基于声学惯性测量的室内无人机定位与跟踪
Indoor Drone Localization and Tracking Based on Acoustic Inertial Measurement
AI总结：
提出声学惯性测量（AIM）技术，利用无人机声学特性结合卡尔曼滤波和IQR规则，在无GPS和非视距环境下实现高精度室内定位与跟踪，误差低于UWB系统且支持任意空间布局。
摘要：
我们提出了声学惯性测量（AIM），一种用于室内无人机定位与跟踪的独特技术。室内无人机定位与跟踪可以说是一个至关重要但尚未解决的挑战：在无GPS环境中，现有方法适用性有限，特别是在非视距（NLoS）情况下，需要大量环境基础设施，或要求对无人机进行大量软硬件更改。相比之下，AIM利用无人机的声学特性来估计其位置并推导其运动，即使在NLoS设置中也是如此。我们使用专用的卡尔曼滤波器和四分位距规则（IQR）来控制位置估计误差，并证明AIM可以支持任意范围和布局的室内空间。我们使用现成的麦克风阵列实现了AIM，并在各种设置下评估了其与商业无人机的性能。结果表明，在复杂的10m×10m室内场景中，AIM的平均定位误差比基于UWB的商业系统低46%，而由于NLoS情况，最先进的红外系统甚至无法工作。当部署分布式麦克风阵列时，平均误差可降低至20m范围内的0.5m以下，甚至支持任意范围和布局的空间。
英文摘要：
We present Acoustic Inertial Measurement (AIM), a one-of-a-kind technique for indoor drone localization and tracking. Indoor drone localization and tracking are arguably a crucial, yet unsolved challenge: in GPS-denied environments, existing approaches enjoy limited applicability, especially in Non-Line of Sight (NLoS), require extensive environment instrumentation, or demand considerable hardware/software changes on drones. In contrast, AIM exploits the acoustic characteristics of the drones to estimate their location and derive their motion, even in NLoS settings. We tame location estimation errors using a dedicated Kalman filter and the Interquartile Range rule (IQR) and demonstrate that AIM can support indoor spaces with arbitrary ranges and layouts. We implement AIM using an off-the-shelf microphone array and evaluate its performance with a commercial drone under varied settings. Results indicate that the mean localization error of AIM is 46% lower than that of commercial UWB-based systems in a complex 10m\times10m indoor scenario, where state-of-the-art infrared systems would not even work because of NLoS situations. When distributed microphone arrays are deployed, the mean error can be reduced to less than 0.5m in a 20m range, and even support spaces with arbitrary ranges and layouts.
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