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TelOpTrak: Heuristics-enhanced Indoor Location Tracking for Tele-operated Robots

Published online by Cambridge University Press:  12 March 2012

Johann Borenstein ,
Russ Miller  and
Adam Borrell
Johann Borenstein*
Affiliation:
(Department of Mechanical Engineering, University of Michigan, MI, USA)
Russ Miller
Affiliation:
(Department of Natural Resources and Environment, University of Michigan, MI, USA)
Adam Borrell
Affiliation:
(Boston Dynamics, Waltham, MA, USA)
*
(E-mail: [email protected])
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Abstract

With most tele-operated robots the operator's only feedback is the view from an onboard camera. Live video lets the operator observe the robot's immediate surroundings but does not establish the orientation or whereabouts of the robot in its environment. An additional plot of the robot's trajectory would be helpful for the operator and is sometimes provided, based on GPS. However, indoors where GPS is unavailable, tracking has to rely on dead-reckoning, which is too inaccurate to be useful. Our proposed TelOpTrak method corrects dead-reckoning errors even when only odometry and a low-cost (and thus, high-drift) MEMS-class gyro are available on the robot. TelOpTrak corrects gyro drift by exploiting the structured nature of most buildings, but without having to directly sense building features. This paper explains the TelOpTrak method and provides comprehensive experimental results.

Earlier versions of this paper (Borenstein et al., 2010a), (Borenstein et al., 2010b) were presented at two conferences. The main difference between the earlier conference papers and the present manuscript is that the latter is more comprehensive, more up-to-date, and it presents an entirely new set of experimental results, including results of a live demonstration at the 2010 Robotics Rodeo event at Ft. Benning, USA.

Keywords

Tele-operatedLocationHeuristic
Type
Research Article
Information
The Journal of Navigation , Volume 65 , Issue 2 , April 2012 , pp. 265 - 279
Copyright
Copyright © The Royal Institute of Navigation 2012

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References

REFERENCES

Borenstein, J. and Feng, L. (1996). Measurement and Correction of Systematic Odometry Errors in Mobile Robots. IEEE Transactions on Robotics and Automation, 12, 869–880.CrossRefGoogle Scholar
Borenstein, J. and Ojeda, L. (2009). Heuristic Reduction of Gyro Drift in Vehicle Tracking Applications. Proceedings of the International Journal of Vehicle Information and Communication Systems, 2, 78–98.CrossRefGoogle Scholar
Borenstein, J. and Ojeda, L. (2010). Heuristic Drift Elimination for Personnel Tracking Systems. The Journal of Navigation, 63, 591–606.CrossRefGoogle Scholar
Borenstein, J., Miller, R., Borrell, A. and Thomas, D. (2010a). Heuristics-Enhanced Dead-Reckoning (HEDR) for Accurate Position Tracking of Tele-operated UGVs. Proceedings of the SPIE Defence, Security + Sensing; Unmanned Systems Technology XII, Orlando, Florida.CrossRefGoogle Scholar
Borenstein, J., Miller, R., Borrell, A. and Thomas, D. (2010b). Heuristics-Enhanced Dead-reckoning for Improved Situation Awareness with Tele-operated Robots. Proceedings of the 2010 Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), Detroit, MI.CrossRefGoogle Scholar
Chung, H., Ojeda, L. and Borenstein, J. (2001). Accurate Mobile Robot Dead-reckoning With a Precision-calibrated Fibre Optic Gyroscope. IEEE Transactions on Robotics and Automation, 17, 80–84.CrossRefGoogle Scholar
De Agostino, M., Manzino, A. M. and Piras, M. (2010). Performances comparison of different MEMS-based IMUs. Proceedings of the Position Location and Navigation Symposium (PLANS), 2010 IEEE/ION, Indian Wells/Palm Springs, California USA.CrossRefGoogle Scholar
Johnson, A. E., Goldberg, S. B., Cheng, Y. and Matthies, L. H. (2008). Robust and Efficient Stereo Feature Tracking for Visual Odometry. Proceedings of the International Conference on Robotics and Automation (ICRA), Pasadena, CA.CrossRefGoogle Scholar
Levinson, J. and Thrun, S. (2010). Robust vehicle localization in urban environments using probabilistic maps. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Anchorage, Alaska.CrossRefGoogle Scholar
Microfinity, Spec Sheet, http://www.cruizcore.com.Google Scholar
Nistér, D., Naroditsky, O. and Bergen, J. (2006). Visual odometry for ground vehicle applications. Journal of Field Robotics, 23, 3–20.CrossRefGoogle Scholar
Tardós, J., Neira, J., Newman, P. and Leonard, J. (2002). Robust Mapping and Localization in Indoor Environments using Sonar Data. International Journal of Robotics Research, 21, 311–330.CrossRefGoogle Scholar