Esra Guclu

University of Bonn, Eskişehir Osmangazi University

Papers

Total Citations

H-Index

About

Esra Guclu is at the forefront of agricultural robotics and autonomous system safety, pioneering methods that bridge 3D scene understanding with real-world robotic deployment. Her most impactful work, **PAg-NeRF** (30 citations), introduces a novel Neural Radiance Field (NeRF) framework that delivers fast, end-to-end panoptic 3D representations specifically tailored for agricultural environments. This innovation enables robots to achieve precise, holistic scene understanding—simultaneously identifying objects, their semantics, and their 3D geometry—which is critical for autonomous monitoring and intervention tasks like fruit picking or plant inspection. Complementing this, Guclu has developed an **Online Distance Tracker** for robotic safety verification, providing an efficient method to compute minimum distances between a robot and its surroundings. This tool directly addresses the fundamental challenge of collision avoidance, ensuring that autonomous systems operate safely in dynamic, unstructured environments. Through these contributions, Guclu is not only advancing the capabilities of agricultural robots but also establishing robust safety frameworks essential for their real-world adoption. Her work represents a vital step toward reliable, intelligent automation in the field.