Acoustic Simultaneous Localization and Mapping
(a-SLAM) jointly localizes the trajectory of a microphone array
installed on a moving platform, whilst estimating the acoustic
map of surrounding sound sources, such as human speakers.
Whilst traditional approaches for SLAM in the vision and optical
research literature rely on the assumption that the surrounding
map features are static, in the acoustic case the positions of
talkers are usually time-varying due to head rotations and body
movements. This paper demonstrates that tracking of moving
sources can be incorporated in a-SLAM by modelling the acoustic
map as a Random Finite Set (RFS) of multiple sources and
explicitly imposing models of the source dynamics. The proposed
approach is verified and its performance evaluated for realistic
simulated data.