Visual place recognition (VPR) - the act of recognizing a familiar visual
place - becomes difficult when there is extreme environmental appearance change
or viewpoint change. Particularly challenging is the scenario where both
phenomena occur simultaneously, such as when returning for the first time along
a road at night that was previously traversed during the day in the opposite
direction. While such problems can be solved with panoramic sensors, humans
solve this problem regularly with limited field of view vision and without
needing to constantly turn around. In this paper, we present a new depth- and
temporal-aware visual place recognition system that solves the opposing
viewpoint, extreme appearance-change visual place recognition problem. Our
system performs sequence-to-single matching by extracting depth-filtered
keypoints using a state-of-the-art depth estimation pipeline, constructing a
keypoint sequence over multiple frames from the reference dataset, and
comparing those keypoints to those in a single query image. We evaluate the
system on a challenging benchmark dataset and show that it consistently
outperforms state-of-the-art techniques. We also develop a range of diagnostic
simulation experiments that characterize the contribution of depth-filtered
keypoint sequences with respect to key domain parameters including degree of
appearance change and camera motion.

本研究提出了一种新的深度 - 时间感知视觉地点识别系统，通过提取深度过滤关键点序列，对与单个查询图像中的关键点进行比较，解决了对立的视角和极端外观变化视觉地点识别问题，实验证明在多个基准数据集上表现优异，还进行了一系列模拟实验，以表征深度过滤关键点序列在关键域参数方面的贡献。