Map representations learned by expert demonstrations have shown promising research value. However, the field of visual navigation still faces challenges due to the lack of real-world human-navigation datasets that can support efficient, supervised, representation learning of environments. We present a Landmark-Aware Visual Navigation (LAVN) dataset to allow for supervised learning of human-centric exploration policies and map building. We collect RGBD observation and human point-click pairs as a human annotator explores virtual and real-world environments with the goal of full coverage exploration of the space. The human annotators also provide distinct landmark examples along each trajectory, which we intuit will simplify the task of map or graph building and localization. These human point-clicks serve as direct supervision for waypoint prediction when learning to explore in environments. Our dataset covers a wide spectrum of scenes, including rooms in indoor environments, as well as walkways outdoors. We release our dataset with detailed documentation atthis https URL(DOI:https://doi.org/10.57967/hf/2386) and a plan for long-term preservation.
View on arXiv@article{johnson2025_2402.14281,
title={ A Landmark-Aware Visual Navigation Dataset },
author={ Faith Johnson and Bryan Bo Cao and Kristin Dana and Shubham Jain and Ashwin Ashok },
journal={arXiv preprint arXiv:2402.14281},
year={ 2025 }
}