Visual Place Recognition aims at recognizing previously visited places by
relying on visual clues, and it is used in robotics applications for SLAM and
localization. Since typically a mobile robot has access to a continuous stream
of frames, this task is naturally cast as a sequence-to-sequence localization
problem. Nevertheless, obtaining sequences of labelled data is much more
expensive than collecting isolated images, which can be done in an automated
way with little supervision. As a mitigation to this problem, we propose a
novel Joint Image and Sequence Training protocol (JIST) that leverages large
uncurated sets of images through a multi-task learning framework. With JIST we
also introduce SeqGeM, an aggregation layer that revisits the popular GeM
pooling to produce a single robust and compact embedding from a sequence of
single-frame embeddings. We show that our model is able to outperform previous
state of the art while being faster, using 8 times smaller descriptors, having
a lighter architecture and allowing to process sequences of various lengths.
Code is available at this https URL

通过利用 JIST 和 SeqGeM 提出的多任务学习框架和序列嵌入池化方法，本研究旨在通过视觉线索识别之前访问过的地点，以解决标记数据获取困难的问题，并在速度、嵌入大小、架构轻量化和处理多长度序列等方面优于现有技术。