用移动机器人采集多波段高光谱图像，实现地形反射率校准与环境光照自适应分析。
Field Calibration of Hyperspectral Cameras for Terrain Inference
- 基于移动平台采集多波段高光谱图像并进行光照条件下的反射率校准
- 可准确计算植被健康指数与土壤含水量，精度依赖于光照补偿机制
- 适合自动驾驶、智能农业等需精准地形感知的场景
同一类地表（如不同含水率的土壤）会直接影响车辆通行能力，而传统RGB视觉系统难以区分此类差异。通过扩展至近红外波段的光谱信息，可有效支持地表类别内识别。然而，光谱分析准确性高度依赖环境光照条件。本文提出一种系统架构，用于从移动机器人上采集并配准多波段高光谱图像，并开发了在变化光照条件下对相机进行反射率校准的方法。为验证系统实用性，我们构建了名为HYPER DRIVE的系统，成功实现了在移动平台上计算植被健康指数（如NDVI）和土壤水分含量（如SMC），显著提升了复杂光照下地形推断的可靠性。
原文摘要 · Abstract (English)
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.
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