AcousticFusion: Fusing Sound Source Localization to Visual SLAM in Dynamic Environments
This addresses the problem of computational inefficiency in dynamic SLAM for mobile robots, though it is an incremental improvement by integrating audio cues.
The paper tackles the challenge of dynamic objects in SLAM by fusing sound source localization with RGB-D images to remove dynamic obstacles, achieving stable self-localization with minimal computational resources.
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.