# NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio
## 2 Related Work
### Slam
WiFi is integrated as a sensor to simultaneously locate the robot and estimate the WiFi Access Point (AP) positions in the environment using the complete CSI information and providing an inexpensive solution for indoor navigation using WiFi sensors. On the other hand, a Growing Gaussian Mixture Regression (GGMR) algorithm to simultaneously build a spatial map and a radio map was described in [59], which used a communication-based SLAM framework for indoor environments using mmWave signals. The method was tested in a simulation and the outputs were a signal power map and the position relative to the AP. In [60], a rescue robot mapped the WiFi signal strength in the working area, correcting the drift of its six Degrees of Freedom (DoF) gyro-odometry localization system. Gaussian-based WiFi localization was extended to full three-dimensional and the proposed approach comprised a three-stage SLAM process that involved: (_i_) mapping of RSSI values within the environment; (_ii_) Gaussian process learning; and (_iii_) WiFi localization. Additional sensors were also contemplated, such as an IMU for attitude information and a 2D rangefinder. An improved WiFi GraphSLAM algorithm based on signal strength was implemented in [19] and was suitable for a broader range of environments since it made no signature uniqueness assumptions and uses solvers that are more tractable to larger map sizes. Furthermore, 5G NR technology can be targeted to solve the SLAM problem, especially under GNSS-denied environments. However, only a small number of 5G SLAM works have been proposed so far. Given a set of gNB stations placed over large kilometric distances, position tracking can be seen as a non-linear combinatorial problem [16]. A robot carrying UE may use 5G NR to track its position over time by comparing the ranges from each base station and infer its position, as shown in Figure 1. The ranges to each gNB \(S_{n}(x,y)\) where \(n\) is the individual index can be either estimated from the UL and DL Time Difference of Arrival (TDoA) or ToA signals and quality information. In this context, inertial navigation systems can complement the system with the necessary angular information to estimate the orientation of the robot [19; 57] and create a pose estimate through sensor fusion. Also, fingerprinting methods allow one to compare the current observation with the fingerprint history or database to identify areas with similar signal qualities [61]. A 5G mmWave SLAM approach was presented in [62]. Context inference was used to detect obstacles and their type in the simulated indoor environment, based on the reflected signals, which act as mirrors of the transmitted and the authors concluded that by increasing the number of APs generally improves the localization and obstacle detection functions, assuming enough RSS, AoA, and ToA measurements. [PERSON] et al. [48] highlighted that with 5G mmWave signals it is possible to estimate the state of a vehicle (position and heading), from the transmissions of a single base station. They proposed a Bayesian 5G mmWave tracking filter, which explicitly relies on mapping the radio environment. The filter also acknowledged clock bias. Furthermore, a low-complexity multidimensional Estimation of Signal Parameters by Rotational Invariance Technique (ESPRIT)-based channel estimator applied to a 5G SLAM framework [63] and further described in [64] can provide the orientation of the UE in relation to the gNB stations. The potential to jointly estimate the positions of UE and map their propagation environment using a single base station was explored in [65]. The algorithm was designed to work in a diffuse multi-path channel environment where the orientation and the clock bias were unknown. By utilizing only the diffuse multi-path information, the algorithm allowed for robust operation. In addition, the statistical dependency between CSI and the position, orientation, and clock offset of a UE in a 5G network was studied in [66], allowing one to infer the state of a UE and map its propagation environment.