论文标题
Slarm：同时本地化和无线电映射，用于通信感知的连接机器人
SLARM: Simultaneous Localization and Radio Mapping for Communication-aware Connected Robot
论文作者
论文摘要
提出了一个新颖的同时本地化和无线电映射（SLARM）框架，用于在未知的室内环境中进行通信感知的连接机器人，其中同时本地化和映射（SLAM）算法（SLAM）算法以及全球地理地图恢复（GGMR）算法将算法付诸实践，以同时构建一张地理图和无线电图的频道图。具体而言，地理图包含障碍物和可通过区域的精确布局的信息，无线电图表征了访问点和连接的机器人之间的位置依赖性最大预期通道功率增益。数值结果表明：1）SLAM算法中的预定分辨率和所提出的GGMR算法显着影响构造的无线电图的准确性； 2）当分辨率值小于0.15m时，由Slarm框架构建的无线电图的准确性超过78.78％，当分辨率值预先定义为0.05亿时，精度达到91.95％。
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.
