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About

Fernando Cladera is a robotics researcher whose work sits at the intersection of autonomous aerial systems, semantic mapping, and multi-robot collaboration. He has made significant contributions to enabling robots to operate intelligently in complex, GPS-denied environments — most notably through his work on semantic SLAM, where he demonstrated large-scale autonomous flight under dense forest canopy using semantically meaningful object representations (97 citations). This foundational work underpins his broader research agenda: making robots not just spatially aware, but meaningfully aware of their surroundings. Cladera has extended these ideas into heterogeneous multi-robot systems, developing frameworks that allow air-ground robot teams to share semantic maps and coordinate autonomously (57 citations), and scaling this to multi-agent active mapping scenarios. His contributions to sensing infrastructure are equally notable, including the M3ED event camera dataset (50 citations) and the TreeScope agricultural robotics dataset (18 citations), which have provided the community with critical benchmarks for high-speed and field robotics research. His work in precision agriculture (44 citations) and indoor exploration using learned information gain (41 citations) further demonstrates his commitment to real-world deployability. With over 350 cumulative citations across recent publications, Cladera is rapidly establishing himself as a leading voice in field robotics and autonomous systems research.