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A novel simultaneous localization and radio mapping (SLARM) framework forcommunication-aware connected robots in the unknown indoor environment isproposed, where the simultaneous localization and mapping (SLAM) algorithm andthe global geographic map recovery (GGMR) algorithm are leveraged tosimultaneously construct a geographic map and a radio map named a channel powergain map. Specifically, the geographic map contains the information of aprecise layout of obstacles and passable regions, and the radio mapcharacterizes the position-dependent maximum expected channel power gainbetween the access point and the connected robot. Numerical results show that:1) The pre-defined resolution in the SLAM algorithm and the proposed GGMRalgorithm significantly affect the accuracy of the constructed radio map; and2) The accuracy of radio map constructed by the SLARM framework is more than78.78% when the resolution value smaller than 0.15m, and the accuracy reaches91.95% when the resolution value is pre-defined as 0.05m.

SLARM: Simultaneous Localization and Radio Mapping for Communication-aware Connected Robot