CN112097792A - Ackerman model mobile robot odometer calibration method - Google Patents
Ackerman model mobile robot odometer calibration method Download PDFInfo
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- CN112097792A CN112097792A CN202010888953.1A CN202010888953A CN112097792A CN 112097792 A CN112097792 A CN 112097792A CN 202010888953 A CN202010888953 A CN 202010888953A CN 112097792 A CN112097792 A CN 112097792A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
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Abstract
The invention discloses a method for calibrating an odometer of an Ackerman model mobile robot, which comprises the steps of respectively mounting a wheel type encoder, a laser radar and an IMU on the mobile robot, acquiring the speed of the mobile robot through the wheel type encoder mounted on a motor, and acquiring the motion distance of the robot through integrating the speed; acquiring a rotation angle of the mobile robot within a certain time through the IMU; and acquiring the distance and the angle of the angular point relative to the mobile robot at different sampling moments by using a single angular point in a laser radar tracking environment. And calculating the estimated displacement and the real displacement of the mobile robot according to the acquired related data to obtain an error coefficient, and finishing the calibration of the odometer of the mobile robot. The invention utilizes the high-precision characteristics of the IMU and the laser radar to finish the calibration of the odometer of the robot, improves the pose estimation precision of the robot in the moving process, further improves the precision of the mobile robot in the processes of drawing construction, positioning and navigation, and is applied to the technical field of simultaneous positioning and map construction of the mobile robot.
Description
Technical Field
The invention relates to the technical field of mobile robot odometer calibration, in particular to a calibration method for an Ackerman model mobile robot odometer.
Background
With the rapid development of computer technology, machine vision, artificial intelligence and other technologies, mobile robots have also been studied more deeply and applied increasingly widely. In the field of national defense, unmanned aerial vehicles and unmanned vehicles are used for reconnaissance, information collection and tracking; in the field of logistics, the AGV car becomes an important component of an intelligent logistics system. In the service field, various cleaning robots, greeting robots, catering robots, shopping guide robots, medical robots, and the like are also coming out in succession, and the SLAM technology, which is one of the support technologies, is also being developed. In the SLAM technology, pose estimation of a mobile robot is particularly important, but due to assembly errors of the mobile robot, gaps between teeth, wheel slip in the motion process and the like, when the pose estimation is performed by using odometer information, the mobile robot often has a relatively large error, so that the calibration of the odometer is particularly important.
The traditional odometer calibration method is mostly aimed at two-wheel differential mobile robots driven by two motors, the robot structure is simple, manual distance measurement is mostly used in the process of measuring related distances, the precision is low, the running track of the robot is always preset in the calibration process, the diversity of the motion modes of the robot is reduced, the robot is often greatly different from the actual motion situation, different motion modes often generate different errors, the calibration result is not good in universality, and the efficiency is low. Meanwhile, the ackerman model is complex in structure and is obviously different from a two-wheel differential mobile robot, so that the calibration method for other model mobile robots may not be suitable for the model robot.
Aiming at the defects of the prior art, the invention provides a calibration method of an ackerman model robot odometer, which is mainly used for calibrating the odometer by utilizing a laser radar with higher precision in ranging and an IMU inertia measurement unit with more precise measurement angle. According to the invention, the laser radar and the IMU are taken as references, the trolley displacement in a certain time interval is calculated, the trolley displacement is estimated by using the odometer information, the trolley displacement and the odometer information are compared to obtain an error coefficient, and the displacement estimated by the odometer information is adjusted, so that the aim of correctly estimating the position and the attitude of the trolley is achieved.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a calibration method for an ackerman model mobile robot odometer, which is capable of improving the calibration accuracy and the calibration efficiency of the ackerman model mobile robot odometer calibration by using a laser radar with high accuracy in ranging and an LMU with high accuracy in measuring angles.
