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About

Karin de Langis is a robotics and computer vision researcher whose work sits at the intersection of autonomous underwater systems and human-robot collaboration. Her research focuses primarily on developing robust perception capabilities for autonomous underwater vehicles (AUVs), with a particular emphasis on diver detection, multi-diver tracking, and real-time visual processing in challenging aquatic environments. De Langis has made notable contributions to the field through her rigorous analysis of deep neural network architectures for diver detection, producing a landmark dataset of approximately 105,000 annotated images that has helped advance benchmarking in underwater robotics. Her most-cited work (2020, 11 citations) systematically evaluates deep object detectors to support diver-following behaviors, while subsequent research addresses the consistency challenges of deploying these models on real robotic platforms. She has also extended her expertise beyond underwater settings, contributing to semantically-aware obstacle avoidance strategies for mobile robots operating in unstructured environments. A recurring theme in her research is practical deployability — ensuring that perception systems are not only accurate but reliable and real-time capable aboard autonomous platforms. Her work on multi-diver tracking and re-identification further demonstrates her commitment to enabling meaningful, safe collaboration between humans and autonomous systems in demanding real-world conditions.