Advertisement

Mapping of Ultra-Wide Band Positional Variance for Indoor Environments

  • Harry A. G. PointonEmail author
  • Frederic A. Bezombes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11650)

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.

Keywords

Ultra-Wide Band State estimation Sensor characterisation 

References

  1. 1.
    Alarifi, A., et al.: Ultra wideband indoor positioning technologies: analysis and recent advances. Sens. (Switz.) 16(5), 707 (2016).  https://doi.org/10.3390/s16050707. http://www.mdpi.com/1424-8220/16/5/707CrossRefGoogle Scholar
  2. 2.
    Gageik, N., Benz, P., Montenegro, S.: Obstacle detection and collision avoidance for a UAV With complementary low-cost sensors. IEEE Access 3, 599–609 (2015)CrossRefGoogle Scholar
  3. 3.
    Guo, K., et al.: Ultra-wideband-based localization for quadcopter navigation. Unmanned Syst. 04(01), 23–34 (2016).  https://doi.org/10.1142/S2301385016400033. http://www.worldscientific.com/doi/abs/10.1142/S2301385016400033CrossRefGoogle Scholar
  4. 4.
    Juliá, M., Gil, A., Reinoso, O.: A comparison of path planning strategies for autonomous exploration and mapping of unknown environments. 427–444 (2012).  https://doi.org/10.1007/s10514-012-9298-8CrossRefGoogle Scholar
  5. 5.
    Kapoor, R., Ramasamy, S., Gardi, A., Sabatini, R.: UAV navigation using signals of opportunity in urban environments: a review. Energy Procedia 110, 377–383 (2017).  https://doi.org/10.1016/J.EGYPRO.2017.03.156. https://www.sciencedirect.com/science/article/pii/S1876610217301868CrossRefGoogle Scholar
  6. 6.
    Ledergerber, A., D’Andrea, R.: Ultra-wideband range measurement model with Gaussian processes. In: 2017 IEEE Conference on Control Technology and Applications (CCTA), pp. 1929–1934. IEEE (2017).  https://doi.org/10.1109/CCTA.2017.8062738. http://ieeexplore.ieee.org/document/8062738/
  7. 7.
    Ledergerber, A., D’andrea, R.: Calibrating away inaccuracies in ultra wideband range measurements: a maximum likelihood approach. IEEE Access 6, 78719–78730 (2018).  https://doi.org/10.1109/ACCESS.2018.2885195. https://ieeexplore.ieee.org/document/8561287/CrossRefGoogle Scholar
  8. 8.
    Liu, J., Pu, J., Sun, L., He, Z.: An approach to robust INS/UWB integrated positioning for autonomous indoor mobile robots. Sensors 19(4), 950 (2019).  https://doi.org/10.3390/s19040950. http://www.mdpi.com/1424-8220/19/4/950CrossRefGoogle Scholar
  9. 9.
    Masiero, A., Fissore, F., Vettore, A., Masiero, A., Fissore, F., Vettore, A.: A low cost UWB based solution for direct georeferencing UAV photogrammetry. Remote Sens. 9(5), 414 (2017).  https://doi.org/10.3390/rs9050414. http://www.mdpi.com/2072-4292/9/5/414CrossRefGoogle Scholar
  10. 10.
    McLoughlin, B.J., Pointon, H.A., McLoughlin, J.P., Shaw, A., Bezombes, F.A.: Uncertainty characterisation of mobile robot localisation techniques using optical surveying grade instruments. Sens. (Switz.) 18(7), 2274 (2018).  https://doi.org/10.3390/s18072274. http://www.mdpi.com/1424-8220/18/7/2274CrossRefGoogle Scholar
  11. 11.
    Pointon, H.A.G., McLoughlin, B.J., Matthews, C., Bezombes, F.A.: Towards a model based sensor measurement variance input for extended Kalman filter state estimation. Drones 3(1) (2019).  https://doi.org/10.3390/drones3010019. http://www.mdpi.com/2504-446X/3/1/19CrossRefGoogle Scholar
  12. 12.
    Ridolfi, M., et al.: Experimental evaluation of UWB indoor positioning for sport postures. Sensors 18(1) (2018).  https://doi.org/10.3390/s18010168. http://www.mdpi.com/1424-8220/18/1/168CrossRefGoogle Scholar
  13. 13.
    Sookyoi, T.: Experimental analysis of indoor positioning system based on ultra-wideband measurements (2016)Google Scholar
  14. 14.
    Vanegas, F., Gonzalez, F.: Enabling UAV navigation with sensor and environmental uncertainty in cluttered and GPS-denied environments. Sens. (Switz.) 16(5) (2016).  https://doi.org/10.3390/s16050666CrossRefGoogle Scholar
  15. 15.
    Wang, J., Raja, A.K., Pang, Z.: Prototyping and experimental comparison of IR-UWB based high precision localization technologies. In: Proceedings of 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, vol. 20, pp. 1187–1192 (2016).  https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.216

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Engineering and Technology Research InstituteLiverpool John Moores UniversityLiverpoolUK

Personalised recommendations