|Title||MOM: Microphone based 3D Orientation Measurement|
|Publication Type||Conference Paper|
|Year of Publication||2022|
|Authors||Z Gao, A Li, D Li, J Liu, J Xiong, Y Wang, B Li, and Y Chen|
|Conference Name||Proceedings 21st Acm/Ieee International Conference on Information Processing in Sensor Networks, Ipsn 2022|
While a tremendous amount of effort has been devoted to localization, the orientation of a device, especially in 3D space, is seldom explored. Although many sensor-based methods utilizing gyro-scope, accelerometer, and magnetometer have been proposed to measure 3D orientation, these methods generally suffer from high cumulative errors and performance degradation when the device is moving. In this paper, we present MOM, the first microphone-based system that estimates the 3D orientation of a device. The key idea of MOM is to employ free sound sources in our surrounding environment as anchors. The prior knowledge of these sound sources, including the signal waveform and the locations of the sound sources, is not required to be known. In particular, we propose an angle-of-arrival (AoA) extraction algorithm that compares fine-grained time delays over microphones at a low computational cost. We implement our system on three platforms including a 6-microphone array Seeed Studio ReSpeaker, a commodity earphone Sennheiser AMBEO smart headset and a commodity smartphone Google Pixel 4. Extensive experiments show that MOM can achieve significantly higher accuracy compared with status quo approaches and is robust against cumulative errors. We apply MOM to two real-life applications, i.e., head tracking and 3D reconstruction, to demonstrate the applicability and generality of MOM in practice.