# NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio
## 1 Introduction  
Simultaneous Localization and Mapping (SLAM) is a fundamental research topic in robotics [1; 2], which significantly impacts prolonged operation of mobile robots. Several methods have been proposed to solve the SLAM problem, e.g., using Light Detection And Ranging (LiDAR) [3] and visual-based methods [4] to build a representative map, while maintaining the robot localized within that map. Traditionally, this is accomplished by obtaining the physical ranges between the robot and its surrounding objects or through the detection of suitable features to track within the target area. Modern SLAM approaches utilize multi-modal sensory systems to perceive the robot's environment [5]. In outdoor areas, uncertainty increases dramatically as mobile robots traverse uneven terrain and dense urban areas, such as densely structured cities but also rural unstructured environments, such as forests, mines and offshore. In these scenarios, visual and laser cues can be limited due to many factors: the homogeneity of the environment, judidering motion from rough terrain, illumination variations as well as temporary seasonal changes, e.g., fog, rain, wind, dust and smoke clouds. Global Navigation Satellite Systems (GNSS)-denied outdoor scenarios are common due to the geometry of the environment, where multi-path and shadow fading affect the atmospherically received signal, especially in mountainous terrains and urban canyons. Moreover, urban structures are a source of obstruction for signals arriving from satellites by limiting thevisible horizon of a receiver. Hence, the availability of fix measurements and their quality may be obsolete. The above factors introduce error into the localization system of GNSS-dependent robots and can be detrimental during outdoor navigation and mapping tasks. To mitigate this problem, a terrestrial cellular Fifth Generation Mobile Network (5G) New Radio (NR) positioning method can provide significant advantages when coupled with standalone modalities, such as visual-, LiDAR-, inertial-, and radar-based approaches. It is also less prone to the risks identified for GNSS, as it benefits from 5G being ubiquitously deployed in urban and suburban areas around the world, with cell availability not being a major concern for positioning applications [6]. As such, it is foreseen to boost new industrial use cases for the domains of autonomous vehicles and robotics, such as healthcare, food production and agriculture, city management, public transportation, manufacturing, and asset maintenance. In this study, we propose NR5G-SAM, a 5G NR SLAM framework which uses multi-lateration and Time of Arrival (ToA) ranging to generate a tightly coupled cellular radio-inertial pose estimation. This approach is grounded on the factor graph SLAM algorithm, described comprehensively in [7], which solves the full SLAM problem by estimating the robot's localization, while mapping the environment and keeping track of the pose history as a set of graph nodes. Through an estimation of the Maximum A Posteriori (MAP) likelihood of the full SLAM problem, we create a sparse graph from the set of estimated robot poses, and perform iterative Smoothing and Mapping (SAM) [8; 9] to improve the localization by a set of factors. The estimation of robot positioning is computed from the transmitted Positioning Reference Signal (PRS) between each gNodeB (gNB) base station to the User Equipment (UE), formally known as the Down-Link (DL) signals. Channel State Information (CSI) can be complementary used with the goal to improve the estimation process and mitigate positioning errors [10]. Therefore, we create a radio map of the environment, consisting of Received Signal Strength Indicator (RSSI) measurements from each gNb, which can be utilized as features during navigation within the environment.