CN112097792B - Method for calibrating odometer of mobile robot by using Ackerman model - Google Patents
Method for calibrating odometer of mobile robot by using Ackerman model Download PDFInfo
- Publication number
- CN112097792B CN112097792B CN202010888953.1A CN202010888953A CN112097792B CN 112097792 B CN112097792 B CN 112097792B CN 202010888953 A CN202010888953 A CN 202010888953A CN 112097792 B CN112097792 B CN 112097792B
- Authority
- CN
- China
- Prior art keywords
- mobile robot
- displacement
- odometer
- robot
- calibration
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- 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
-
- 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
- G01C25/005—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass initial alignment, calibration or starting-up of inertial devices
Landscapes
- Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Navigation (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses a calibration method of an ackerman model mobile robot odometer, which comprises the steps of respectively installing a wheel encoder, a laser radar and an IMU on a mobile robot, acquiring the speed of the mobile robot through the wheel encoder installed on a motor, and acquiring the movement distance of the robot through integrating the speed; acquiring a corner of the mobile robot in a certain time through the IMU; and acquiring the distances and angles of the angular points relative to the mobile robot at different sampling moments through a single angular point in the laser radar tracking environment. Calculating estimated displacement and actual displacement of the mobile robot according to the acquired related data to obtain an error coefficient, and completing the calibration of the odometer of the mobile robot. According to the invention, the high-precision characteristics of the IMU and the laser radar are utilized to finish the calibration of the robot odometer, so that the pose estimation precision of the robot in the moving process is improved, and the precision of the mobile robot in the process of drawing, positioning and navigation is further improved, and the method 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 an ackerman model mobile robot odometer calibration method.
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. In the field of national defense, unmanned aerial vehicles and unmanned aerial vehicles are used for reconnaissance, information collection and tracking; in the logistics field, AGV dolly has become the important component of intelligent logistics system. In the service field, various cleaning robots, welcome robots, catering robots, shopping guide robots, medical robots, and the like are also successively developed, and SLAM technology, one of the supporting technologies, is also continuously developed. In the SLAM technology, pose estimation of the mobile robot is particularly important, but because of assembly errors, gaps between teeth, wheel skidding in the motion process and other reasons of the mobile robot, the mobile robot often has larger errors when performing pose estimation by using odometer information, so that calibration of the odometer is particularly important.
The existing odometer calibration method is mainly aimed at two-wheel differential mobile robots driven by two motors, the structure of the robot is simpler, manual ranging is mostly used when relevant distances are measured, the precision is lower, the running track of the robot can be reserved in the past during calibration, the diversity of the movement forms of the robot is reduced, the robot is often greatly different from the actual movement conditions, different movement forms often generate different errors, and therefore the calibration result is not good in universality and the efficiency is lower. Meanwhile, the ackerman model has a complex 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 robots.
Aiming at the defects of the prior art, the invention provides an ackerman model robot odometer calibration method, which is mainly used for calibrating an odometer by utilizing a laser radar with higher accuracy in distance measurement and an IMU inertial measurement unit with higher accuracy in measurement angle. According to the invention, the laser radar and the IMU are used as references, the displacement of the trolley in a certain time interval is calculated, the displacement of the trolley is estimated by using the odometer information, the two are compared to obtain an error coefficient, and the displacement estimated by the odometer information is further adjusted, so that the purpose of accurately estimating the pose of the trolley is achieved.
Disclosure of Invention
In view of the above problems, the invention aims to provide an ackerman model mobile robot odometer calibration method, which is used for calibrating an odometer by utilizing a laser radar with higher accuracy in distance measurement and an LMU with higher accuracy in measurement angle, so that the calibration accuracy and the calibration efficiency of the ackerman model mobile robot odometer calibration are improved.
