## 10 Conclusions  
In this survey, we addressed RF-based localisation mostly from a robotic point of view. First, we explored the methods that exist for RF-based localisation extensively under two classes: Rage-based and Fingerprinting. Then, we investigated and compared current state-of-the-art RF-based localisation applied to the robotic areas. Subsequently, we discussed the challenges and solutions that 5G will provide. Finally, the future research direction was given. 5G NR will introduce features that could be harnessed to revolutionize localisation and robots' applicability, such as low latency, high throughput, and high-resolution angular and time-based measurements. However, 5G-based localisation research is still in its infancy. Thus, many possibilities to exploit this novel communication technology in the robotic localisation context have been left unexplored. Nonetheless, the current solutions adopted for general RF receivers can be applied to 5G without modification. Until now, cameras, IMU, LIDAR, and GPS represented the predominant choice for building a SLAM system. However, the unique characteristics introduced by the 5G NR can establish RF-based localisation among the most common robotic tools for safe autonomous navigation. Leveraging such unexploited features and surpassing the main technological obstacles will be the focus of future research to ensure seamless integration of 5G into the current localisation system. Writing--original draft, M.K. ; Writing--review & editing, C.C. ; Supervision, H.B., J.L.S.-L. and H.V. All authors have read and agreed to the published version of the manuscript. This work was funded by the Luxembourg National Research Fund (FNR) 5G-SKY project (ref. C19/IS/13713801, and by the European Commission Horizon2020 research and innovation programme under the grant agreement No 101017258 (SESAME). Not applicable. +++ ==WARNING: Truncated because of repetitions==  
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