Intra-class terrain differences such as water content directly influence a vehicle's ability to traverse terrain, yet RGB vision systems may fail to distinguish these properties. Evaluating a terrain's spectral content beyond red-green-blue wavelengths to the near infrared spectrum provides useful information for intra-class identification. However, accurate analysis of this spectral information is highly dependent on ambient illumination. We demonstrate a system architecture to collect and register multi-wavelength, hyperspectral images from a mobile robot and describe an approach to reflectance calibrate cameras under varying illumination conditions. To showcase the practical applications of our system, HYPER DRIVE, we demonstrate the ability to calculate vegetative health indices and soil moisture content from a mobile robot platform.

翻译：同类地形内部差异（如含水量）直接影响车辆的地形通过能力，而RGB视觉系统可能无法有效识别这些特性。将地形光谱分析范围从红-绿-蓝波段扩展至近红外光谱，可为同类地形的精细识别提供关键信息。然而，对此类光谱信息的精确分析高度依赖于环境光照条件。本文提出一种用于移动机器人多波长高光谱图像采集与配准的系统架构，并阐述在变化光照条件下实现相机反射率标定的方法。为展示我们开发的HYPER DRIVE系统的实际应用价值，我们在移动机器人平台上实现了植被健康指数与土壤含水量的实时计算能力。