Jaehyung Jung — accepted AI-conference papers

2026

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

Sebastián Barbas Laina, Simon Boche, Sotiris Papatheodorou, Simon Schaefer, Jaehyung Jung, Helen Oleynikova +1

ICRA 2026poster

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…

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2026

OKVIS2-X: Open Keyframe-Based Visual-Inertial SLAM Configurable with Dense Depth or LiDAR, and GNSS

Simon Boche, Jaehyung Jung, Sebastián Barbas Laina, Stefan Leutenegger

ICRA 2026poster

To empower mobile robots with usable maps as well as highest state estimation accuracy and robustness, we present OKVIS2-X: a state-of-the-art multi-sensor Simultaneous Localization and Mapping (SLAM) system building dense volumetric occupancy maps, while scalable to large environments and operating…

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2025

Uncertainty-Aware Visual-Inertial SLAM with Volumetric Occupancy Mapping

Jaehyung Jung, Simon Boche, Sebastián Barbas Laina, Stefan Leutenegger

ICRA 2025

We propose visual-inertial simultaneous localization and mapping that tightly couples sparse reprojection errors, inertial measurement unit pre-integrals, and relative pose factors with dense volumetric occupancy mapping. Hereby depth predictions from a deep neural network are fused in a fully proba

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