Measurement and correction of systematic odometry errors in mobile robots

Borenstein, J.; Feng, L.

doi:10.1109/70.544770

Cited by 663 publications

(439 citation statements)

References 21 publications

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“…2) was chosen, instead of standard square path (proposed by [8]), to simulate various working conditions of the warehouse platform, such as various turn radii and forward and backward movements. The real orientation angle of the robot was measured optically, in defined stop points along the path, from the picture taken by a camera above the robot.…”

Section: Fig 1 Modified Irobot Roomba Used In the Experimentmentioning

confidence: 99%

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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Section: Fig 1 Modified Irobot Roomba Used In the Experimentmentioning

confidence: 99%

Indoor mobile robot attitude estimation with MEMS gyroscope

Cechowicz

2017

ITM Web Conf.

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“…The surface, wheels and rollers were cleaned, and the odometry was calibrated before the experiments. The calibration was performed as described in the odometry calibration example from [7], which consists of a simplified version of the Borenstein method [2].…”

Section: Odometrymentioning

confidence: 99%

The Khepera IV Mobile Robot: Performance Evaluation, Sensory Data and Software Toolbox

Soares

Navarro

Martinoli

2015

Advances in Intelligent Systems and Computing

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Abstract. Taking distributed robotic system research from simulation to the real world often requires the use of small robots that can be deployed and managed in large numbers. This has led to the development of a multitude of these devices, deployed in the thousands by researchers worldwide. This paper looks at the Khepera IV mobile robot, the latest iteration of the Khepera series. This full-featured differential wheeled robot provides a broad set of sensors in a small, extensible body, making it easy to test new algorithms in compact indoor arenas. We describe the robot and conduct an independent performance evaluation, providing results for all sensors. We also introduce the Khepera IV Toolbox, an open source framework meant to ease application development. In doing so, we hope to help potential users assess the suitability of the Khepera IV for their envisioned applications and reduce the overhead in getting started using the robot.

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“…All these defects lead to an unbounded accumulation of the localization error. A proposed solution and study of this type of error can be found in (Borenstein and Feng, 1996;1995;Martinelli, 2001).…”

Section: Introductionmentioning

confidence: 99%

Mobile Robot: SLAM Implementation for Unknown Indoor Environment Exploration

Emharraf¹,

Bourhaleb²,

Saber³

et al. 2016

Journal of Computer Science

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Abstract:The autonomy is the most crucial criteria in mobile robots. This operation aims to offer the ability of finding the position and build a map of the environment to the robot. Many methods have been proposed to solve this problem. In this study, an implementation of SLAM approach for unknown indoor environment exploring by mobile robot is proposed. In fact, the proposed approach touch on the unknown indoor environments exploring with static obstacles, based on robot mobile abilities (extereoceptive and proprioceptive sensors). In one hand, the measurements given by the proprioceptive sensor (odometry) are used for the auto localization system. In the other hand, the map building based on extereoceptive sensor scanning and robot position. Therefore, the approach maintains two maps: (1) (OM) map grid describe the occupancy of environment; (2) (TM) map grid memorizes the robot former positions. Furthermore, the use of the proposed maps afford an efficient description and exploitation of the environment resources over time. Finally, the results in simulation and real robots experiments using random exploration (for test), demonstrate the fusibility of the proposed approach.

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Measurement and correction of systematic odometry errors in mobile robots

Cited by 663 publications

References 21 publications

Indoor mobile robot attitude estimation with MEMS gyroscope

Indoor mobile robot attitude estimation with MEMS gyroscope

The Khepera IV Mobile Robot: Performance Evaluation, Sensory Data and Software Toolbox

Mobile Robot: SLAM Implementation for Unknown Indoor Environment Exploration

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