This paper presents the WiFi-Sensor-for-Robotics (WSR) toolbox, an open source C++ framework. It enables robots in a team to obtain relative bearing to each other, even in non-line-of-sight (NLOS) settings which is a very challenging problem in robotics. It does so by analyzing the phase of their communicated WiFi signals as the robots traverse the environment. This capability, based on the theory developed in our prior works, is made available for the first time as an opensource tool. It is motivated by the lack of easily deployable solutions that use robots' local resources (e.g WiFi) for sensing in NLOS. This has implications for localization, ad-hoc robot networks, and security in multi-robot teams, amongst others. The toolbox is designed for distributed and online deployment on robot platforms using commodity hardware and on-board sensors. We also release datasets demonstrating its performance in NLOS and line-of-sight (LOS) settings for a multi-robot localization usecase. Empirical results show that the bearing estimation from our toolbox achieves mean accuracy of 5.10 degrees. This leads to a median error of 0.5m and 0.9m for localization in LOS and NLOS settings respectively, in a hardware deployment in an indoor office environment.
翻译：本文展示了WiFi- 传感器软件工具箱( WSR), 这个开放源代码 C++ 框架。 它使团队中的机器人能够获得相对相对的相对比重, 即使在机器人中非常具有挑战性的非视觉( NLOS) 设置中也是如此。 它通过分析机器人穿越环境的机器人所传送的WiFi信号的阶段来这样做。 基于我们先前作品中开发的理论, 这个能力首次作为开放源代码工具提供。 它的动机是缺少使用机器人本地资源( e. g WiFi) 进行感测的容易部署的解决方案。 这对本地化、 自动热控机器人网络以及多机器人团队的安全都有影响。 工具箱的设计是为了使用商品硬件和机载传感器在机器人平台上传播和在线部署。 我们还在 NLOS 和 线( LOS) 设置中展示了多机器人本地化使用软件的性能。 这个“ 网络” 结果分别显示, 当地部署成本 0.10 和“ 0. 10 ” 中, 从我们定位工具箱的定位环境中, 显示一个“ 0. 10 ” 和“ 10 ” 中, 以 10 的“ 中, 从当地部署“ 中, 10” 的“ 中, 显示一个“ 10” 和“ 10” 的“ 的“ 10” 的“ 的“ 的“ ” 硬度” 硬度” 硬度” 定位” 定位” 定位” 定位”, 显示一个“ 定位” 的“ 定位” 的“ 的“ 的“,, 定位” 定位” 显示一个“ 的“ 的“ 定位” 定位” 。
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