# NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio
## 2 Related Work
### Slam
According to [67], 5G systems operating above 24 GHz have promising properties for localization and mapping. They proposed tackling the SLAM problem in four phases: (_i_) DL data transmission; (_ii_) multi-dimensional channel estimation; (_iii_) channel parameter clustering; and (_iv_) SLAM based on a novel likelihood function. The proposed approach was able to decompose the problem into simpler steps, thus leading to lower overall complexity. In the simulation, their ESPRIT variants utilized the same signal subspace to extract angular frequencies in all dimensions, allowing for the usage of the same channel distributions in the likelihood function, hence producing a new faster channel estimator for 5G SLAM with a low computational complexity. In recent years, there has been significant progress in the development of efficient, consistent, and robust visual- and LiDAR-based SLAM algorithms, which have emerged as the leading approaches [68; 69; 70; 71; 72; 73]. These methods utilize a combination of camera and  
Figure 1: Positioning with 5G NR and User Equipment (UE) by performing trilateration between gNBs and the UE on a mobile robotic system [16]. LiDAR sensors to create high-quality maps of environments combined with accurate pose estimators. With the use of deep-learning techniques for feature extraction and localization, significantly improved the accuracy and robustness of these algorithms. Furthermore, recent advances in optimization methods, such as nonlinear pose graph optimization, enhanced the consistency and efficiency of the algorithms. Overall, these developments led to the increased adoption of visual- [4] and LiDAR-based [3] SLAM algorithms in various real-world applications within the robotics and autonomous vehicle communities. Most importantly, scene recognition capability is an important aspect of any algorithm, as accurate location information related to detected landmarks can be leveraged as absolute references. Yet, in radio SLAM methods it is harder to recognize revisited places through the feature extraction of features as signal propagation is spatiotemporarily dynamic. Substantial progress in the development of efficient, consistent, and robust _visual- and LiDAR-based SLAM_ algorithms has been witnessed in the latest decades [15; 68; 69; 70; 71; 72; 73]. Scene recognition capability is an important aspect of any SLAM algorithm, as accurate location information related to detected landmarks can be leveraged as absolute robot locations. Yet, in radio localization methods it is harder to recognize revisited places through feature extraction of landmarks as in visual SLAM approaches [4], and future work is foreseen towards this direction. In this work, we address the full SLAM problem in which we are interested in generating a map, while estimating the posterior probability of the robot's full trajectory \(x\) and map \(m\) given the sensor measurements \(z\) and control inputs \(u\) over time, \(p(x_{1:t},m|z_{1:t},u_{1:t})\), as opposed to online SLAM, which considers only the most recent pose for state estimation. While conventional SLAM builds a geometrical map of the environment (e.g., see [74]), we use cellular RSSI measurements to obtain a mapping between signal strength measurements [11; 75] and the locations of the base stations that transmit them. We represent the signal propagation via REMs, which are useful as part of the research around radio-based SLAM approaches [76; 77], having the potential to assist robot localization [78].