# Material Mapping in Unknown Environments using Tapping Sound

## TL;DR

This paper presents an autonomous robot system that uses a tapping mechanism and sound analysis to map materials in unknown environments, aiding applications like search and rescue.

## Contribution

It introduces a novel integration of SLAM and sound-based material classification using machine learning for autonomous material mapping.

## Key findings

- The system successfully creates material maps in unknown environments.
- Sound-based classification achieves high accuracy in identifying materials.
- The approach is effective for search and rescue scenarios.

## Abstract

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.