High-precision UWB based localisation for UAV in extremely confined environments

Yang, Beiya and Yang, Erfu and Yu, Leijian and Loeliger, Andrew (2022) High-precision UWB based localisation for UAV in extremely confined environments. IEEE Sensors Journal, 22 (1). pp. 1020-1029. ISSN 1530-437X (https://doi.org/10.1109/JSEN.2021.3130724)

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In this paper, a high-precision ultra-wideband (UWB) based unmanned aerial vehicle (UAV) localisation approach is proposed for applications in extremely confined environments. It is motivated by the emerging demand on autonomous inspection in such environments that are hard or impossible for humans to access. Instead of the traditional localisation techniques such as global positioning system (GPS), vision based or other localisation techniques, the UWB based localisation technique is adopted for precise UAV positioning due to its high accuracy, implementation simplicity and suitability in such environments. To avoid the requirement on strict synchronisation between sensor nodes and provide decimetre-level accuracy, the proposed algorithm combined the two-way time-of-flight (TW-TOF) localisation scheme with the maximum likelihood estimation (MLE) method. This differs from applications in other environments, the number and deployment area of anchor nodes are highly restricted in such environments. Therefore, an in-depth investigation for the anchor deployment strategies is presented to find the most suitable geometry configurations with accurate and robust performance. Finally, extensive simulations, static experiments and flight tests have been conducted to validate the localisation performance under different deployment strategies. The experiments show that average localisation error and standard deviation (STD) under 0.2 m and 0.07 m are obtainable by using our proposed approach under three different geometry configurations of anchor nodes. This is suitable for different applications in extremely confined environments.


Yang, Beiya, Yang, Erfu ORCID logoORCID: https://orcid.org/0000-0003-1813-5950, Yu, Leijian and Loeliger, Andrew;