Some robotic localization methods, such as ultra wideband localization and lighthouse localization, require external localization infrastructure in order to operate. However, there are situations where this localization infrastructure does not exist in the field, such as robotic exploration tasks. Deploying low power wireless sensor networks (WSNs) as localization infrastructure can potentially solve this problem. In this work, we demonstrate the use of an OpenWSN network of miniaturized low power sensor nodes as localization infrastructure. We demonstrate a quadrotor performing laser-based relative bearing measurements of stationary wireless sensor nodes with known locations and using these measurements to localize itself. These laser-based measurements require little computation on the WSN nodes, and are compatible with state-of-the-art 2 mm × 3 mm monolithic wireless system-on-chips (SoCs). These capabilities were demonstrated on a Crazyflie quadcopter using an Extended Kalman Filter and a network of motes running the OpenWSN wireless sensor network stack. The RMS error for X positioning was 0.57 m and the error for Y positioning was 0.39 m. This is the first use of an OpenWSN sensor network to support robotic localization. Furthermore, simulations show that these same measurements could be used for localizing sensor motes with unknown locations in the future.

中文翻译：

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具有时间同步的无线传感器节点和仅方位测量的基于四旋翼的灯塔定位。

一些机器人本地化方法（例如超宽带本地化和灯塔本地化）需要外部本地化基础结构才能运行。但是，在某些情况下该本地化基础结构在现场不存在，例如机器人勘探任务。将低功率无线传感器网络（WSN）部署为本地化基础结构可以潜在地解决此问题。在这项工作中，我们演示了将微型低功耗传感器节点的OpenWSN网络用作本地化基础结构的方法。我们演示了一个四旋翼飞行器，它对已知位置的固定无线传感器节点执行基于激光的相对方位测量，并使用这些测量来定位自身。这些基于激光的测量几乎不需要在WSN节点上进行任何计算，并与最先进的2 mm×3 mm单片无线片上系统（SoC）兼容。在使用扩展卡尔曼滤波器的Crazyflie四轴飞行器和运行OpenWSN无线传感器网络堆栈的节点网络上演示了这些功能。X定位的RMS误差为0.57 m，Y定位的误差为0.39 m。这是OpenWSN传感器网络首次用于支持机器人本地化。此外，仿真表明，这些相同的测量结果可用于将来定位未知位置的传感器微粒。57 m，Y定位误差为0.39 m。这是OpenWSN传感器网络首次用于支持机器人本地化。此外，仿真表明，这些相同的测量结果可用于将来定位未知位置的传感器微粒。57 m，Y定位误差为0.39 m。这是OpenWSN传感器网络首次用于支持机器人本地化。此外，仿真显示，这些相同的测量结果可用于将来定位未知位置的传感器微粒。