IndoorUAV: Benchmarking Vision-Language UAV Navigation in Continuous Indoor Environments

• URL: http://arxiv.org/abs/2512.19024v1
• Date: Mon, 22 Dec 2025 04:42:35 GMT
• Title: IndoorUAV: Benchmarking Vision-Language UAV Navigation in Continuous Indoor Environments
• Authors: Xu Liu, Yu Liu, Hanshuo Qiu, Yang Qirong, Zhouhui Lian,
• Abstract summary: Vision-IndoorLanguage Navigation (VLN) enables agents to navigate in complex environments by following natural language instructions grounded in visual observations.<n> indoor UAV-based VLN remains underexplored, despite its relevance to real-world applications such as inspection, delivery, and search-and-rescue in confined spaces.<n>We introduce textbfIndoorUAV, a novel benchmark and method specifically tailored for VLN with indoor UAVs.
• Score: 21.821075450697027
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language Navigation (VLN) enables agents to navigate in complex environments by following natural language instructions grounded in visual observations. Although most existing work has focused on ground-based robots or outdoor Unmanned Aerial Vehicles (UAVs), indoor UAV-based VLN remains underexplored, despite its relevance to real-world applications such as inspection, delivery, and search-and-rescue in confined spaces. To bridge this gap, we introduce \textbf{IndoorUAV}, a novel benchmark and method specifically tailored for VLN with indoor UAVs. We begin by curating over 1,000 diverse and structurally rich 3D indoor scenes from the Habitat simulator. Within these environments, we simulate realistic UAV flight dynamics to collect diverse 3D navigation trajectories manually, further enriched through data augmentation techniques. Furthermore, we design an automated annotation pipeline to generate natural language instructions of varying granularity for each trajectory. This process yields over 16,000 high-quality trajectories, comprising the \textbf{IndoorUAV-VLN} subset, which focuses on long-horizon VLN. To support short-horizon planning, we segment long trajectories into sub-trajectories by selecting semantically salient keyframes and regenerating concise instructions, forming the \textbf{IndoorUAV-VLA} subset. Finally, we introduce \textbf{IndoorUAV-Agent}, a novel navigation model designed for our benchmark, leveraging task decomposition and multimodal reasoning. We hope IndoorUAV serves as a valuable resource to advance research on vision-language embodied AI in the indoor aerial navigation domain.

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