Distributed Extended Kalman Particle Filter for Acoustic SLAM

In complex environments, the background noise and room reverberation seriously degrade the performance of acoustic simultaneous localization and mapping (ASLAM) systems. To remedy the problem, an ASLAM method based on distributed extended Kalman particle filter (DEKF-PF) is proposed. First, the fixed-position microphones are considered as nodes in a distributed microphone array. The direction of arrival (DOA) between the robot and the sound source, and the time difference of arrival (TDOA) between the robots are simultaneously used as observations for the DEKF-PF. Then, the particle filter (PF) integrated with extended Kalman filter (EKF) is used to perform local and joint state updates of the SLAM system. The distributed state update mechanism enables the sharing and fusion of state information among nodes, which can effectively improve the overall estimation accuracy of the system and suppress the accumulated error. Next, the average consensus algorithm efficiently fuses the local and joint state information to obtain the sound source positions and robot trajectories. Finally, the ASLAM with the DEKF-PF is performed based on distributed microphone arrays. The proposed method combines the nonlinear modeling capability of the PF and the linear estimation advantage of the EKF, improving the localization accuracy of ASLAM systems. Simulation and real-world experimental results verify the effectiveness of the proposed method.