# NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio
## 2 Related Work
### Slam  
Most SLAM approaches based on wireless technology have been used for indoor applications, with the notable exception of outdoor GNSS-based SLAM [50]. Below, we review important body of research on _radio-based SLAM_. Among works that use UWB technology, one of the most notable is [51], which employs a Factor SLAM approach that considers the association of spectral MPCs and their geometric features. A Back Propagation (BP)-SLAM algorithm was used for efficient marginalization of the joint posterior distribution between the real anchors and virtual anchor positions. A particle filter was utilized for the state estimation of an agent in an indoor area, with the reported Root Mean Square Error (RMSE) below 0.2 m. Similarly, the Channel-SLAM approach proposed in [52] simultaneously estimated the receiver position and the positions of the virtual transmitters with a Rao-Blackwellized Particle Filter (RBPF) using UWB. The idea behind this work was the assumption that MPCs are LoS signals, originating from the so-called VAs. The validation was performed against the posterior Crammer-Rao lower bound in an indoor scenario. In [53], the authors used a static UWB anchor and a mobile pedestrian carrying a hand-held UWB tag and an Inertial Measurement Unit (IMU). A positioning accuracy near 1 m was achieved 95% of the time during experiments. The MPC reflection points allow for the identification of objects and walls, comparing it with an existing floor plan map. In [18], the UWB tag provides range measurements between the robot and anchors, which are used to update the Particle Filter (PF) weights. The proposed algorithm also includes a data association step to match the incoming range measurements with the corresponding anchors. The particle resampling was performed based on the importance sampling technique and the number of particles was adaptively adjusted to maintain the required accuracy level. The experimental results showed that only the proposed range (RO-SLAM) algorithm outperforms the traditional approaches in terms of accuracy and efficiency, at the cost of the computational overhead. The RadarSLAM algorithm was proposed in [54]. It utilizes a UWB super high-frequency pulse-echo radar sensor and a mobile robot equipped with a laser rangefinder for benchmarking purposes. The approach successfully converts the signals generated by the pulse-echo radar measurements to spectrograms, subsequently producing unique fingerprints of locations in the indoor environment. Moreover, [PERSON] et al. [55] proposed an unsupervised neural Radio Frequency (RF) SLAM method from unlabeled CSI samples in a MIMO setup using Reconfigurable Intelligent Surfaces (RIS),implementing a Multi-Layer Perceptron (MLP) with Time of Flight (ToF) signals as input. The 3D map representation is visualized as a pointcloud, where each point is color-coded based on its CSI magnitude. In [56], the exchange of radio signals between the mobile agents and static anchors using MPCs was studied. A multiple VA-based RF-SLAM method was used to jointly estimate the time-varying location of agents and the static locations of flat surfaces. The reported theoretical accuracy was 0.1 m on average in the estimation of the agents and the VAs alike. The work of [PERSON] et al. [57] demonstrated how existing WiFi infrastructure can be exploited for localization and mapping with low-power and low-computation hardware. In this experiment, a mobile robot equipped with a 3D LiDAR, a set of smartphones, and wheel encoders was used to perform similarity and loop closure through a two-step graph optimization process, using the WiFi signal fingerprints and allowing the mobile robot to navigate and correct its odometry drift. This method was able to attain a mean localization error of 0.88 m in a large industrial campus. A bearing-based WiFi SLAM approach for indoor robots was proposed in [58].