# NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio
## 6 Lessons Learned  
The pose estimation capability of the UEB just based on 5G positioning requires filtering from an EKF or other optimization approach to minimize the large errors, which are introduced during signal reception. The signal quality and the physical interaction of the emitted signal with the environment is envisaged to be crucial to performance. An important factor during the early developments of the method is the geometry of the gNB stations. For our experiments, we made estimations for the robot inside the coverage area of the surrounding stations. In reality, with a mobile robot in a dynamic mobility scenario, this can have a detrimental effect on the localization unless mitigation actions are considered, such as automatic cell selection and switching to maintain the integrity of localization performance. Signal reflections, scattering, and refractions occur in real applications, and interference from other sources will impact the uncertainty within the measurements as additional noise. We assumed that the noise is Gaussian, but in reality this may vary. However, we foresee that the growing availability of 5G NR in urban areas where it is currently widely available as SA or Non Stand Alone (NSA) and further research, will resolve some of these important issues. Within this study, we show that it is possible to estimate the pose of the robot via sensor fusion with an EKF, along with inertial sensing and robot wheel velocities using 5G NR. We utilize the IMU as an orientation, angular velocity and acceleration source, as it is widely available as an onboard resource of mobile robotic systems. We have considered the LoS signals from gNBs, which is not necessarily a limitation, as there are rural and urban regions that have these qualities. Despite our choice for simplification, Non-Line-of-Sight (NLoS) is a foreseen problem and multi-path effects in the FR1 frequencies. In FR2, it has been shown in previous works that a multi-path signal can help in the pose estimation process within urban and indoor environments, as the emitted rays have a low penetration ability on structures and instead reflect off of them, hence can be used as VAs for ray-tracing approaches. In terms of FEB, we have shown that the factor graph adoption within the proposed approach can provide a smoothed localization that is robust over time. The errors are handled efficiently and the predictions are fast at 200 Hz, providing a real-time performance. It has assisted us in creating a modular representation that is capable of modeling the  
Figure 13: A 3D representation of the global 5G REM generated from the individual gNodeB radio maps. Peaks represent the gNodeB locations and the color highlights the signal strength degradation throughout the environment. relationship of the robot motion with the IMU factor, the robot pose from the NR5G factor and RSSI factor measurements, during the design phase of the overall SLAM problem. Additionally, it provided us with modularity and extendability of NR5G-SAM from the early design phase, which is highly attractive for maintaining and scaling the SLAM approach with additional sources as factors. The BEB serves as an example of how signal metrics can assist the pose estimation process of a robot in outdoor environments. In our approach, we have used the RSSI values from each gNB station along with the UMa model to predict the position of the robot. We have chosen to use the RSSI as an experiment due to the wide adoption in UWB and WiFi approaches for position estimation [10]. The BEB pose estimates present, in general, more noisy behavior compared to multilateration output. Moreover, we chose this direction as it can provide a double benefit of generating REMs and a pose estimate for robot localization purposes.