  Visual place recognition (VPR) is a key issue for robotics and autonomous
systems. For the trade-off between time and performance, most of methods use
the coarse-to-fine hierarchical architecture, which consists of retrieving
top-N candidates using global features, and re-ranking top-N with local
features. However, since the two types of features are usually processed
independently, re-ranking may harm global retrieval, termed re-ranking
confusion. Moreover, re-ranking is limited by global retrieval. In this paper,
we propose a tightly coupled learning (TCL) strategy to train triplet models.
Different from original triplet learning (OTL) strategy, it combines global and
local descriptors for joint optimization. In addition, a bidirectional search
dynamic time warping (BS-DTW) algorithm is also proposed to mine locally
spatial information tailored to VPR in re-ranking. The experimental results on
public benchmarks show that the models using TCL outperform the models using
OTL, and TCL can be used as a general strategy to improve performance for
weakly supervised ranking tasks. Further, our lightweight unified model is
better than several state-of-the-art methods and has over an order of magnitude
of computational efficiency to meet the real-time requirements of robots.
