Gyrodometry: a new method for combining data from gyros and odometry in mobile robots

Borenstein, J.; Feng, L.

doi:10.1109/robot.1996.503813

Cited by 184 publications

(82 citation statements)

References 7 publications

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“…비시스템적 오차와 관련해서는 Borenstein [14] 과 Komoriya [15] 와 같이 오도메트리와 자이로 센서를 함께 사용함으로써 비시스템적 오차에 대응하고 위치인식의 불확실성을 감소 시키는 방법이 제안되기도 하였다. Chong [16] 은 로봇의 주행 이전의 연구에서는 이륜차동구동형로봇에 대해 오차원 인간의 복합효과를 고려한 정밀한 오도메트리 보정기법 [17] 을 제안하고 적절한 주행시험경로 설계를 통한 보정성능 향상 [18] 을 보였다.…”

Section: 오도메트리의 오차 원인은 크게 시스템적 오차와 비시unclassified

Design of Experimental Test Tracks for Odometry Calibration of Wheeled Mobile Robots

Jung¹,

Moon²,

Jung³

et al. 2014

The Journal of Korea Robotics Society

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Odometry using wheel encoder is a common relative positioning technique for wheeled mobile robots. The major drawback of odometry is that the kinematic modeling errors are accumulated when the travel distance increases. Therefore, accurate calibration of odometry is required. In several related works, various schemes for odometry calibration are proposed. However, design guidelines of test tracks for odometry calibration were not considered. More accurate odometry calibration results can be achieved by using appropriate test track because the position and orientation errors after the test are affected by the test track. In this paper, we propose the design guidelines of test tracks for odometry calibration schemes using experimental heading errors. Numerical simulations and experiments clearly demonstrate that the proposed design guidelines result in more accurate calibration results.

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Section: 오도메트리의 오차 원인은 크게 시스템적 오차와 비시unclassified

Design of Experimental Test Tracks for Odometry Calibration of Wheeled Mobile Robots

Jung¹,

Moon²,

Jung³

et al. 2014

The Journal of Korea Robotics Society

View full text Add to dashboard Cite

show abstract

“…However, the sensor systems accumulate the errors that induced by the mismatches between real robot and models, slippage of wheels, and variance of wheel diameter due to the air pressure during the navigation. The localization systems with inertial navigation system (INS) utilize the linear accelerations and angular velocities of the mobile robot (Borenstein and Feng, 1996). The systems integrate these informations to estimate the current position and the heading angle.…”

Section: Sensor Systems For Indoor Mobile Robotsmentioning

confidence: 99%

Improving Position Estimation of the RFID Tag Floor Localization with Multiple Recognition Ranges

Park¹,

Lee²,

Kim³

et al. 2011

Advanced Radio Frequency Identification Design and Applications

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This chapter introduces the RFID tag floor localization method with multiple recognition ranges and its mathematical formulation to improve position estimation accuracy. Using the multiple recognition ranges of RFID reader, the reader can obtain more information about the distances to the tags on the tag floor. The information is used to improve the position estimation performance. At first, this chapter reviews the RFID tag floor localization method with single recognition range for mobile robots (Park et al., 2010) and The performance measure based on the position estimation error variance for the localization method. For the second, this paper extends the mathematical formulation of the localization method and the performance measure for the case of multiple recognition ranges. This work is related to the previous work (Park et al., 2009) that used multiple powers to improve position estimation performance. However, previous work lacks analysis and mathematical formulation of general RFID tag recognition models. We extend the mathematical formulation and the analysis of the single recognition range RFID tag floor localization method (Park et al., 2010) to the multiple recognition range case. Then the minimum error variance of multiple recognition range is introduced as a lower bound of position estimation error variance. Finally, it presents performance improvement of proposed localization method via the Monte-Carlo simulation and simple experiments. The analysis for the simulation and experimental results and the consideration for real application will be given. This chapter is organized as follows; This section discusses sensor systems used in the mobile robot localization. Then the advantages of the RFID systems as sensor systems for localization are discussed and the researches on the systems are reviewed. Section 2 introduces the RFID tag floor localization, its mathematical formulation and its performance index. Section 3 represents the motivation of introducing the use of multiple recognition ranges for the RFID tag floor localization method, and extend the mathematical formulation and the error variance for the multiple recognition range case. Section 4 conducts the Monte-Carlo simulation to show the improvement of the position estimation performance when the multiple recognition range is used. Section 5 represents experimental results that support the simulation results. In Section 6, the minimum error variance (Park et al., 2010) as a lower bound of error variance is extended to the multiple recognition range case. Section 7 gives the conclusions, discussions and tasks for the further researches.

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“…In most mobile robots, odometer is implemented by reusing the active wheel of the robot base [6][7][8][9][10]. This kind of positioning usually causes coupling of different errors, for example, the well-known calibration method for differential drive mobile robot UMBmark uses clockwise and counter clockwise square path to decoupling the two errors caused by unequal wheel diameters and uncertainty of the wheelbase [6][7][8][9][10]. Maddahi developed an improved UMBmark method by classifying the error by lateral and longitudinal position error, which is applicable to both differential drive and omnidirectional wheeled robots [11,12].…”

Section: Introductionmentioning

confidence: 99%

A Novel Positioning Method Based on Swedish Wheel and Gyroscope for Mobile Robot

Chen¹,

Wang²,

Su³

et al. 2015

Proceedings of the 2015 International Conference on Electrical, Computer Engineering and Electronics

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Abstract. In this paper a general positioning subsystem that used for several kinds of mobile robot is presented. The subsystem consist of a Optical Fiber Gyroscope(OFG) with high precision and a Orthogonal Passive Wheel System(OPWS) which is combination of Swedish Wheels and Optical Encoders. The OPWS is an independent planar odometer which is regarded as the local reference of the kinematic model, while the OFG provides the instant angle between global and local reference frame of the robot. A dead recking model is developed regardless of the specific robot base structure. The error sources of the proposed positioning method is analyzed in allusion to an omnidirectional robot configuration. Calibration method based on geometric trajectory tracking is introduced according to the error sources, followed by the experimental results that confirm both feasibility of the positioning method and effectiveness of the calibration method.

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Gyrodometry: a new method for combining data from gyros and odometry in mobile robots

Cited by 184 publications

References 7 publications

Design of Experimental Test Tracks for Odometry Calibration of Wheeled Mobile Robots

Design of Experimental Test Tracks for Odometry Calibration of Wheeled Mobile Robots

Improving Position Estimation of the RFID Tag Floor Localization with Multiple Recognition Ranges

A Novel Positioning Method Based on Swedish Wheel and Gyroscope for Mobile Robot

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