Mobile Robot Position Determination Using Data Integration of Odometry and Gyroscope

Houshangi, N.; Azizi, F.

doi:10.1109/wac.2006.375994

Cited by 18 publications

(8 citation statements)

References 21 publications

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“…The obtained results (fig. 4) are similar to those documented in [5,6]. The attitude error from gyroscope signal is smaller than the odometry error.…”

Section: Measurementssupporting

confidence: 86%

Indoor mobile robot attitude estimation with MEMS gyroscope

Cechowicz

2017

ITM Web Conf.

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Abstract.A method of attitude estimation with a low-cost, strap-on MEMS sensor was proposed in the article. The method relies on dynamic gyroscope bias change estimation and updates during detected stops of the robot. The algorithm has been tested in laboratory with iRobot Roomba robot and should be improved to be useful in an industrial environment. The measurement of attitude of a mobile platform is necessary for correct navigation, especially in autonomous vehicles, which use dead reckoning between position updates from external sources. Since the system is intended to operate indoors, in industrial halls and shops, it cannot avail of GPS and Earth magnetic field sensors because of anomalies, which are common inside the steel constructions. Therefore, the accuracy of the gyroscope-based attitude estimation is significant. The proposed method aims to address the common problem of gyroscope bias drift, by dynamic update of sensor bias and simultaneous use of all gyroscope axes, to improve the quality of the measurements. A popular 3-axial gyroscope and 3-axial accelerometer sensors were used during the test runs. Obtained results suggest that it is possible to improve short-term accuracy of inertial dead reckoning, to get a system that could be of practical use in industrial AGV systems or intelligent vehicles.

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“…The obtained results (fig. 4) are similar to those documented in [5,6]. The attitude error from gyroscope signal is smaller than the odometry error.…”

Section: Measurementssupporting

confidence: 86%

Indoor mobile robot attitude estimation with MEMS gyroscope

Cechowicz

2017

ITM Web Conf.

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“…Features are detected by SURF descriptor while the tracking phase is processed in vehicle coordinates. A dead reckoning method is utilized to calculate the global trajectory at each frame [24].…”

Section: Resultsmentioning

confidence: 99%

Visual odometry based on a Bernoulli filter

Zhang

Clarke

Knoll

2015

Int. J. Control Autom. Syst.

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In this paper, we propose a Bernoulli filter for estimating a vehicle's trajectory under random finite set (RFS) framework. In contrast to other approaches, ego-motion vector is considered as the state of an extended target while the features are considered as multiple measurements that originated from the target. The Bernoulli filter estimates the state of the extended target instead of tracking individual features, which presents a recursive filtering framework in the presence of high association uncertainty. Experimental results illustrate that the proposed approach exhibits good robustness under real traffic scenarios.

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“…There are few versions for the UKF which they differ from each other based on whether to include the random noise into the calculations. Houshangi and Azizi have used the UKF to successfully integrate information from odometry and a single axis gyroscope (Houshangi, 2005(Houshangi, , 2006Azizi, 2004). The mobile robot used in their work is a Pioneer 2-Dxe from ActivMedia Robotics Corporation.…”

Section: Wwwintechopencommentioning

confidence: 99%