# NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio
## 2 Related Work
### Radio Technologies and 5G NR  
Several radio-based technologies have been used for positioning purposes, including Bluetooth Low Energy (BLE), Ultra Wideband (UVWB), Wireless Fidelity (WiFi), Long-Range Protocol (LoRa), and 4G Long-Term Evolution (LTE) [17; 18; 19; 20; 21] with varying degrees of success. Currently, 5G is a study item for the Third Generation Partnership Project (3 GPP) Release 17, with the potential to not only outperform GNSS, but also enable accurate estimations of orientation [22]. 5G NR provides higher speeds, lower latencies, and larger bandwidth capacities than 4G-LTE [23], which can be very attractive for field robotic applications and the future of the Internet of Things (IoT), as Mobile Edge Computing (MEC) is now a reality in many regions around the world. _Radio-based localization_ occurs in two steps. Firstly, the radio waves need to be converted to ranges and/or angle information, e.g., using range-free or range-based approaches [24]. Secondly, depending on the environment topology and application requirements, triangulation, trilateration, multilateration, proximity, and scene analysis can be adopted to obtain the pose information for tracking purposes. An in-depth analysis of the methods can be seen in [25; 26]. GNSS is commonly used for positioning with outdoor robots. However, the signal quality is not always available, especially in rural areas such as dense forests and canyons, but also in building-congested cities. With the high degree of resolvability of propagation paths in mmWave frequencies, multi-path information can be exploited in 5G both for positioning and mapping of the environment. Ray tracing is a popular technique that has given promising results for mmWave signals at the cost of additional computational resources [27]. The general requirements to achieve high accuracy radio localization in 5G cellular are: (i) very large data-to-transmission rates; (ii) application-dependent low latency; (iii) seamless Physical Layer (PHY) integration; (iv) low computational overhead; (v) continuous tracking without loss of frames; and (vi) recovery mechanisms. Several 5G-based localization challenges have been identified in the past [28] including: (i) signal reflection, (ii) radio interference, (iii) obstruction, (iv) multi-path, (v) limited coverage, and (vi) cell resource loads at peak user times. In [29], the authors exploited the mmWave band of 5G to provide precise position information for location-based services considering the deployment costs and ease of installation. The algorithm was based on Bayesian tracking filter, which makes use of Multi-Path Components (MPC) to provide position information. Emphasis was given to the data association as a core block, which was used to associate the arrival time of each MPC to the predicted delay. A Maximum Likelihood Estimator (MLE) was used to estimate the arrival time from the received 5G signal. Moreover, the predicted delay was then computed from the positions of a Virtual Anchor (VA), which is the occurrence point of a signal reflection, and the estimated user position. The output of the data association block was given as an input to the Bayesian filter to continuously track the user position with a resulting Line-of-Sight (LoS) error of 2-3 cm in indoor setups. In [30], joint positioning and clock synchronization in 5G Ultra-Dense Network (UDN) using a two-stage Extended Kalman Filter (EKF) was proposed. The model considered a high-density Base Station (BS) with Multiple-Input Multiple-Output (MIMO) arrays. This allows the UE to have a LoS with multiple BS for most of the time, thereby increasing the accuracy of the cascaded EKF system. The first stage of EKF active on each BS, tracks the Direction of Arrival (DoA) and ToA of incoming uplink signals from UE. The second stage that runs on a central node in the cloud, estimates the position of the UE by fusing the DoA and ToA estimates from multiple BS. In addition, the clock drift between UE and network can be obtained.