In order to achieve the above purpose, the invention adopts the following technical scheme:
(1) Acquiring a real corner of the mobile robot in the motion process through an IMU installed on the mobile robot;
(2) Calculating the motion speed of the mobile robot by the pulse number in unit time of the wheel encoder, and integrating the speed to obtain the motion distance of the mobile robot in a certain time interval;
(3) Obtaining estimated displacement of the mobile robot by utilizing a track estimation algorithm according to the movement distance of the mobile robot and the corner of the mobile robot obtained by the IMU;
(4) Acquiring the distance and angle of the single characteristic angular point in the laser radar tracking environment relative to the mobile robot at the time of twice sampling, and calculating the real displacement of the mobile robot by combining with the IMU angular angle and utilizing geometric deduction;
(5) And obtaining an error coefficient by comparing the estimated displacement of the mobile robot with the actual displacement of the mobile robot, and adjusting the estimated displacement of the mobile robot to realize the calibration of the odometer.
Preferably, in the step (3), the step of acquiring the estimated displacement of the mobile robot is as follows:
(3-1) obtaining the Motor rotation speed ω m The moving linear velocity of the moving robot body is v c ＝r·ω m The method comprises the steps of carrying out a first treatment on the surface of the Wherein r is the radius of the mobile robot wheel;
(3-2) integrating the speed of the mobile robot to obtain a movement distance s within a given time interval;
(3-3) taking the midpoint of the connecting line of the rear wheel of the mobile robot as a datum point O, and moving the datum point from A (x, y) to B (x ', y') around the O point;
(3-4) obtaining according to a track estimation algorithm:
where s is the distance moved by the trolley in a given time, θ 1 ,θ 2 The attitude angles of the trolley during two times of sampling are respectively;
(3-5) calculating the displacement of the mobile robot according to the coordinates of the mobile robot obtained by the two samplings
Preferably, in the step (4), the step of acquiring the true displacement of the mobile robot is as follows:
(4-1) at the start time, the lidar observes the corner and outputs the relative positional relationship of the corner and the radar coordinate system, which is at a distance d from the lidar 1 Angle alpha 1 ；
(4-2) at the end point, the angular point is at a distance d from the lidar 2 Angle alpha 2 ；
(4-3) in the T time, the rotation angle of the robot is delta theta, the rotation angle is obtained by the IMU, and the included angle between the laser radar and the angular point for two ranging is alpha 3 Then alpha can be calculated 3 ＝α 1 +α 2 +Δα；
(4-4) robot displacement is:
Preferably, in the step (5), the step of obtaining the odometer error coefficient is as follows:
(5-1) calculating error coefficients from the plurality of sets of data obtained by one calibration
Wherein l odom For estimating displacement from arrival of odometer information, l laser The true displacement is obtained by laser radar information; taking delta through multiple calibration l Mean value of>
(5-2) repeating the step (5-1), and controlling the mobile robot to travel according to different tracks to obtain a plurality of groups
Taking out
Is more universal, is obtained>
(5-3) to
As error coefficient, add to Mileage information estimate +.>
And control the movement of the trolley, compare l with l laser The correctness of the coefficient is verified, wherein l is the estimated displacement after calibration.
Compared with the prior art, the invention has the following obvious prominent substantive features and obvious technical progress:
1. the reference distance for calibration is obtained by geometrically deducing the laser radar and IMU data, 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 form of the robot is diversified, and the calibration result is more universal;
3. the invention can sample and calculate for many times in the one-time motion process 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 of the present invention.
Fig. 2 is an ackerman model mobile robot dynamics model diagram of the ackerman model mobile robot odometer calibration method of the present invention.
Fig. 3 is a schematic diagram of estimating the displacement of the mobile robot by using a laser radar according to the method for calibrating the odometer of the mobile robot by using the ackermann model.
Fig. 4 is a schematic diagram of mobile robot dead reckoning in the method for calibrating the ackermann model mobile robot odometer according to the present invention.
FIG. 5 is a data processing flow chart of the method for calibrating the odometer of the mobile robot with the ackerman model.
Detailed Description
In order to make the above objects, technical solutions and advantages of the present invention more obvious and understandable, the present invention is described in detail below with reference to the accompanying drawings and preferred embodiments, and the specific embodiments are only for facilitating understanding of the present invention and do not limit the scope of protection of the present invention.
