  Visual place recognition is the task of recognizing same places of query
images in a set of database images, despite potential condition changes due to
time of day, weather or seasons. It is important for loop closure detection in
SLAM and candidate selection for global localization. Many approaches in the
literature perform computationally inefficient full image comparisons between
queries and all database images. There is still a lack of suited methods for
efficient place recognition that allow a fast, sparse comparison of only the
most promising image pairs without any loss in performance. While this is
partially given by ANN-based methods, they trade speed for precision and
additional memory consumption, and many cannot find arbitrary numbers of
matching database images in case of loops in the database. In this paper, we
propose a novel fast sequence-based method for efficient place recognition that
can be applied online. It uses relocalization to recover from sequence losses,
and exploits usually available but often unused intra-database similarities for
a potential detection of all matching database images for each query in case of
loops or stops in the database. We performed extensive experimental evaluations
over five datasets and 21 sequence combinations, and show that our method
outperforms two state-of-the-art approaches and even full image comparisons in
many cases, while providing a good tradeoff between performance and percentage
of evaluated image pairs. Source code for Matlab will be provided with
publication of this paper.
