Active Illumination Control in Low-Light Environments using NightHawk

TL;DR

NightHawk is a novel framework that optimizes active illumination and exposure settings in real-time to enhance robot vision in low-light subterranean environments, significantly improving feature detection and matching.

Contribution

It introduces an online Bayesian optimization approach with a new image utility metric, enabling adaptive illumination control for better visual perception in challenging conditions.

Findings

Feature detection and matching improved by 47-197%.

Enhanced visual estimation reliability in low-light environments.

Validated on a legged robot in a culvert setting.

Abstract

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.