In distributed microphone arrays, each microphone is connected to a separate recording device, which requires compensation for their different clock shifts. In this paper, we propose a new type of distributed arrays, in which the microphones are moving. This mobility introduces innovation that can be used to estimate the positions of both the sound sources and the microphones, as well as compensate for the clock shifts. Estimation is done using extended Kalman filters with heuristics that improve the convergence.We discuss the proposed method in the context of mobile robotics: each microphone is attached to a controllable mobile robot. In this scenario, the array can be used to localize soundemitting objects in the robot's environment, while providing information about robots' positions. Results show that the error of estimating robots' positions is as low as 5 cm with the source source localization error of 6 mm and the clock synchronization error of 178 μs, for the case of 9 microphones and 5 sound sources.

Vision-based methods are very popular for simultaneous localization and environment mapping (SLAM). One can imagine that exploiting the natural acoustic landscape of the robot's environment can prove to be a useful alternative to vision SLAM. Visual SLAM depends on matching local features between images, whereas distributed acoustic SLAM is based on matching acoustic events. Proposed DASLAM is based on distributed microphone arrays, where each microphone is connected to a separate, moving, controllable recording device, which requires compensation for their different clock shifts. We show that this controlled mobility is necessary to deal with underdetermined cases. Estimation is done using particle filtering.Results show that both tasks can be accomplished with good precision, even for the theoretically underdetermined cases. For example, we were able to achieve mapping error as low as 17.53 cm for sound sources with localization error of 18.61 cm and clock synchronization error of 42 μs for 2 robots and 2 sources.