Embodiment one:
the method for calibrating the mobile robot odometer of the Ackerman model comprises the following operation steps:
(1) Acquiring a real corner of the mobile robot in the motion process through an IMU installed on the mobile robot;
(2) Calculating the motion speed of the mobile robot by the pulse number in unit time of the wheel encoder, and integrating the speed to obtain the motion distance of the mobile robot in a certain time interval;
(3) Obtaining estimated displacement of the mobile robot by utilizing a track estimation algorithm according to the movement distance of the mobile robot and the corner of the mobile robot obtained by the IMU;
(4) Acquiring the distance and angle of the single characteristic angular point in the laser radar tracking environment relative to the mobile robot at the time of twice sampling, and calculating the real displacement of the mobile robot by combining with the IMU angular angle and utilizing geometric deduction;
(5) And obtaining an error coefficient by comparing the estimated displacement of the mobile robot with the actual displacement of the mobile robot, and adjusting the estimated displacement of the mobile robot to realize the calibration of the odometer.
According to the calibration method of the mobile robot odometer of the ackerman model, the calibration of the odometer is carried out by utilizing the laser radar with higher accuracy in distance measurement and the LMU with higher accuracy in measurement angle, and the calibration accuracy and the calibration efficiency of the calibration of the mobile robot odometer of the ackerman model are improved.
Embodiment two:
this embodiment is substantially the same as the first embodiment, and is specifically as follows:
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 Motor rotation speed ω m The moving linear velocity of the moving robot body is v c ＝r·ω m The method comprises the steps of carrying out a first treatment on the surface of the Wherein r is the radius of the mobile robot wheel;
(3-2) integrating the speed of the mobile robot to obtain a movement distance s within a given time interval;
(3-3) taking the midpoint of the connecting line of the rear wheel of the mobile robot as a datum point O, and moving the datum point from A (x, y) to B (x ', y') around the O point;
(3-4) obtaining according to a track estimation algorithm:
where s is the distance moved by the trolley in a given time, θ 1 ,θ 2 The attitude angles of the trolley during two times of sampling are respectively;
(3-5) calculating the displacement of the mobile robot according to the coordinates of the mobile robot obtained by the two samplings
In this embodiment, in the step (4), the step of acquiring the true displacement of the mobile robot is as follows:
(4-1) at the start time, the lidar observes the corner and outputs the relative positional relationship of the corner and the radar coordinate system, which is at a distance d from the lidar 1 Angle alpha 1 ；
(4-2) at the end point, the angular point is at a distance d from the lidar 2 Angle alpha 2 ；
(4-3) in the T time, the rotation angle of the robot is delta alpha, the rotation angle is obtained by the IMU, and the included angle between the laser radar and the angular point for two ranging is alpha 3 Then alpha can be calculated 3 ＝α 1 +α 2 +Δα；
(4-4) robot displacement is:
In this embodiment, in the step (5), the step of obtaining the odometer error coefficient is as follows:
(5-1) calculating error coefficients from the plurality of sets of data obtained by one calibration
Wherein l odom For estimating displacement from arrival of odometer information, l laser The true displacement is obtained by laser radar information; taking delta through multiple calibration l Mean value of>
(5-2) repeating the step (5-1), and controlling the mobile robot to travel according to different tracks to obtain a plurality of groups
Taking out
Is more universal, is obtained>
(5-3) to
As error coefficient, add to Mileage information estimate +.>
And control the movement of the trolley, compare l with l laser The correctness of the coefficient is verified, wherein l is the estimated displacement after calibration.
According to the method, the high-precision characteristics of the IMU and the laser radar are utilized to finish calibration of the robot odometer, so that the precision of pose estimation of the robot in the moving process is improved, and further the precision of the mobile robot in the process of image construction, positioning and navigation is improved.
Embodiment III:
this embodiment is substantially the same as the above embodiment, with the following specific points:
in this embodiment, fig. 1 is a schematic view of a motion environment of a mobile robot. Wherein it is required that in this environment only one corner exists within the detectable range of the lidar for the lidar to track. Fig. 2 is a dynamic model of an ackermann model mobile robot, where a lidar and an IMU may be mounted at the head-tail portion of the robot along the central axis of the robot, respectively. Fig. 3 is a schematic diagram of laser measurement of mobile robot displacement, which is estimated from two samples. Fig. 4 is a schematic diagram of the reckoning of the ackerman model mobile robot, and the pose of the mobile robot at the time of two sampling is estimated by using the encoder and IMU information. Fig. 5 is a data processing flow of the ackermann model mobile robot odometer calibration.
