Hyperspectral sensors have enjoyed widespread use in the realm of remote sensing; however, they must be adapted to a format in which they can be operated onboard mobile robots. In this work, we introduce a first-of-its-kind system architecture with snapshot hyperspectral cameras and point spectrometers to efficiently generate composite datacubes from a robotic base. Our system collects and registers datacubes spanning the visible to shortwave infrared (660-1700 nm) spectrum while simultaneously capturing the ambient solar spectrum reflected off a white reference tile. We collect and disseminate a large dataset of more than 500 labeled datacubes from on-road and off-road terrain compliant with the ATLAS ontology to further the integration and demonstration of hyperspectral imaging (HSI) as beneficial in terrain class separability. Our analysis of this data demonstrates that HSI is a significant opportunity to increase understanding of scene composition from a robot-centric context. All code and data are open source online: https://river-lab.github.io/hyper_drive_data

翻译：高光谱传感器在遥感领域已得到广泛应用，然而，它们必须适配为可在移动机器人上操作的格式。在本工作中，我们首次提出一种采用快照式高光谱相机和点光谱仪的系统架构，能够从机器人基座上高效生成复合数据立方体。我们的系统可采集并配准覆盖可见光至短波红外（660-1700纳米）光谱范围的数据立方体，同时捕获从白色参考瓦片反射的环境太阳光谱。我们收集并发布了一个包含超过500个标注数据立方体的大型数据集，这些数据来自符合ATLAS本体论的道路和非道路地形，以进一步推动高光谱成像（HSI）在地形类别可分离性方面的整合与验证。我们的数据分析表明，HSI为从机器人中心视角增强对场景构成的理解提供了重要机遇。所有代码和数据均已开源发布于：https://river-lab.github.io/hyper_drive_data