arXiv:2610. 41030v1 Announce Type: new Abstract: Robot perception is increasingly data driven, yet field data is slow, costly, and sometimes disruptive to collect.
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NavGen introduces a text-to-video data generation pipeline that creates about 400K vision‑language navigation episodes for both indoor and outdoor scenes, using high‑fidelity visual generative models. The approach includes a style‑diversification method to scale up rare, hard‑to‑collect data. Models trained on NavGen data outperform those trained on existing UAV navigation datasets and achieve a 75% success rate in real‑world flying experiments.
The paper explores a synthetic-first training approach for detecting drones in medium- and long-wave infrared imagery, combining synthetic scene generation with fine-tuning on real data. It demonstrates that synthetic data can establish initial object representations, but real infrared data is crucial to close domain gaps and improve reliability. The study finds that aligning datasets has a greater impact on performance than increasing model size, and that semantic alignment in feature space is the strongest predictor of success, with radiometric factors like entropy and dynamic range also contributing.
The paper introduces the Wide-area Spatio-temporal Scene Understanding (WSTU) problem, which demands simultaneous wide-area coverage, per-target resolution, and temporal continuity—capabilities lacking in existing datasets. To address this, the authors present HARD, an ultra‑high‑resolution (12768×9564) UAV dataset annotated for object detection, multi‑object tracking, and scene‑level visual question answering. They also propose a latency‑aware metric, streaming‑HOTA (s‑HOTA), and show through baseline experiments that high resolution and processing latency significantly impact detection, tracking, and VQA performance, revealing gaps in current methods for WSTU.
arXiv:2610. 29845v1 Announce Type: cross Abstract: Autonomous mobile robots operating in dense crowds and cluttered sites require $360^\circ$ perception to react to surrounding hazards.
AerialDojo-200K is a large-scale benchmark suite for open-world aerial object-goal search, featuring 42 simulation scenes across four families and 21 types, including urban, natural, infrastructure, and disaster environments. The dataset contains 205,732 task instances—over 100K semantic-goal and over 100K image-goal tasks—each with a collision-free reference trajectory and multi-view video recordings. A unified evaluation framework splits scenes into 21 in-distribution and 21 out-of-distribution sets, and preliminary tests on multimodal large language models show significant room for improvement in general-purpose aerial agents.
arXiv:2606. 29783v1 Announce Type: cross Abstract: Vision-based aerial tracking is critical in GPS-denied environments.