The method for calibrating the ackerman model mobile robot odometer comprises the following steps of S101-S108:
step S101, controlling the mobile robot to move in the environment shown in FIG. 1, wherein the movement form can be straight line, circumference and the combination of the straight line and the circumference;
step S102, acquiring a rotation angle delta theta of the mobile robot within a certain time by using an IMU;
step S103, with reference to FIG. 2, the encoder is used to obtain the rotation speed ω of the mobile robot motor in unit time m The linear velocity of the motion of the mobile robot is v c ＝r·ω m Wherein r is the radius of the mobile robot wheel, vs c Integrating to obtain a movement distance s by the odometer;
step S104, referring to FIG. 3, the coordinate system xOy and the coordinate system xO' y are respectively represented as pose transformation of the laser radar coordinate system within the T time difference; the point A represents the unique angular point in the environment, and at the initial moment, the laser radar observes the angular point and outputs the relative position relation between the angular point and the radar coordinate system, and the distance between the angular point and the laser radar is d 1 Angle of theta 1 At the end point, the distance between the corner point and the laser radar is d 2 Angle of theta 2 And in the time T, the rotation angle of the robot is delta theta, and the rotation angle is obtained by the IMU. The included angle of the laser radar to the angular point for twice ranging is theta 3 Then calculate θ 3 ＝θ 1 +θ 2 +Δθ, then the robot displacement is:
wherein d is 3 Namely, a trolley is used for parking the vehicle, which is obtained by laser radar information;
in step S105, referring to fig. 4, with the midpoint O ' of the rear wheel link as the reference point, the reference point moves from a (x, y) to B (x ', y ') around the O point, and then the track estimation algorithm is used to obtain:
then after a number of samplings the displacement of the trolley with respect to the starting point is: />
Wherein θ is 1 ，θ 2 The attitude angle of the mobile robot at the points A and B is the attitude angle of the mobile robot at the points A and B;
step S106, combine with the graph 5,S cor And theta cor The distance and the angle of the angular point relative to the laser radar are respectively S odom Distance, θ, of mobile robot estimated for wheel odometer within a certain time IMU Obtaining the displacement l calculated by the laser radar according to the data for the corner of the mobile robot acquired by the IMU laser After estimating the resulting displacement with the odometer odom Defining the error coefficient as
Step S107, obtaining a plurality of groups of delta through multiple calibration l Taking delta l Average value of (2)
The real displacement of the trolley
Finishing 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 ackerman model mobile robot odometer comprises the steps of respectively installing a wheel type encoder, a laser radar and an inertial measurement unit IMU on a mobile robot. Acquiring the speed of the mobile robot through a wheel type encoder arranged on a motor, and acquiring the movement distance of the robot through integrating the speed; acquiring a corner of the mobile robot in a certain time through the IMU; acquiring the distance and the angle of the corner relative to the mobile robot at different sampling moments through a single corner in a laser radar tracking environment; calculating estimated displacement and actual displacement of the mobile robot according to the acquired related data to obtain an error coefficient, and completing the calibration of the odometer of the mobile robot. According to the embodiment, the high-precision characteristics of the IMU and the laser radar are utilized to finish the calibration of the robot odometer, the precision of pose estimation of the robot in the moving process is improved, and then the precision of the mobile robot in the process of drawing, positioning and navigation is improved, so that the method is suitable for being applied to the technical field of simultaneous positioning and map construction (SLAM) of the mobile robot.
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the embodiments described above, and various changes, modifications, substitutions, combinations or simplifications made under the spirit and principles of the technical solution of the present invention can be made according to the purpose of the present invention, and all the changes, modifications, substitutions, combinations or simplifications should be equivalent to the substitution, so long as the purpose of the present invention is met, and all the changes are within the scope of the present invention without departing from the technical principles and the inventive concept of the present invention.
