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Sensors 2018, 18(7), 2274; https://doi.org/10.3390/s18072274

Uncertainty Characterisation of Mobile Robot Localisation Techniques using Optical Surveying Grade Instruments

Engineering and Technology Research Institute, Liverpool John Moores University, 3 Byrom St, Liverpool L3 3AF, UK
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Received: 15 June 2018 / Revised: 7 July 2018 / Accepted: 11 July 2018 / Published: 13 July 2018
(This article belongs to the Special Issue GNSS and Fusion with Other Sensors)
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Abstract

Recent developments in localisation systems for autonomous robotic technology have been a driving factor in the deployment of robots in a wide variety of environments. Estimating sensor measurement noise is an essential factor when producing uncertainty models for state-of-the-art robotic positioning systems. In this paper, a surveying grade optical instrument in the form of a Trimble S7 Robotic Total Station is utilised to dynamically characterise the error of positioning sensors of a ground based unmanned robot. The error characteristics are used as inputs into the construction of a Localisation Extended Kalman Filter which fuses Pozyx Ultra-wideband range measurements with odometry to obtain an optimal position estimation, all whilst using the path generated from the remote tracking feature of the Robotic Total Station as a ground truth metric. Experiments show that the proposed method yields an improved positional estimation compared to the Pozyx systems’ native firmware algorithm as well as producing a smoother trajectory. View Full-Text
Keywords: robotic total station; localisation; ultra wide-band; extended Kalman filter; RTS robotic total station; localisation; ultra wide-band; extended Kalman filter; RTS
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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McLoughlin, B.J.; Pointon, H.A.G.; McLoughlin, J.P.; Shaw, A.; Bezombes, F.A. Uncertainty Characterisation of Mobile Robot Localisation Techniques using Optical Surveying Grade Instruments. Sensors 2018, 18, 2274.

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