter pose increment by adopting linear least squares to obtain an approximate solution
According to an approximate solution
And (5) carrying out odometer calibration.
2. The method for calibration of an ackermann-type robot odometer based on scan matching according to claim 1, further comprising:
the transformation equation of the laser radar pose increment and the odometer pose increment is as follows: y is i * ＝X*Y i ；
It is written in matrix form as:
to obtain
3. The method for calibration of an ackermann-type robot odometer based on scan matching according to claim 1, further comprising:
matching the laser radar point cloud data with a grid map by using an NDT point cloud matching algorithm, comprising:
dividing the space occupied by the source point cloud into two-dimensional grids with set sizes, and then calculating the normal distribution parameter of each grid;
calculating a probability distribution model of the grid;
carrying out coordinate transformation on all points in the point cloud to be registered, and transforming the point cloud to be matched to a reference coordinate system;
calculating the probability of each conversion point falling in the corresponding grid according to the normal distribution parameters, and determining the normal distribution of each grid;
calculating the probability of the corresponding point falling in the corresponding grid cell and summing to obtain the matching confidence coefficient of the two point clouds;
and optimizing the matching confidence coefficient by using the maximum matching confidence coefficient as a target through a gradient descent method or a Newton method until convergence is achieved, and finally obtaining the laser radar pose information at the Ts moment and the Te moment.
4. The method for calibration of an ackermann-type robot odometer based on scan matching according to claim 1, further comprising:
combined with robot kinematics model according to formula
Carrying out pose estimation on the motion iteration integral of the robot from the start time Ts to the cut-off time Te to obtain odometer pose information at the time Ts and the time Te;
a robot kinematics model comprising:
wherein L is the wheelbase between the front axle and the rear axle of the Ackerman model, 2d is the distance between the two wheels at the rear of the vehicle, theta is the angle value of the two wheels at the front of the vehicle, and v is the angle value of the two wheels at the front of the vehicle L Is the linear velocity of the left rear wheel v of the vehicle R V is the linear velocity of the rear wheel at the right side of the vehicle, omega is the angular velocity of the vehicle, theta oTs Is T s Steering angle of time, theta oTe Is T e Steering angle at time, v oTe Is T e Linear velocity of vehicle at time, x oTe Is T e Odometer x-coordinate of time of day, y oTe Is T e Y-coordinate, x of odometer at time of day oTs Is T s Odometer x-coordinate of time of day, y oTs Is T s Y coordinate of the odometer at the moment, d Te Half the distance between the two rear wheels of the vehicle at time Te, d Ts Is T s Half the distance between the two rear wheels of the vehicle at the moment.
5. The utility model provides an ackermann type robot odometer calibration system based on scanning matching which characterized in that:
the method comprises the following steps:
a lidar pose information generation module configured to: acquiring current frame laser radar point cloud data, and matching the laser radar point cloud data with a grid map by using an NDT point cloud matching algorithm to obtain laser radar position and pose information at the Ts moment and the Te moment;
an odometer pose information generation module configured to: acquiring latest frame wheel type odometer data, and interpolating the odometer data through the starting and stopping time stamps of the laser radar point cloud data to obtain odometer position and pose information at the Ts moment and the Te moment;
a pose increment calculation module configured to: laser radar pose increment Y for calculating time from Ts to Te i * And odometer pose increment Y from Ts moment to Te moment i ；
An odometer calibration module configured to: solving a transformation equation of the laser radar pose increment and the odometer pose increment by adopting linear least square to obtain an approximate solution
According to an approximate solution
And (5) carrying out odometer calibration.
6. The scanning matching based ackermann-type robotic odometer calibration system of claim 5, wherein:
the transformation equation of the laser radar pose increment and the odometer pose increment is as follows: y is i * ＝X*Y i ；
It is written in the form of a matrix,comprises the following steps:
to obtain
7. The scanning matching based ackermann-type robotic odometer calibration system of claim 5, wherein:
matching the laser radar point cloud data with a grid map by using an NDT point cloud matching algorithm, comprising:
dividing the space occupied by the source point cloud into two-dimensional grids with set sizes, and then calculating the normal distribution parameter of each grid;
calculating a probability distribution model of the grid;
carrying out coordinate transformation on all points in the point cloud to be registered, and transforming the point cloud to be matched to a reference coordinate system;
calculating the probability of each conversion point falling in the corresponding grid according to the normal distribution parameters, and determining the normal distribution of each grid;
calculating the probability of the corresponding point falling in the corresponding grid cell and summing to obtain the matching confidence coefficient of the two point clouds;
and optimizing the matching confidence coefficient by using the maximum matching confidence coefficient as a target through a gradient descent method or a Newton method until convergence is achieved, and finally obtaining the laser radar pose information at the Ts moment and the Te moment.
8. The scanning matching based ackermann-type robotic odometer calibration system of claim 5, wherein:
combined with robot kinematics model according to formula
Performing pose estimation on the motion iteration integral of the robot from the start time Ts to the cut-off time Te to obtain the time Ts and the time TeOdometer pose information;
a robot kinematics model comprising:
wherein L is the wheelbase between the front axle and the rear axle of the Ackerman model, 2d is the distance between the two wheels at the rear of the vehicle, theta is the angle value of the two wheels at the front of the vehicle, and v is the angle value of the two wheels at the front of the vehicle L Is the linear velocity of the left rear wheel v of the vehicle R The linear velocity of the rear wheel on the right side of the vehicle is, v is the linear velocity of the vehicle, omega is the angular velocity of the vehicle, and theta is oTs Is T s Steering angle of time, theta oTe Is T e Steering angle at time, v oTe Is T e Linear velocity of vehicle at time, x oTe Is T e Odometer x-coordinate of time of day, y oTe Is T e Y-coordinate, x of odometer at time of day oTs Is T s Odometer x-coordinate of time of day, y oTs Is T s Y coordinate of the odometer at the moment, d Te Half the distance between the two rear wheels of the vehicle at time Te, d Ts Is T s Half the distance between the two rear wheels of the vehicle at the moment.
9. A computer readable storage medium having a program stored thereon, wherein the program when executed by a processor implements the steps in the method for odometry calibration of an ackermann robot based on scan matching according to any one of claims 1 to 4.
10. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method for odometry calibration for an ackermann-type robot based on scan matching according to any one of claims 1 to 4.
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| CN116148824A (en) * | 2023-04-17 | 2023-05-23 | 机科发展科技股份有限公司 | Automatic calibration system and method for navigation parameters of laser unmanned forklift |
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| CN116148824A (en) * | 2023-04-17 | 2023-05-23 | 机科发展科技股份有限公司 | Automatic calibration system and method for navigation parameters of laser unmanned forklift |
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