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.

室内视觉地点识别对于人类和机器人的定位和导航具有益处。此论文介绍了 NYC-Indoor-VPR 数据集，它是纽约市 13 个不同拥挤场景的超过 36,000 张图片的独特且丰富的集合，这些图片采用了不同的光照条件和外观变化。为了建立视觉地点识别的基准，我们提出了一种半自动标注方法，通过计算每个图片的位置信息。最后，我们使用我们的标注数据集对几种最先进的视觉地点识别算法进行了基准评估，揭示了其挑战和对视觉地点识别研究的价值。