In order to achieve the purpose, the invention adopts the following technical scheme:
(1) acquiring a real corner of the mobile robot in a motion process through an IMU (inertial measurement Unit) arranged on the mobile robot;
(2) calculating the movement speed of the mobile robot by the pulse number of the wheel type encoder in unit time, and integrating the speed to obtain the movement distance of the mobile robot in a certain time interval;
(3) obtaining estimated displacement of the mobile robot by using a track presumption algorithm according to the movement distance of the mobile robot and the mobile robot corner obtained by the IMU;
(4) the method comprises the steps of acquiring the distance and the angle of a single characteristic angular point in a laser radar tracking environment relative to a mobile robot at two sampling moments, and calculating the real displacement of the mobile robot by combining IMU corners and utilizing geometric derivation;
(5) and the estimated displacement of the mobile robot is adjusted by comparing the estimated displacement of the mobile robot with the real displacement of the mobile robot to obtain an error coefficient, so that the odometer calibration is realized.
Preferably, in the step (3), the step of acquiring the estimated displacement of the mobile robot is as follows:
(3-1) obtaining the rotating speed omega of the motormWhen the linear velocity of the mobile robot body is vc＝r·ωm(ii) a Wherein r is the radius of the wheel of the mobile robot;
(3-2) integrating the speed of the mobile robot to obtain a movement distance s in a given time interval;
(3-3) taking the middle point of the connecting line of the rear wheels of the mobile robot as a reference point O, and moving the reference point from A (x, y) to B (x ', y') around the point O;
(3-4) obtaining the following data according to a track presumption algorithm:
where s is the distance the trolley is moving in a given time, θ1,θ2Respectively representing attitude angles of the trolley during two times of sampling;
(3-5) calculating the displacement of the mobile robot according to the coordinates of the mobile robot obtained by twice sampling
Preferably, in the step (4), the step of acquiring the real displacement of the mobile robot is as follows:
(4-1) at the starting moment, the laser radar observes the angular point and outputs the relative position relation between the angular point and a radar coordinate system, and the distance between the angular point and the radar coordinate system is d1Angle of alpha1；
(4-2) at the end time, the angular point is at a distance d from the lidar2Angle of alpha2；
(4-3) in the T time, the rotation angle of the robot is delta theta and is obtained by the IMU, and the included angle of the two ranging measurements of the laser radar to the angular point is alpha3Then alpha can be calculated3＝α1+α2+Δα；
(4-4) the displacement of the robot is as follows:
Preferably, in the step (5), the step of obtaining the odometer error coefficient is as follows:
(5-1) calculating error coefficients from a plurality of sets of data obtained by one calibration
Wherein lodomFor the estimated displacement of arrival from the odometer information,/laserIs the true displacement obtained from the lidar information; through multiple calibration, getlAverage value of (2)
(5-2) repeating the step (5-1), controlling the mobile robot to run according to different tracks, and obtaining a plurality of groups
Get
The average value of the average value is more universal
(5-3) in order to
As error coefficients, added to the odometer information estimation
And controlling the trolley to move, and comparing l with llaserAnd verifying the correctness of the coefficient, wherein l is the estimated displacement after calibration.
Compared with the prior art, the invention has the following obvious and prominent substantive characteristics and remarkable technical progress:
1. the reference distance for calibration is obtained by geometrically deriving the laser radar and IMU data, so that the method has high precision and eliminates the interference of artificial ranging;
2. the invention does not preset the motion trail of the robot, so that the motion forms of the robot are diversified, and the calibration result is more universal;
3. the invention can sample and calculate for many times in the process of one-time movement of the robot to obtain a plurality of groups of error coefficients, thereby greatly improving the calibration efficiency.
Drawings
The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention.
Fig. 1 is a diagram of the motion environment required for mobile robot odometer calibration according to the invention.
Fig. 2 is a dynamic model diagram of the ackerman model mobile robot in the ackerman model mobile robot odometer calibration method of the present invention.
Fig. 3 is a schematic diagram of the method for calibrating the odometer of the ackerman model mobile robot according to the present invention, wherein the displacement of the mobile robot is estimated by using a laser radar.
Fig. 4 is a schematic diagram of dead reckoning of the mobile robot according to the ackermann model mobile robot odometer calibration method of the invention.
FIG. 5 is a data processing flow chart of the odometer calibration method of the Ackerman model mobile robot of the present invention.
Detailed Description
In order to make the above objects, technical solutions and advantages of the present invention more comprehensible, the present invention is described in detail below with reference to the accompanying drawings and preferred embodiments, wherein the specific embodiments are only for facilitating understanding of the present invention and do not limit the scope of the present invention.
