Location and Orientation Detection of Mobile Robots Using Sound Field Features under Complex Environments

Abstract

In this paper, the feasibility of utilizing sound for robot's pose detection is investigated, and a novel and robust robot location and orientation detection method based on sound field features for noisy environment is proposed. Unlike traditional methods, the proposed method does not explicitly consider the characteristic of direct path from sound source to microphones, nor attempt to suppress the effect of reverberations and noise signals. Instead, it utilizes the sound field features of a robot at different location and orientation in a normal environment. The sound field feature is captured by using a probability distribution estimation method called Gaussian mixture model (GMM). The experimental results show that this method can detect robot's location and orientation under both line-of-sight and non-line-of-sight conditions using only two microphones and is robust to environmental noise. Moreover, it can also solve the microphones' mismatch problem and can be applied to both near-field and far-field conditions. Since this method can provide global location and orientation detection, it is suitable to fuse with other localization methods to provide initial conditions for reduction of the search effort, or provide the compensation for localizing certain locations that cannot be detected using other localization methods