夜间视觉增强：通过动态调节灯光与曝光，提升地下环境机器人视觉质量。
Active Illumination Control in Low-Light Environments using NightHawk
- 基于特征检测设计新指标，实时优化光照与曝光参数。
- 实地测试中特征检测匹配率提升47%至197%。
- 适合在低光复杂场景下运行的移动机器人使用。
地下环境如涵洞因光线昏暗且缺乏显著特征，给机器人视觉带来重大挑战。尽管机载照明可缓解此问题，却会引发镜面反射、过曝及功耗增加等新问题。本文提出 NightHawk 框架，将主动照明与曝光控制结合，以优化此类场景下的图像质量。该框架将问题建模为在线贝叶斯优化，通过一种基于特征检测的新指标量化图像效用，并作为优化器的代价函数。我们构建了事件触发的递归优化流程，并部署于一条腿式机器人，在伊利运河下方的涵洞中进行导航。实地实验表明，特征检测与匹配性能提升47%至197%，显著增强了复杂光照条件下的视觉估计可靠性。
原文摘要 · Abstract (English)
Subterranean environments such as culverts present significant challenges to robot vision due to dim lighting and lack of distinctive features. Although onboard illumination can help, it introduces issues such as specular reflections, overexposure, and increased power consumption. We propose NightHawk, a framework that combines active illumination with exposure control to optimize image quality in these settings. NightHawk formulates an online Bayesian optimization problem to determine the best light intensity and exposure-time for a given scene. We propose a novel feature detector-based metric to quantify image utility and use it as the cost function for the optimizer. We built NightHawk as an event-triggered recursive optimization pipeline and deployed it on a legged robot navigating a culvert beneath the Erie Canal. Results from field experiments demonstrate improvements in feature detection and matching by 47-197% enabling more reliable visual estimation in challenging lighting conditions.
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