Nina Felicitas Heide

Fraunhofer Institute of Optronics, System Technologies and Image Exploitation

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About

Nina Felicitas Heide is a researcher at the forefront of autonomous systems and explainable artificial intelligence, with a focus on enabling safe, trustworthy machine perception in unstructured and hazardous environments. Her work bridges the gap between robotic perception and real-world deployment, particularly in the domain of autonomous construction machinery. In her 2023 paper, “Machine learning for the perception of autonomous construction machinery” (7 citations), she addresses the critical need for holistic sensing and perception capabilities that allow robotic systems to operate autonomously and safely in challenging settings. Heide also contributes to the growing field of explainable AI; her 2020 work, “A Step towards Explainable Artificial Neural Networks in Image Processing by Dataset Assessment” (4 citations), introduces the IC-ACC methodology—a novel framework for exploratory data analysis that helps researchers understand what their neural networks are learning by jointly assessing information content and accuracy. Additionally, her research on multi-sensor data fusion, “UCSR: Registration and Fusion of Cross-Source 2D and 3D Sensor Data in Unstructured Environments” (3 citations), proposes a flexible calibration framework that eliminates the need for manual measurements, enabling more accurate and robust sensor integration. With a growing citation record and a clear focus on practical, safety-critical applications, Heide is establishing herself as a key voice in the development of autonomous systems that are not only intelligent but also transparent and reliable.