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

TL;DR

The paper introduces SLARM, a framework that combines SLAM and radio mapping to enable communication-aware navigation for robots in unknown indoor environments, achieving high radio map accuracy.

Contribution

It presents a novel integrated approach for simultaneous geographic and radio map construction, improving radio map accuracy in indoor robot navigation.

Findings

Radio map accuracy exceeds 78.78% at resolutions below 0.15m.

Accuracy reaches 91.95% at a 0.05m resolution.

The resolution significantly impacts the radio map accuracy.

Abstract

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.