  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.
