  Recently, the methods based on Convolutional Neural Networks (CNNs) have
gained popularity in the field of visual place recognition (VPR). In
particular, the features from the middle layers of CNNs are more robust to
drastic appearance changes than handcrafted features and high-layer features.
Unfortunately, the holistic mid-layer features lack robustness to large
viewpoint changes. Here we split the holistic mid-layer features into local
features, and propose an adaptive dynamic time warping (DTW) algorithm to align
local features from the spatial domain while measuring the distance between two
images. This realizes viewpoint-invariant and condition-invariant place
recognition. Meanwhile, a local matching DTW (LM-DTW) algorithm is applied to
perform image sequence matching based on temporal alignment, which achieves
further improvements and ensures linear time complexity. We perform extensive
experiments on five representative VPR datasets. The results show that the
proposed method significantly improves the CNN-based methods. Moreover, our
method outperforms several state-of-the-art methods while maintaining good
run-time performance. This work provides a novel way to boost the performance
of CNN methods without any re-training for VPR. The code is available at
https://github.com/Lu-Feng/STA-VPR.