The first embodiment is as follows:
a method for calibrating an Ackerman model mobile robot odometer comprises the following operation steps:
(1) acquiring a real corner of the mobile robot in a motion process through an IMU (inertial measurement Unit) arranged on the mobile robot;
(2) calculating the movement speed of the mobile robot by the pulse number of the wheel type encoder in unit time, and integrating the speed to obtain the movement distance of the mobile robot in a certain time interval;
(3) obtaining estimated displacement of the mobile robot by using a track presumption algorithm according to the movement distance of the mobile robot and the mobile robot corner obtained by the IMU;
(4) the method comprises the steps of acquiring the distance and the angle of a single characteristic angular point in a laser radar tracking environment relative to a mobile robot at two sampling moments, and calculating the real displacement of the mobile robot by combining IMU corners and utilizing geometric derivation;
(5) and the estimated displacement of the mobile robot is adjusted by comparing the estimated displacement of the mobile robot with the real displacement of the mobile robot to obtain an error coefficient, so that the odometer calibration is realized.
According to the calibration method of the ackerman model mobile robot odometer, the odometer is calibrated by using the laser radar with high precision in ranging and the LMU with high precision in measuring angles, and the calibration precision and the calibration efficiency of the ackerman model mobile robot odometer calibration are improved.
Example two:
this embodiment is substantially the same as the first embodiment, and is characterized in that:
in this embodiment, in the step (3), the step of acquiring the estimated displacement of the mobile robot is as follows:
(3-1) obtaining the rotating speed omega of the motormWhen the linear velocity of the mobile robot body is vc＝r·ωm(ii) a Wherein r is the radius of the wheel of the mobile robot;
(3-2) integrating the speed of the mobile robot to obtain a movement distance s in a given time interval;
(3-3) taking the middle point of the connecting line of the rear wheels of the mobile robot as a reference point O, and moving the reference point from A (x, y) to B (x ', y') around the point O;
(3-4) obtaining the following data according to a track presumption algorithm:
where s is the distance the trolley is moving in a given time, θ1,θ2Respectively representing attitude angles of the trolley during two times of sampling;
(3-5) calculating the displacement of the mobile robot according to the coordinates of the mobile robot obtained by twice sampling
In this embodiment, in the step (4), the step of acquiring the real displacement of the mobile robot is as follows:
(4-1) at the starting moment, the laser radar observes the angular point and outputs the relative position relation between the angular point and a radar coordinate system, and the distance between the angular point and the radar coordinate system is d1Angle of alpha1；
(4-2) at the end time, the angular point is at a distance d from the lidar2Angle of alpha2；
(4-3) in T time, the rotation angle of the robot is delta alpha and is obtained by the IMU, and the included angle of the two ranging measurements of the laser radar to the angular point is alpha3Then alpha can be calculated3＝α1+α2+Δα；
(4-4) the displacement of the robot is as follows:
In this embodiment, in the step (5), the step of obtaining the odometer error coefficient is as follows:
(5-1) calculating error coefficients from a plurality of sets of data obtained by one calibration
Wherein lodomFor the estimated displacement of arrival from the odometer information,/laserIs the true displacement obtained from the lidar information; through multiple calibration, getlAverage value of (2)
(5-2) repeating the step (5-1), controlling the mobile robot to run according to different tracks, and obtaining a plurality of groups
Get
The average value of the average value is more universal
(5-3) in order to
As error coefficients, added to the odometer information estimation
And controlling the trolley to move, and comparing l with llaserAnd verifying the correctness of the coefficient, wherein l is the estimated displacement after calibration.
The method of the embodiment utilizes the high-precision characteristics of the IMU and the laser radar to complete the calibration of the odometer of the robot, improves the precision of pose estimation of the robot in the moving process, and further improves the precision of the mobile robot in the processes of drawing construction, positioning and navigation.
Example three:
this embodiment is substantially the same as the above embodiment, and is characterized in that:
in the present embodiment, fig. 1 is a schematic diagram of a motion environment of a mobile robot. Wherein it is required that in this environment only one corner point exists within the detectable range of the lidar for tracking by the lidar. Fig. 2 is a dynamic model of an ackerman model mobile robot, and a laser radar and an IMU may be respectively installed at the head and tail portions of the robot along the central axis of the robot. Fig. 3 is a schematic diagram of laser measurement of the displacement of the mobile robot, and the displacement of the mobile robot is estimated from two samplings. Fig. 4 is a schematic diagram of dead reckoning of an ackerman model mobile robot, and the poses of the mobile robot at two sampling moments are estimated by using encoders and IMU information. Fig. 5 is a data processing flow of the ackerman model mobile robot odometer calibration.
