# NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio
## 1 Introduction
The developed Radio Environmental Map (REM) is used to determine the relative location of the robot to the gNB stations, by comparing the ideal signal propagation model with the estimation at the UE side, similar to the Inverse Distance Weighted (IDW) interpolation technique. The proposed mapping technique finds inspiration in fingerprinting approaches [11; 12], with the difference being that it is performed online using the 5G Urban Macro-Cell (UMa) models' equations [13] and with no prior collection points stored in a database. The radio maps serve as a simple representation of the signal propagation within the environment. We validate the 5G SLAM approach under two experimental setups, using the FR1 n78 band and the FR2 n257 band frequencies over a large perimetric trajectory within a simulated agricultural environment, using the UMa propagation model [14]. In order to demonstrate its significance, we compare NR5G-SAM with LIO-SAM [15], a state-of-the-art LiDAR-inertial SLAM framework to highlight the advantages and disadvantages it can potentially provide to the mobile robotics domain. This work is an extension of our previous work on 5G Positioning [16] and serves as an important development step towards an innovative and robust cellular SLAM framework for field robotics. The contributions of this work aim to answer fundamental questions of our ongoing research on the potential of using 5G NR for field robot localization. Namely:  
* Which are the relevant attempts for radio localization and mapping in field robotics? * Can we perform localization assisted by cellular 5G NR signals and Millimeter Wave (mmWave) and what accuracy can we expect? * Can we use 5G NR CSI to interpolate REMs at a given geographic location? * Can we use 5G NR coupled with other sensor modalities for localization and a REM as a radio SLAM framework? * How would the NR5G-SAM framework perform compared to a state-of-the-art LiDAR SLAM approach in a relevant outdoor environment? Our aim is to highlight that solely LiDAR-Inertial approaches are not sustainable on their own for long term localization in outdoor environments. The focus of this work is not to demonstrate the superiority of NR5G-SAM over LIO-SAM, but rather showcase the limitations of a LiDAR-Inertial approach in challenging outdoor environments, which is a commonly deployed sensor setup in autonomous vehicles and field robots globally and specifically cover the scenarios that GNSS is impaired or denied. Although the performance comparison may not seem entirely appropriate, we firmly believe in its relevance within the context of the paper's research objectives, especially for real-world robot applications. In the remainder of this document, we describe related work on radio technologies used for localization, mapping, and SLAM in Section 2. We also provide as a contribution a general survey of related works in this area based on relevant radio technologies. In the following Section 3, we present the design and implementation of the NR5G-SAM framework and in Section 4 we describe in detail the 5G FR1 and FR2 experimental test-beds. In Section 5, we present our experimental results and critically analyze the outcome of the 5G positioning and the proposed NR5G-SAM algorithm compared to the LIO-SAM approach. As a projection of our current outcomes, we critically analyze the challenges that lie in the future steps of this research in Section 6. Finally, in Section 7, we draw our conclusions and present the future research direction of 5G NR SLAM within field robotics.