Abstract
This paper presents recent work on the subject of measurement variance in Ultra-Wide Band localisation systems. Recent studies have shown the utility in more rigorous noise characterisation of sensor inputs used in state estimation systems such as the Extended Kalman Filter. This investigation strategy is extended to using data collected during trials of such state estimation algorithms using an Unmanned Ground Vehicle, for the generation of variance maps of the testing environments. The feasibility of building variance models from this data is discussed, and other applications for the information is proposed. As there exist circumstances where the practice of moving the agent around a space incrementally is not practicable, such as in the case of Unmanned Aerial Vehicles, or in restricted spaces, an alternate method is needed. From the results it can be concluded that the use of data collected during standard operation in the environment is not sufficient for initial characterisation of localisation sensors. Initial analysis of this data was also utilised to investigate the effects of environmental factors.
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Pointon, H.A.G., Bezombes, F.A. (2019). Mapping of Ultra-Wide Band Positional Variance for Indoor Environments. In: Althoefer, K., Konstantinova, J., Zhang, K. (eds) Towards Autonomous Robotic Systems. TAROS 2019. Lecture Notes in Computer Science(), vol 11650. Springer, Cham. https://doi.org/10.1007/978-3-030-25332-5_11
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