CityNav: Language-Goal Aerial Navigation Dataset with Geographic Information
<|reference_start|>CityNav: Language-Goal Aerial Navigation Dataset with Geographic Information: Vision-and-language navigation (VLN) aims to guide autonomous agents through real-world environments by integrating visual and linguistic cues. Despite notable advancements in ground-level navigation, the exploration of aerial navigation using these modalities remains limited. This gap primarily arises from a lack of suitable resources for real-world, city-scale aerial navigation studies. To remedy this gap, we introduce CityNav, a novel dataset explicitly designed for language-guided aerial navigation in photorealistic 3D environments of real cities. CityNav comprises 32k natural language descriptions paired with human demonstration trajectories, collected via a newly developed web-based 3D simulator. Each description identifies a navigation goal, utilizing the names and locations of landmarks within actual cities. As an initial step toward addressing this challenge, we provide baseline models of navigation agents that incorporate an internal 2D spatial map representing landmarks referenced in the descriptions. We have benchmarked the latest aerial navigation methods alongside our proposed baseline model on the CityNav dataset. The findings are revealing: (i) our aerial agent model trained on human demonstration trajectories, outperform those trained on shortest path trajectories by a large margin; (ii) incorporating 2D spatial map information markedly and robustly enhances navigation performance at a city scale; (iii) despite the use of map information, our challenging CityNav dataset reveals a persistent performance gap between our baseline models and human performance. To foster further research in aerial VLN, we have made the dataset and code available at https://water-cookie.github.io/city-nav-proj/<|reference_end|>
@article{lee2024citynav:,
    title={CityNav: Language-Goal Aerial Navigation Dataset with Geographic
  Information},
    author={Jungdae Lee, Taiki Miyanishi, Shuhei Kurita, Koya Sakamoto, Daichi
  Azuma, Yutaka Matsuo, Nakamasa Inoue},
    journal={arXiv preprint arXiv:2406.14240},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14240},
    primaryClass={cs.CV cs.AI}
}