Active Illumination Control in Low-Light Environments using NightHawk
This addresses the challenge of reliable visual estimation for robots in dim, featureless settings like culverts, representing an incremental advance in active illumination control.
The paper tackled the problem of poor robot vision in low-light subterranean environments by developing NightHawk, a framework that actively controls illumination and exposure to optimize image quality, resulting in improvements of 47-197% in feature detection and matching.
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.