The method for calibrating the odometer of the Ackerman model mobile robot comprises the following steps of S101-S108:
step S101, controlling the mobile robot to move in the environment as shown in FIG. 1, wherein the movement form can be a straight line, a circle and a composite of the straight line and the circle;
step S102, obtaining a rotation angle delta theta of the mobile robot within a certain time by using an IMU;
step S103, in conjunction with FIG. 2, using the encoder to obtain the motor speed ω of the mobile robot in unit timemThe linear velocity of the mobile robot motion is vc＝r·ωmWhere r is the radius of the wheel of the mobile robot, pair vcThe movement distance s can be obtained by an odometer by integrating;
step S104, with reference to FIG. 3, a coordinate system xOy and a coordinate system xO' y are respectively expressed as pose transformation of a laser radar coordinate system within the T time difference; point A represents a unique angular point in the environment, the angular point is observed by the laser radar at the starting moment, the relative position relation between the angular point and a radar coordinate system is output, and the distance between the angular point and the radar coordinate system is d1Angle of theta1At the end time, the angular point is at a distance d from the lidar2Angle of theta2The rotation angle of the robot during time T is Δ θ, and is obtained by the IMU. The included angle of the two times of ranging of the laser radar diagonal point is theta3Then calculate theta3＝θ1+θ2+ Δ θ, then the robot displacement is:
wherein d is3The trolley displacement is obtained by laser radar information;
step S105, with reference to fig. 4, using the center point O ' of the rear wheel connecting line as a reference point, and moving the reference point from a (x, y) to B (x ', y ') around the point O, then obtaining, according to a track estimation algorithm:
then after a number of samples the displacement of the trolley relative to the starting point is:
wherein theta is1，θ2The attitude angles of the mobile robot at two points A and B are shown;
step S106, see FIGS. 5 and ScorAnd thetacorRespectively the distance and angle of the corner point relative to the laser radar, SodomEstimated distance of the mobile robot within a certain time, theta, for a wheeled odometerIMUObtaining the displacement l calculated by the laser radar for the mobile robot corner acquired by the IMU according to the datalaserAfter the displacement estimated by the odometer is lodomDefining an error coefficient of
Step S107, obtaining a plurality of groups through multiple times of calibrationlGet itlAverage value of (2)
The true displacement of the carriage
Completing the calibration of the odometer;
and S108, adding the error coefficient into the pose estimation of the mobile robot after obtaining the error coefficient, and verifying the correctness of the error coefficient.
The method for calibrating the odometer of the Ackerman model mobile robot comprises the steps of respectively mounting a wheel type encoder, a laser radar and an Inertial Measurement Unit (IMU) on the mobile robot. The method comprises the steps that the speed of the mobile robot is obtained through a wheel type encoder arranged on a motor, and the movement distance of the robot is obtained through integrating the speed; acquiring a rotation angle of the mobile robot within a certain time through the IMU; acquiring the distances and angles of angular points relative to the mobile robot at different sampling moments by tracking a single angular point in an environment through a laser radar; and calculating the estimated displacement and the real displacement of the mobile robot according to the acquired related data to obtain an error coefficient, and finishing the calibration of the odometer of the mobile robot. The embodiment completes calibration of the odometer of the robot by utilizing the high-precision characteristics of the IMU and the laser radar, improves the accuracy of pose estimation of the robot in the moving process, further improves the accuracy of the mobile robot in mapping, positioning and navigation, and is suitable for the technical field of simultaneous positioning and mapping (SLAM) of the mobile robot.
The embodiments of the present invention have been described with reference to the accompanying drawings, but the present invention is not limited to the embodiments, and various changes and modifications can be made according to the purpose of the invention, and any changes, modifications, substitutions, combinations or simplifications made according to the spirit and principle of the technical solution of the present invention shall be equivalent substitutions, as long as the purpose of the present invention is met, and the present invention shall fall within the protection scope of the present invention without departing from the technical principle and inventive concept of the present invention.
