用声音和惯性数据实现室内无人机高精度定位，无需复杂布设
AIM: Acoustic Inertial Measurement for Indoor Drone Localization and Tracking
- 通过麦克风阵列捕捉无人机声学特征，结合卡尔曼滤波与四分位距去噪
- 复杂室内场景下定位误差比商用UWB系统低46%，在非视距环境下仍有效
- 可扩展至任意布局空间，仅需部署分布式麦克风阵列，无需改造无人机
我们提出声学惯性测量（AIM），一种面向室内无人机定位与追踪的全新技术。在无GPS环境中，现有方法普遍存在适用性差的问题，尤其在非视距（NLoS）场景下，或需大量环境布设，或要求对无人机进行软硬件改造。相比之下，AIM利用无人机自身的声学特性估计位置并推导运动轨迹，即使在非视距条件下亦可工作。我们采用专用卡尔曼滤波器与四分位距规则（IQR）抑制定位误差。实验使用现成麦克风阵列与商用无人机，在多种环境下验证性能。结果表明，在复杂室内场景中，AIM的平均定位误差比商用超宽带（UWB）系统降低46%，而当前最先进的红外系统因受非视距影响根本无法工作。此外，通过部署分布式麦克风阵列，可将该系统扩展至任意范围与布局的室内空间，且精度不受影响。
原文摘要 · Abstract (English)
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). 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 commercial UWB-based systems in complex indoor scenarios, where state-of-the-art infrared systems would not even work because of NLoS settings. We further demonstrate that AIM can be extended to support indoor spaces with arbitrary ranges and layouts without loss of accuracy by deploying distributed microphone arrays.
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