# NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio
## 4 Experimental Validation
In this work, we set the number of gNB stations to \(n=5\) with their fixed locations as the following list of coordinates in the world frame: s1 = [800,\(-\)800], s2 = [\(-\)800,\(-\)800], s3 = [1000, 0], s4 = [\(-\)700, 700], and s5 = [300, 900], as illustrated in Figure 6. The starting point of the robot is the origin [0,0] position. To allow for a comparative evaluation of methods, we have driven the robot within the environment, as shown in Figure 5, while recording all of the necessary signals and raw data, including the ToA information, which is converted to a position within the UEB, along with the associated CSI measurements. We also extract the ground truth trajectory of the mobile robot from the simulator, which we use only for evaluating the localization accuracy of the estimation methods compared. Figure 6: Base stations locations for the experiment using 5G NR Macro cells and the starting location of UE that is attached to the robot base. Note that the right-hand side of the environment consists of inclined solar panels mounted on the ground inside the fenced area. On the left-hand side, there is an empty area that is surrounded only by wooden fences, which delimits the two sides of the environment. The transition between the two areas is made through an empty barn structure, which is not modeled with signal reflection or obstruction properties, as it is out of our current scope. The overall dimensions of the environment are \(120\times 90\) m.  
In terms of positioning, the GA optimization of the UEB follows the setup presented in [16]. For the BEB, the GA has been set with a population size of 200 and an elite count of 0.05. The maximum iterations has been set to 20. Regarding the EKF setups, we provide IMU and wheel velocities from the onboard sensors along with the 5G and RSSI positioning to generate the pose estimates for each block using the implementation described in [79], which is a well-established ROS-based localization framework in the robotics community. We compare our proposed method with LIO-SAM ([[https://github.com/TixiaoShan/LIO-SAM](https://github.com/TixiaoShan/LIO-SAM)]([https://github.com/TixiaoShan/LIO-SAM](https://github.com/TixiaoShan/LIO-SAM)), accessed on 7 March 2023), a LiDAR-Inertial SLAM approach [15], in order to demonstrate its capabilities. The reasoning for this decision is that it is a widely used state-of-the-art factor graph-based approach that utilizes GTSAM ([[https://github.com/borglab/gtsam](https://github.com/borglab/gtsam)]([https://github.com/borglab/gtsam](https://github.com/borglab/gtsam)), accessed on 7 March 2023), a C++ factor graph optimization library developed by [PERSON] et al. [83], for real-time pose estimation and mapping. A LiDAR sensor modeled after the Velodyne VLP-16 ([[https://velodynelidar.com/products/puck/](https://velodynelidar.com/products/puck/)]([https://velodynelidar.com/products/puck/](https://velodynelidar.com/products/puck/)), accessed on 7 March 2023) is added to our robot to be used for localization and mapping of the environment with LIO-SAM. The parametric setup of the algorithm is set to the default and we followed the author's guidelines to set it up for outdoor environments. We draw similarities in the modeling of NR5G-SAM as we are utilizing the GTSAM library for smoothing and mapping, with the difference that we developed a cellular radio-inertial pose estimation and mapping method. Regarding the sensor acquisition rates, the IMU provides data at 200 Hz and the wheel odometry is communicated at 50 Hz. The 5G positioning publishes at the rate of 25 Hz and is representative for the sub-6 GHz FR1 setup. For the mmWave FR2 setup, the publication rate is 35 Hz, which is faster as we are transmitting in the mmWave frequency. The NR5G-SAM localization is published at the IMU frequency and the mapping pose is published at 3 Hz due to the heavy GA optimization over each REM. The evaluation metrics follow next.