Claims (3)
1. The method for calibrating the mobile robot odometer of the ackerman model is characterized by comprising the following operation steps of:
(1) Acquiring a real corner of the mobile robot in the motion process through an IMU installed on the mobile robot;
(2) Calculating the motion speed of the mobile robot by the pulse number in unit time of the wheel encoder, and integrating the speed to obtain the motion distance of the mobile robot in a certain time interval;
(3) Obtaining estimated displacement of the mobile robot by utilizing a track estimation algorithm according to the movement distance of the mobile robot and the corner of the mobile robot obtained by the IMU;
(4) Acquiring the distance and angle of the single characteristic angular point in the laser radar tracking environment relative to the mobile robot at the time of twice sampling, and calculating the real displacement of the mobile robot by combining with the IMU angular angle and utilizing geometric deduction;
(5) Obtaining an error coefficient by comparing the estimated displacement of the mobile robot with the actual displacement of the mobile robot, and adjusting the estimated displacement of the mobile robot to realize the calibration of the odometer;
in the step (5), the step of obtaining the error coefficient of the odometer is as follows:
(5-1) calculating error coefficients from the plurality of sets of data obtained by one calibration
Wherein l odom For estimating displacement from arrival of odometer information, l laser The true displacement is obtained by laser radar information; taking delta through multiple calibration l Mean value of>
(5-2) repeating the step (5-1), and controlling the mobile robot to travel according to different tracks to obtain a plurality of groups
Get->
Is more universal, is obtained>
(5-3) to
As error coefficient, add to Mileage information estimate +.>
And control the movement of the trolley, compare l with l laser The correctness of the coefficient is verified, wherein l is the estimated displacement after calibration.
2. The ackerman model mobile robot odometer calibration method according to claim 1, characterized in that in step (3), the step of obtaining an estimated displacement of the mobile robot is as follows:
(3-1) obtaining the Motor rotation speed ω m The moving linear velocity of the moving robot body is v c ＝r·ω m The method comprises the steps of carrying out a first treatment on the surface of the Wherein r is the radius of the mobile robot wheel;
(3-2) integrating the speed of the mobile robot to obtain a movement distance s within a given time interval;
(3-3) taking the midpoint of the connecting line of the rear wheel of the mobile robot as a datum point O, and moving the datum point from A (x, y) to B (x ', y') around the O point;
(3-4) obtaining according to a track estimation algorithm:
where s is the distance moved by the trolley in a given time, θ 1 ,θ 2 The attitude angles of the trolley during two times of sampling are respectively;
(3-5) calculating the displacement of the mobile robot according to the coordinates of the mobile robot obtained by the two samplings
3. The method for calibrating an ackerman model mobile robot odometer according to claim 1, wherein in the step (4), the step of obtaining the actual displacement of the mobile robot is as follows:
(4-1) at the start time, the lidar observes the corner and outputs the relative positional relationship of the corner and the radar coordinate system, which is at a distance d from the lidar 1 Angle a 1 ；
(4-2) at the end point, the angular point is at a distance d from the lidar 2 Angle a 2 ；
(4-3) in the T time, the rotation angle of the robot is delta a, the angle between the laser radar and the angular point is a, and the angle between the laser radar and the angular point is measured twice by the IMU 3 Then a can be calculated 3 ＝a 1 +a 2 +Δa；
(4-4) robot displacement is:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010888953.1A CN112097792B (en) | 2020-08-28 | 2020-08-28 | Method for calibrating odometer of mobile robot by using Ackerman model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010888953.1A CN112097792B (en) | 2020-08-28 | 2020-08-28 | Method for calibrating odometer of mobile robot by using Ackerman model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112097792A CN112097792A (en) | 2020-12-18 |
| CN112097792B true CN112097792B (en) | 2023-05-16 |
Family
ID=73758389
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010888953.1A Active CN112097792B (en) | 2020-08-28 | 2020-08-28 | Method for calibrating odometer of mobile robot by using Ackerman model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112097792B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN115342830A (en) * | 2021-05-13 | 2022-11-15 | 灵动科技（北京）有限公司 | Calibration method, program product and calibration device for a positioning device and a odometer |
