AIM: Acoustic Inertial Measurement for Indoor Drone Localization and Tracking

2022-11-06

ACM International Conference on Embedded Networked Sensor Systems

AI Summary

To address the challenge of precise UAV localization in GPS-denied indoor environments—particularly under non-line-of-sight (NLoS) conditions where existing methods rely on dedicated hardware or pre-deployed infrastructure—this paper proposes a markerless, infrastructure-free acoustic-inertial fusion localization paradigm. Leveraging only the intrinsic acoustic features of an onboard sound source and a distributed microphone array, the method enables real-time, high-accuracy motion tracking even in NLoS scenarios. We introduce an IQR-enhanced extended Kalman filter to robustly suppress outliers in acoustic source localization. Experimental results demonstrate that, in complex indoor settings, our approach achieves an average positioning error 46% lower than that of commercial UWB systems. Moreover, it consistently maintains sub-meter accuracy (<0.8 m) across arbitrarily scaled and configured spaces, significantly improving NLoS robustness and deployment flexibility.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Computer Vision: Motion & Tracking

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

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.

Problem

• Indoor drone localization in GPS-denied environments
• Tracking drones in Non-Line of Sight (NLoS) conditions
• Reducing hardware/software changes for drone localization

Innovation

• Uses acoustic characteristics for drone localization
• Employs Kalman filter and IQR for error reduction
• Utilizes distributed microphone arrays for scalability