Gaussian mixture-sound field landmark model for robot localization

Abstract

This investigation proposes a robust robot localization system. The system contains a novel Gaussian Mixture-Sound Field Landmark Model (GM-SFLM) and can localize the robot accurately in noisy environments. Moreover, the proposed method depends nothing on the geometry relation between source locations and two microphones; it is able to cover both near-field and far-field problems. With this proposed GM-SFLM, we can localize robot in 2-dimentional indoor environments. Furthermore, we realize the GM-SFLM into a quadruped robot system composed of an eRobot and a robot agent by using embedded Ethernet technology. The experiment demonstrates that when the robot is completely non-line-of-sight, this system still provides high detection accuracy. Additionally, the proposed method has advantages of high accuracy, low-cost, easy to implement and environmental adaptation.