Simultaneous Localization and Radio Environmental Mapping with CSI-based Loop Closure

The advent of cellular 5G technologies introduces positioning as a new service, poised to enhance robotics applications through mobile communications in both urban and rural settings. Field robots in outdoor environments, are faced with unreliable GNSS coverage in urban/rural canyons and obstructions. In featureless terrains and under poor illumination, optical sensing may suffer from significant drift, impairing robot localization. We propose NR5G-SAM-LC, a radio SLAM framework which is signal-independent, capable of utilizing 5GNR, WiFi, or UWB signals, functioning as a standalone or complementary pose information source to these problems. The framework’s efficacy is demonstrated using an UWB testbed, chosen for its similar signal structure and propagation characteristics to 5GNR systems in the FR1 bands and replicate irregular cellular station geometry placement in urban scenarios. The novelty of this work lies in the exploitation of LoS, ambiguous LoS, NLoS conditions, along with station availability and signal strength vectors to improve pose estimation. A multi-link Channel State Information (CSI) interpolation algorithm and radio loop closure methodology is developed that models Received Signal Strength Indicator (RSSI) measurements as radio point clouds, enabling the construction of Radio Environmental Maps (REM). Radio loop closures (LC) are explored as a potential localization aid for radio factor graph SLAM systems. Field experiments using a UGV indicate that REM can play a key role in supporting robotic systems by complementing pose estimation robustness with the application of a spatial transform, and radio loop closures can be used to detect previously visited locations based on similar signal signatures with varying degree of accuracy. Finally, the system is evaluated by comparing the individual block elements, UEB, BEB-REM, BEB-LC and FEB with onboard GNSS RTK and a LiDAR SLAM approach, highlighting its potential for enhancing the robustness of robotic autonomy in challenging real-world scenarios. Results indicate that REM in SLAM combined with radio loop closure capabilities of multi link signatures, can reduce the localization RMSE by 11%, compared to range only localization and radio loop closures can occur at previously visited locations reducing long term navigation errors and temporary drift in multimodal SLAM systems.