Hyper-Drive：面向非结构化环境中机器人的可见-短波红外高光谱成像数据集
Hyper-Drive: Visible-Short Wave Infrared Hyperspectral Imaging Datasets for Robots in Unstructured Environments
AI总结：
本文提出Hyper-Drive机器人高光谱系统，构建覆盖660–1700 nm、含500多个标注数据立方体的公路与越野地形数据集，验证HSI对地形分类和场景理解的价值。
摘要：
高光谱传感器已在遥感领域得到广泛应用；然而，必须将其改造为可在移动机器人上搭载运行的形式。在本研究中，我们提出一种首创的系统架构，结合快照式高光谱相机与点光谱仪，以从机器人平台高效生成复合数据立方体。我们的系统采集并配准覆盖可见光至短波红外（660–1700 nm）光谱的数据立方体，同时捕获由白色参考板反射的环境太阳光谱。我们采集并公开发布了一个包含500多个带标注数据立方体的大型数据集，这些数据来自公路和越野地形，并遵循ATLAS本体，以进一步推动高光谱成像（HSI）的集成，并证明其有助于地形类别可分性。我们对该数据的分析表明，HSI为从以机器人为中心的情境中增强对场景组成的理解提供了重要机遇。所有代码和数据均在线开源：https://river-lab.github.io/hyper_drive_data
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
