  Dynamic objects in the environment, such as people and other agents, lead to
challenges for existing simultaneous localization and mapping (SLAM)
approaches. To deal with dynamic environments, computer vision researchers
usually apply some learning-based object detectors to remove these dynamic
objects. However, these object detectors are computationally too expensive for
mobile robot on-board processing. In practical applications, these objects
output noisy sounds that can be effectively detected by on-board sound source
localization. The directional information of the sound source object can be
efficiently obtained by direction of sound arrival (DoA) estimation, but depth
estimation is difficult. Therefore, in this paper, we propose a novel
audio-visual fusion approach that fuses sound source direction into the RGB-D
image and thus removes the effect of dynamic obstacles on the multi-robot SLAM
system. Experimental results of multi-robot SLAM in different dynamic
environments show that the proposed method uses very small computational
resources to obtain very stable self-localization results.
