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.

As 24/7 imaging activity rises in volume and complexity and the workforce deficit grows teleradiology management of the acutely unwell patient has become increasingly important. The concept is not new: GMC registered reporters with home workstations, potentially anywhere in the world, interacting seamlessly with client RIS systems. Faster data transfer, strict information governance protocols and evidence-based imaging have enabled developments of this service. As the UKs largest teleradiology company, Medica can offer bespoke tools and pathways to enhance patient care. For major trauma cases reporters provide a'seriously injured patient' report within 10 minutes of image receipt direct to the trauma team leader, enabling prompt management of life-threatening injuries.