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Localizing Backscatters by a Single Robot with Zero Start-Up Cost

机译：单个机器人本地化背拍，零启动成本

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摘要

Recent years have witnessed the rapid proliferation of low- power backscatter technologies that realize the ubiquitous and long-term connectivity to empower smart cities and smart homes. Localizing such low-power backscatter tags is crucial for IoT-based smart services. However, current backscatter localization systems require prior knowledge of the site, either a map or landmarks with known positions, increasing the deployment cost. To empower universal localization service, this paper presents Rover, an indoor localization system that simultaneously localizes multiple backscatter tags with zero start-up cost using a robot equipped with inertial sensors. Rover runs in a joint optimization framework, fusing WiFi-based positioning measurements with inertial measurements to simultaneously estimate the locations of both the robot and the connected tags. Our design addresses practical issues such as the interference among multiple tags and the real- time processing for solving the SLAM problem. We prototype Rover using off-the-shelf WiFi chips and customized backscatter tags. Our experiments show that Rover achieves localization accuracies of 39.3 cm for the robot and 74.6 cm for the tags.
机译：近年来已经见证了低功率反向散射技术的快速增殖，以实现无处不在和长期连通性，以赋予智能城市和智能家园。本地化如此低功耗反向散射标记对于基于物联网的智能服务至关重要。然而，当前的反向散射定位系统需要先验的网站知识，映射或具有已知位置的地图，增加部署成本。为了赋予通用本地化服务，本文介绍了Rover，一个室内定位系统，同时定位多个反向散射标签，使用配备有惯性传感器的机器人进行零启动成本。 Rover在联合优化框架中运行，融合了基于WiFi的定位测量，具有惯性测量，同时估计机器人和连接标签的位置。我们的设计解决了多个标签中的干扰等实际问题以及解决SLAM问题的实时处理。我们使用现成的WiFi芯片和定制的反向散射标签原型揽胜。我们的实验表明，Rover为机器人提供了39.3厘米的定位精度，标签为74.6厘米。

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