  Map representation learned by expert demonstrations has shown promising
research value. However, recent advancements in the visual navigation field
face challenges due to the lack of human datasets in the real world for
efficient supervised representation learning of the 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 RGB
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. Dataset is available at DOI: 10.5281/zenodo.10608067.