Claims (4)
1. A calibration method for an Ackerman model mobile robot odometer is characterized by comprising the following operation steps:
(1) acquiring a real corner of the mobile robot in a motion process through an IMU (inertial measurement Unit) arranged on the mobile robot;
(2) calculating the movement speed of the mobile robot by the pulse number of the wheel type encoder in unit time, and integrating the speed to obtain the movement distance of the mobile robot in a certain time interval;
(3) obtaining estimated displacement of the mobile robot by using a track presumption algorithm according to the movement distance of the mobile robot and the mobile robot corner obtained by the IMU;
(4) the method comprises the steps of acquiring the distance and the angle of a single characteristic angular point in a laser radar tracking environment relative to a mobile robot at two sampling moments, and calculating the real displacement of the mobile robot by combining IMU corners and utilizing geometric derivation;
(5) and the estimated displacement of the mobile robot is adjusted by comparing the estimated displacement of the mobile robot with the real displacement of the mobile robot to obtain an error coefficient, so that the odometer calibration is realized.
2. The ackermann model mobile robot odometer calibration method according to claim 1, wherein in the step (3), the step of obtaining the estimated displacement of the mobile robot is as follows:
(3-1) obtaining the rotating speed omega of the motormWhen the linear velocity of the mobile robot body is vc＝r·ωm(ii) a Wherein r is the radius of the wheel of the mobile robot;
(3-2) integrating the speed of the mobile robot to obtain a movement distance s in a given time interval;
(3-3) taking the middle point of the connecting line of the rear wheels of the mobile robot as a reference point O, and moving the reference point from A (x, y) to B (x ', y') around the point O;
(3-4) According to the track presumption algorithm, the following results are obtained:
where s is the distance the trolley is moving in a given time, θ1,θ2Respectively representing attitude angles of the trolley during two times of sampling;
(3-5) calculating the displacement of the mobile robot according to the coordinates of the mobile robot obtained by twice sampling
3. The ackermann model mobile robot odometer calibration method according to claim 1, wherein in the step (4), the step of acquiring the real displacement of the mobile robot is as follows:
(4-1) at the starting moment, the laser radar observes the angular point and outputs the relative position relation between the angular point and a radar coordinate system, and the distance between the angular point and the radar coordinate system is d1Angle of alpha1；
(4-2) at the end time, the angular point is at a distance d from the lidar2Angle of alpha2；
(4-3) in T time, the rotation angle of the robot is delta alpha and is obtained by the IMU, and the included angle of the two ranging measurements of the laser radar to the angular point is alpha3Then alpha can be calculated3＝α1+α2+Δα；
(4-4) the displacement of the robot is as follows:
4. The ackermann model mobile robot odometer calibration method according to claim 1, wherein in the step (5), the step of obtaining the odometer error coefficient is as follows:
(5-1) calibration by one timeCalculating error coefficients for the obtained sets of data
Wherein lodomFor the estimated displacement of arrival from the odometer information,/laserIs the true displacement obtained from the lidar information; through multiple calibration, getlAverage value of (2)
(5-2) repeating the step (5-1), controlling the mobile robot to run according to different tracks, and obtaining a plurality of groups
Get
The average value of the average value is more universal
(5-3) in order to
As error coefficients, added to the odometer information estimation
And controlling the trolley to move, and comparing l with llaserAnd verifying the correctness of the coefficient, wherein l is the estimated displacement after calibration.
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| CN112985464A (en) * | 2021-05-10 | 2021-06-18 | 湖北亿咖通科技有限公司 | Precision detection method of vehicle odometer, electronic device and storage medium |
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| CN114187359A (en) * | 2021-12-13 | 2022-03-15 | 哈尔滨工业大学芜湖机器人产业技术研究院 | Laser radar fixed pose calibration method and system based on pose increment constraint |
| CN114440928A (en) * | 2022-01-27 | 2022-05-06 | 杭州申昊科技股份有限公司 | Combined calibration method for laser radar and odometer, robot, equipment and medium |
| WO2022237375A1 (en) * | 2021-05-13 | 2022-11-17 | 灵动科技（北京）有限公司 | Positioning apparatus calibration method, odometer calibration method, program product, and calibration apparatus |
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