Raphael Hagmanns

Fraunhofer Institute of Optronics, System Technologies and Image Exploitation

Papers

Total Citations

H-Index

About

Raphael Hagmanns is a rising researcher at the forefront of autonomous systems for unstructured outdoor environments, with a particular focus on perception and semantic segmentation for heavy machinery. His work addresses a critical bottleneck in deploying deep learning-based autonomous systems: the scarcity of high-quality, domain-specific data for challenging, non-urban settings. Hagmanns is the lead creator of the GOOSE (Ground Operations for Outdoor Semantic Environments) dataset, a pioneering resource that enables robust perception in unstructured terrains, and its extension, the GOOSE-Ex dataset, which targets semantic segmentation for excavation tasks. These contributions are foundational for advancing autonomy in construction, mining, and agriculture. His most-cited paper, "The GOOSE Dataset for Perception in Unstructured Environments" (2024), has already garnered 27 citations, reflecting its immediate impact on the field. By providing open-access, annotated data for robotic platforms operating "in the wild," Hagmanns is empowering researchers and engineers to develop more resilient perception systems, bridging the gap between controlled lab environments and the messy realities of outdoor deployment.