Distributed extended Kalman filtering for acoustic simultaneous localization and environment mapping

The localization accuracy of Acoustic Simultaneous Localization and Mapping (ASLAM) often suffers from environmental noise and room reverberation. To solve this problem, an ASLAM method based on distributed extended Kalman filter is proposed. Specifically, the distance between the sound source and the robot is effectively estimated by the gating mechanism method in the case of unknown location of the sound source. Then, the Time Difference of Arrival (TDOA) between robots, the Direction of Arrival (DOA) and the distance between the sound source and the robot are presented as observation. Next, the robots' trajectories and the sound source's position are tracked by multiple extended Kalman filters. Finally, the ASLAM localization is completed by fusing the sound source and the robots' positioning information through average consensus algorithm, respectively. The proposed method can efficiently promote the ASLAM localization accuracy in complex acoustic environments. The experimental results verify the effectiveness of the proposed method.