| CN113703448A (en) * | 2021-08-19 | 2021-11-26 | 深圳亿嘉和科技研发有限公司 | Ackerman chassis obstacle avoidance control method based on ultrasonic waves |
| 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 |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0763713A2 (en) * | 1995-09-14 | 1997-03-19 | Zexel Corporation | Method and apparatus for calibration of a distance sensor in a vehicle navigation system |
| CN103941264A (en) * | 2014-03-26 | 2014-07-23 | 南京航空航天大学 | Positioning method using laser radar in indoor unknown environment |
| CN105411490A (en) * | 2015-10-26 | 2016-03-23 | 曾彦平 | Real-time positioning method of mobile robot and mobile robot |
| CN107357297A (en) * | 2017-08-21 | 2017-11-17 | 深圳市镭神智能系统有限公司 | A kind of sweeping robot navigation system and its air navigation aid |
| CN108844553A (en) * | 2018-06-27 | 2018-11-20 | 广州视源电子科技股份有限公司 | Correct the method, apparatus and robot of the mileage in robot moving process |
| CN109916431A (en) * | 2019-04-12 | 2019-06-21 | 成都天富若博特科技有限责任公司 | A kind of wheel encoder calibration algorithm for four wheel mobile robots |
-
2020
- 2020-08-28 CN CN202010888953.1A patent/CN112097792B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0763713A2 (en) * | 1995-09-14 | 1997-03-19 | Zexel Corporation | Method and apparatus for calibration of a distance sensor in a vehicle navigation system |
| CN103941264A (en) * | 2014-03-26 | 2014-07-23 | 南京航空航天大学 | Positioning method using laser radar in indoor unknown environment |
| CN105411490A (en) * | 2015-10-26 | 2016-03-23 | 曾彦平 | Real-time positioning method of mobile robot and mobile robot |
| CN107357297A (en) * | 2017-08-21 | 2017-11-17 | 深圳市镭神智能系统有限公司 | A kind of sweeping robot navigation system and its air navigation aid |
| CN108844553A (en) * | 2018-06-27 | 2018-11-20 | 广州视源电子科技股份有限公司 | Correct the method, apparatus and robot of the mileage in robot moving process |
| CN109916431A (en) * | 2019-04-12 | 2019-06-21 | 成都天富若博特科技有限责任公司 | A kind of wheel encoder calibration algorithm for four wheel mobile robots |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112097792A (en) | 2020-12-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112097792B (en) | Method for calibrating odometer of mobile robot by using Ackerman model | |
| CN109916431B (en) | Wheel encoder calibration algorithm for four-wheel mobile robot | |
| CN108592906B (en) | AGV composite navigation method based on two-dimensional code and inertial sensor | |
| CN110702091B (en) | High-precision positioning method for moving robot along subway rail | |
| CN111044073B (en) | High-precision AGV position sensing method based on binocular laser | |
| CN110077392B (en) | Dead reckoning method of automatic parking positioning system | |
| CN112129297A (en) | Self-adaptive correction indoor positioning method for multi-sensor information fusion | |
| CN105278533A (en) | Omnidirectional moving platform navigation method | |
| CN111930126B (en) | Navigation deviation rectifying method based on differential wheel set AGV | |
| CN114396957B (en) | Positioning pose calibration method based on vision and map lane line matching and automobile | |
| CN111158379B (en) | Steering wheel zero-bias self-learning unmanned vehicle track tracking method | |
| CN112923931A (en) | Feature map matching and GPS positioning information fusion method based on fixed route | |
| CN110514220A (en) | A kind of vehicle mileage calibration method, device and storage medium | |
| CN110763224A (en) | Navigation method and navigation system for automatic guided transport vehicle | |
| Liu et al. | Method for adaptive robust four-wheel localization and application in automatic parking systems | |
| CN115577320A (en) | Multi-sensor asynchronous data fusion method based on data interpolation | |
| Śmieszek et al. | Application of Kalman filter in navigation process of automated guided vehicles | |
| CN114442054A (en) | Sensor and chassis combined calibration system and method for mobile robot | |
| CN112710301B (en) | High-precision positioning method and system for automatic driving vehicle | |
| CN207540557U (en) | A kind of device pinpoint in short-term for AGV trolleies | |
| CN114475581B (en) | Automatic parking positioning method based on wheel speed pulse and IMU Kalman filtering fusion | |
| Krantz et al. | Non-uniform dead-reckoning position estimate updates | |
| CN114291117B (en) | Four-wheel steering unmanned vehicle track tracking control method | |
| CN113219973B (en) | Local path control method of mobile robot | |
| CN112666574B (en) | Method for improving AGV positioning accuracy by using laser radar based on uniform particles |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |