FindAnything: Open-Vocabulary and Object-Centric Mapping for Robot Exploration in Any Environment

MCML Authors

Sotiris Papatheodorou

* Former Member

→ Group Stefan Leutenegger

* Former PI

Stefan Leutenegger

Prof. Dr.

Principal Investigator

* Former Principal Investigator

Abstract

Geometrically accurate and semantically expressive map representations have proven invaluable for robot deployment and task planning in unknown environments. Nevertheless, real-time, open-vocabulary semantic understanding of large-scale unknown environments still presents open challenges, mainly due to computational requirements. In this paper we present FindAnything, an open-world mapping framework that incorporates vision-language information into dense volumetric submaps. Thanks to the use of vision-language features, FindAnything combines pure geometric and open-vocabulary semantic information for a higher level of understanding. It proposes an efficient storage of open-vocabulary information through the aggregation of features at the object level. Pixel-wise vision-language features are aggregated based on eSAM segments, which are in turn integrated into object-centric volumetric submaps, providing a mapping from open-vocabulary queries to 3D geometry that is scalable also in terms of memory usage. We demonstrate that FindAnything performs on par with the state-of-the-art in terms of semantic accuracy while being substantially faster and more memory-efficient, allowing its deployment in large-scale environments and on resource-constrained devices, such as MAVs. We show that the real-time capabilities of FindAnything make it useful for downstream tasks, such as autonomous MAV exploration in a simulated Search and Rescue scenario.

inproceedings LBP+26

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ICRA 2026

IEEE International Conference on Robotics and Automation. Vienna, Austria, Jun 01-05, 2026.

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Authors

S. B. Laina • S. Boche • S. Papatheodorou • S. Schaefer • J. Jung • H. Oleynikova • S. Leutenegger

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Links

DOI GitHub

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Research Area

 B3 | Multimodal Perception