NYC-Indoor-VPR: A Long-Term Indoor Visual Place Recognition Dataset with
  Semi-Automatic Annotation
Diwei Sheng Anbang Yang John-Ross Rizzo Chen Feng
Visual Place Recognition (VPR) in indoor environments is beneficial to humans
and robots for better localization and navigation. It is challenging due to
appearance changes at various frequencies, and difficulties of obtaining ground
truth metric trajectories for training and evaluation. This paper introduces
the NYC-Indoor-VPR dataset, a unique and rich collection of over 36,000 images
compiled from 13 distinct crowded scenes in New York City taken under varying
lighting conditions with appearance changes. Each scene has multiple revisits
across a year. To establish the ground truth for VPR, we propose a
semiautomatic annotation approach that computes the positional information of
each image. Our method specifically takes pairs of videos as input and yields
matched pairs of images along with their estimated relative locations. The
accuracy of this matching is refined by human annotators, who utilize our
annotation software to correlate the selected keyframes. Finally, we present a
benchmark evaluation of several state-of-the-art VPR algorithms using our
annotated dataset, revealing its challenge and thus value for VPR research.