RaSpectLoc: RAman SPECTroscopy-dependent robot LOCalisation

TL;DR

This paper introduces a novel robot localisation method using Raman spectroscopy to identify material composition, enhancing differentiation of similar objects and achieving 16% higher accuracy than existing techniques.

Contribution

It presents the first material-based localisation algorithm, a Gazebo plugin for Raman spectrometers, and a new benchmark dataset for evaluating material-based localisation methods.

Findings

Achieves 16% more accurate localisation than current state-of-the-art methods.

Provides the first dataset and benchmarks for material-based robot localisation.

Demonstrates the effectiveness of Raman spectroscopy in differentiating similar objects.

Abstract

This paper presents a new information source for supporting robot localisation: material composition. The proposed method complements the existing visual, structural, and semantic cues utilized in the literature. However, it has a distinct advantage in its ability to differentiate structurally, visually or categorically similar objects such as different doors, by using Raman spectrometers. Such devices can identify the material of objects it probes through the bonds between the material's molecules. Unlike similar sensors, such as mass spectroscopy, it does so without damaging the material or environment. In addition to introducing the first material-based localisation algorithm, this paper supports the future growth of the field by presenting a gazebo plugin for Raman spectrometers, material sensing demonstrations, as well as the first-ever localisation data-set with benchmarks for material-based localisation. This benchmarking shows that the proposed technique results in a significant improvement over current state-of-the-art localisation techniques, achieving 16\% more accurate localisation than the leading baseline.