SLARM: Simultaneous Localization and Radio Mapping for Communication-aware Connected Robot
This work addresses the problem of creating accurate radio maps for communication-aware connected robots in unknown indoor environments, which is an incremental improvement for robotics and wireless communication.
This paper proposes SLARM, a framework for robots to simultaneously localize themselves and map both the physical environment and radio signal strength. It achieves a radio map accuracy of over 78.78% with a resolution smaller than 0.15m, and 91.95% accuracy at 0.05m resolution.
A novel simultaneous localization and radio mapping (SLARM) framework for communication-aware connected robots in the unknown indoor environment is proposed, where the simultaneous localization and mapping (SLAM) algorithm and the global geographic map recovery (GGMR) algorithm are leveraged to simultaneously construct a geographic map and a radio map named a channel power gain map. Specifically, the geographic map contains the information of a precise layout of obstacles and passable regions, and the radio map characterizes the position-dependent maximum expected channel power gain between the access point and the connected robot. Numerical results show that: 1) The pre-defined resolution in the SLAM algorithm and the proposed GGMR algorithm significantly affect the accuracy of the constructed radio map; and 2) The accuracy of radio map constructed by the SLARM framework is more than 78.78% when the resolution value smaller than 0.15m, and the accuracy reaches 91.95% when the resolution value is pre-defined as 0.05m.