# NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio
## 5 Results and Discussion  
In this section, we discuss the results obtained using the proposed 5G SLAM method in the delineated experimental scenarios. In general, we consider the performance of NR5G-SAM as a standalone radio-inertial SLAM approach to be an attractive choice for adaptation within field robot applications. In more detail, we analyze the performance of the method in the following paragraphs. Overall, NR5G-SAM performed adequately in the FR1 setup by scoring sub-meter accuracy on average (as shown in Table 1), while traversing the environment. The setup used a transmission frequency of 3.5 GHz and sub-carrier spacing of 60 kHz, which is a band used already by smartphones. The overall trajectories performed can be seen in Figure 7, where the 5G positioning from the UEB can be seen as a noisy trajectory in green, due to weak signals arriving from gNBs, which are at a distance. The estimated trajectories of the FEB in red and the BEB in cyan for this use case can be seen as smoothed trajectories meeting the half meter error mark on average overall. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **5G Positioning** & **NR5G-SAM** & **LIO-SAM** \\ \hline Prior & \(0.90\pm 0.66\) & \(0.55\pm 0.47\) & \(0.12\pm 0.26\) \\ Post & \(0.91\pm 0.65\) & \(0.52\pm 0.46\) & \(52.87\pm 106.84\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: FR1 results for the estimated trajectories for 5G Positioning, NR5G-SAM, and LIGO-SAM, including the average Root Mean Squared Errors and their Standard deviations for the cases of prior and post LIGO-SAM failure (in meters). As we increased the sub-carrier spacing to 240 kHz and the transmission frequency to 29.5 GHz, we experienced the advertised benefits of mmWave as a large improvement in the 5G positioning, which is reflected by the results of Table 2 and the demonstrated trajectories of Figure 8. The estimated trajectories demonstrate a great potential in approaching the ground truth performance, with error results similar to other works [33]. A noteworthy result is that LIGO-SAM failed to estimate the localization for the full robot trajectory due to the lack of features and the homogeneity of the environment. A highlight of the error distributions can be seen in Figure 9b for FR1 and Figure 10 for the FR2 setup with an emphasis on the moment of failure for LIGO-SAM. It can be observed that the error increases quadratically. This did not occur during the right half of the trajectory (as shown in Figure 5) due to having more features to track, such as the solar panel edges  
Figure 8: The trajectories performed under the FR2 Setup using mmWave frequencies. Figure 7: The trajectories performed under the FR1 Setup using sub-6 GHz frequencies. and planes. This is a common disadvantage of LiDAR-based SLAM approaches in outdoor environments such as rural areas, either due to the similarity of features or the lack of them. On the other end, NR5G-SAM presents immunity against these type of errors and is able to support the robot throughout the whole experiment, as is evident in the generated trajectories as it is a radio-based approach. Figure 9: (**a**) Overall estimated trajectories, including a highlight of the (**b**) average RMSE error distributions, with emphasis on the failure moment and (**c**) LID-SAM getting lost due to the lack of features; we maintain localization from NR5G-SAM. In terms of the localization performance, the comparison considers the 5G positioning from the User Equipment Block of our approach and the results from the NR5G-SAM framework, as well as LIO-SAM. We have measured the average RMSE for the FR1 setup (Table 1) and the FR2 setup (Table 2) by considering both cases: prior and post LIO-SAM failure. In the FR1 setup and prior to LIO-SAM failure, the error for 5G positioning is 0.90 m and NR5G-SAM presents 0.55 m with a standard deviation of \(\pm\)0.47 m, which is an improvement from our previous work [16], as we have utilized an EKF for filtering the large deviations.