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.

翻译：本文提出了一种支持机器人定位的新信息源：材料成分。该方法补充了文献中已有的视觉、结构和语义线索。然而，其独特优势在于能够通过拉曼光谱仪区分结构、视觉或类别相似的物体（如不同的门）。此类设备通过探测材料分子间的键合来识别其材质。与质谱法等类似传感器不同，拉曼光谱仪在探测过程中不会损坏材料或环境。除首次提出基于材料的定位算法外，本文还通过提供拉曼光谱仪的Gazebo插件、材料感知演示，以及首个带有基准测试的基于材料定位数据集，为该领域的未来发展提供支持。该基准测试表明，所提技术相较于现有最优定位方法有显著提升，相较于领先基线模型实现了16%的定位精度提升。