# NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio
## 3 Methodology
### NR5G-SAM System  
The system receives sensor data from the 5G NR network, which we consider to be a 5G Stand Alone (SA) deployment and time synchronized, as well as the simulated onboard IMU. Additionally, it integrates wheel odometry velocity measurements. By using the measurements as observations, it keeps track of the robot within the environment and estimates its trajectory over time. Robot tracking is a non-linear optimization problem that can be formulated as a least squares process of computing the posterior state and is analogous to MAP. Factor graphs provide benefits by modeling the problem in a decoupled way, which allows for a modular design for SLAM algorithms, with a reference in several works such as [7; 15]. Hence, we adopt it to model the 5G NR signal and sensor measurements as factors.  
Three factors have been introduced within the framework, namely: (_i_) NR 5G factors; (_ii_) IMU pre-integration factors; and (_iii_) RSSI factors. All of these factors are binded by the robot state vector. We add a new node to the graph for every measurement that is received over time and we assume that all noise from the measurements and motion of the robot is Gaussian noise. In general, it is a good fit for many types of natural and human-made noise sources, such as electronic and thermal noise in sensors and also for atmospheric disturbances. Hence, we have assumed that the noise present in sensors and the 5G signals follows the Gaussian distribution as a practical approximation of their complex noise models. Additionally, it can account for the effects of multiple sources of noise that combine to produce the total noise effect on the robot's state estimation process, present in many real-world phenomena and promotes the use of statistical estimation methods for modeling such as factor graphs. The generated factor graph is used to predict the localization of the robot through incremental smoothing and mapping (iSAM2) [9].  
The overall SLAM framework consists of three core blocks. The User Equipment Block (UEB) concerns the signal reception and pre-processing of the CSI signals and DL signals and converts them into positioning information. The Front-End Block (FEB) is responsible for generating the factor graph and the robot localization by considering IMU and either NR5G or RSSI factors. The Back-End Block (BEB) is responsible for computing the individual radio environmental maps, via interpolation from RSSI measurements and the 3 GPP UMa propagation model during navigation. This process generates a mapping pose estimate that is integrated within the FEB block for state prediction and corrections. As a representation of the fading signal, we also output a global 5G NR REM from the estimated individual radio maps. The full architecture is illustrated in Figure 2 and the analysis follows for each block in the forthcoming subsections.  
Figure 2: System architecture of NR5G-SAM consisting of three core components: (i) UEB, which deals with the UE signal reception and positioning; (ii) FEB, which performs factor graph optimization and computes the localization pose; and (iii) BEB, which addresses radio mapping, based on RSSI measurements of each gNodeB station, then generates the global REM and mapping pose estimation back into the FEB.