Material Mapping in Unknown Environments using Tapping Sound
This work addresses material mapping for mobile robots in unknown environments, with potential applications in search and rescue, but it is incremental as it combines existing SLAM and sound-based methods.
The paper tackles the problem of enabling a mobile robot to autonomously explore unknown environments and identify materials by integrating SLAM with sound-based classification using a tapping mechanism, demonstrating that the system can create useful material maps in such scenarios.
In this paper, we propose an autonomous exploration and a tapping mechanism-based material mapping system for a mobile robot in unknown environments. The goal of the proposed system is to integrate simultaneous localization and mapping (SLAM) modules and sound-based material classification to enable a mobile robot to explore an unknown environment autonomously and at the same time identify the various objects and materials in the environment. This creates a material map that localizes the various materials in the environment which has potential applications for search and rescue scenarios. A tapping mechanism and tapping audio signal processing based on machine learning techniques are exploited for a robot to identify the objects and materials. We demonstrate the proposed system through experiments using a mobile robot platform installed with Velodyne LiDAR, a linear solenoid, and microphones in an exploration-like scenario with various materials. Experiment results demonstrate that the proposed system can create useful material maps in unknown environments.