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arXiv · 2511.17497

HALO: High-Altitude Language-Conditioned Monocular Aerial Exploration and Navigation

Yuezhan Tao·Dexter Ong·Fernando Cladera·Jason Hughes·Camillo J. Taylor·Pratik Chaudhari·Vijay Kumar
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Abstract

We demonstrate real-time high-altitude aerial metric-semantic mapping and exploration using a monocular camera paired with a global positioning system (GPS) and an inertial measurement unit (IMU). Our system, named HALO, addresses two key challenges: (i) real-time dense 3D reconstruction using vision at large distances, and (ii) mapping and exploration of large-scale outdoor environments with accurate scene geometry and semantics. We demonstrate that HALO can plan informative paths that exploit this information to complete missions with multiple tasks specified in natural language. In simulation-based evaluation across large-scale environments of size up to 78,000 sq. m., HALO consistently completes tasks with less exploration time and achieves up to 68% higher competitive ratio in terms of the distance traveled compared to the state-of-the-art semantic exploration baseline. We use real-world experiments on a custom quadrotor platform to demonstrate that (i) all modules can run onboard the robot, and that (ii) in diverse environments HALO can support effective autonomous execution of missions covering up to 24,600 sq. m. area at an altitude of 40 m. Experiment videos and more details can be found on our project page: https://tyuezhan.github.io/halo/.

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Yuezhan Tao, Dexter Ong, Fernando Cladera, Jason Hughes, Camillo J. Taylor, Pratik Chaudhari, Vijay Kumar. 2025-11-21. HALO: High-Altitude Language-Conditioned Monocular Aerial Exploration and Navigation. https://arxiv.org/abs/2511.17497

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