Towards Informative Path Planning for Acoustic SLAM

Abstract

Acoustic scene mapping is a challenging task as microphone arrays can often localize sound sources only in terms of their directions. Spatial diversity can be exploited constructively to infer source-sensor range when using microphone arrays installed on moving platforms, such as robots. As the absolute location of a moving robot is often unknown in practice, Acoustic Simultaneous Localization And Mapping (a-SLAM) is required in order to localize the moving robot’s positions and jointly map the sound sources. Using a novel a-SLAM approach, this paper investigates the impact of the choice of robot paths on source mapping accuracy. Simulation results demonstrate that a-SLAM performance can be improved by informatively planning robot paths.