Visual Place Recognition (VPR) is the task of matching current visual imagery
from a camera to images stored in a reference map of the environment. While
initial VPR systems used simple direct image methods or hand-crafted visual
features, recent work has focused on learning more powerful visual features and
further improving performance through either some form of sequential matcher /
filter or a hierarchical matching process. In both cases the performance of the
initial single-image based system is still far from perfect, putting
significant pressure on the sequence matching or (in the case of hierarchical
systems) pose refinement stages. In this paper we present a novel hybrid system
that creates a high performance initial match hypothesis generator using short
learnt sequential descriptors, which enable selective control sequential score
aggregation using single image learnt descriptors. Sequential descriptors are
generated using a temporal convolutional network dubbed SeqNet, encoding short
image sequences using 1-D convolutions, which are then matched against the
corresponding temporal descriptors from the reference dataset to provide an
ordered list of place match hypotheses. We then perform selective sequential
score aggregation using shortlisted single image learnt descriptors from a
separate pipeline to produce an overall place match hypothesis. Comprehensive
experiments on challenging benchmark datasets demonstrate the proposed method
outperforming recent state-of-the-art methods using the same amount of
sequential information. Source code and supplementary material can be found at
this https URL

本文提出一种新型混合系统，使用 SeqNet 生成临时描述符，并结合单图片学习描述符进行选择性控制序列得分汇总，以创建高性能初始匹配假设生成器，进而在挑战性基准数据集上达到超越现有最先进方法的效果。