# NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio
## 7 Conclusions  
In this work, we have presented the implementation of a preliminary cellular NR5G-SAM framework. We have evaluated its localization accuracy in terms of average RMSE and their standard deviations in two-dimensional Cartesian space for two radio setups in a simulated agricultural environment. We have also compared it to LIO-SAM to evaluate the robustness and overall performance. We have demonstrated that a cellular 5G SLAM can assist in localizing a robotic system in an outdoor setup with relatively low error margins in FR1 and FR2 frequencies. The overall accuracy was 0.52-0.55 m for sub-6 GHz and 0.19-0.23 m for the mmWave frequency, which is comparable to the performance of modern single receiver GNSS methods [22; 85]. Due to the early stage of the work, we did not account yet for multi-path effects, including regions of non-line of sight (NLoS) use cases, which exist in real-world scenarios and will be addressed as part of our future work. Moreover, we presented an RSSI mapping representation process that can be further improved and used for the SLAM pose estimation correction process alongside the RSSI positioning. NR5G-SAM can provide many benefits to the robotics communities as an additional pose information source in multi-modal sensor fusion approaches combined with GNSS, LiDAR, visual, and inertial sensing. In fact, LiDAR technology is already industrially mature compared to 5G NR sensing, but with time and further efforts it should be able to surpass GNSS in areas with coverage (e.g., see [22]) and support robotics applications in GNSS-denied environments. An open question which inevitably falls into the scope of our future research is the comparison of NR5G-SAM under a real-world scenario against GNSS and its variants, as it would provide informative results compared to the global standard in outdoor positioning. As a future development stage, we also intend to explore the estimation of orientation from AoA and AoD approaches, the reason being that 5G NR introduces the concept of beam-forming where each signal can be traced to the base station that emitted it, revealing the azimuth angle of transmission. Also, the angle of reception can be estimated from the robot with an antenna array. Regarding velocities, similar to GNSS systems, the estimation of motion can be performed via the Doppler effect [86], which can be used as a reference to minimize uncertainty, and the UEB RSSI variability can be learned with machine learning methods [87; 88], having the potential to be included and adopted within our framework. Moreover, we foresee the integration of the complete CSI parameters such as the Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ), which are often used for cellular signal diagnostics, in the optimization process and including them as factors in the factor graph. We also intend to introduce a loop closure constraint within NR5G-SAM based on the complete set of signal propagation parameters and REMs to further improve the localization performance of the framework. With this additional capability, we aim to tackle multi-path effects and even take advantage of them for localization as unique NLoS features. Furthermore, a macroscopic ambition would be to combine spatial maps acquired by LiDAR sensors with REMs to enable enhanced localization and scene understanding capabilities in outdoor environments that can assist in mapping GNSS-denied environments, such as indoors, urban, and other rural areas (e.g., forests, mines). Effective communication will be an important challenge for robotic systems operating in outdoor complex environments to support localization capabilities. All in all, we intend to perform an analysis of the method's robustness under an urban scenario and with degraded cellular signals, which would assess the performance of the algorithm under various levels of 5G signal quality.