Gaussian mixture-sound field landmark model for robot localization applications

Abstract

This work proposes a novel Gaussian mixture-sound field landmark model for localization applications, based on the principle that sound fields produced by sources at different locations can be distinguished in terms of their statistical patterns. The experimental results indicate that two microphones are sufficient to differentiate among the patterns. The proposed method is robust against environmental noise and performs accurately in a complex environment. Moreover, it cannot only detect the non-line-of-sight locations when the direct path between the microphones and the location is blocked, but also can distinguish the locations aligned with respect to the line connecting the microphones. However, using only two microphones, these scenarios are difficult to handle by traditional direction-of-arrival or beamforming methods in microphone array research. The experiments were conducted on a quadruped robot platform with an eRobot agent using embedded Ethernet technology. Because of its high accuracy and low-cost, this method is suitable for robot localization in real environments. The experimental results also show that the proposed method with only two microphones outperforms the conventional multiple signal classification method (MUSIC) technique with six microphones at various signal-to-noise ratios.