  A major focus of current research on place recognition is visual localization
for autonomous driving. In this scenario, as cameras will be operating
continuously, it is realistic to expect videos as an input to visual
localization algorithms, as opposed to the single-image querying approach used
in other visual localization works. In this paper, we show that exploiting
temporal continuity in the testing sequence significantly improves visual
localization - qualitatively and quantitatively. Although intuitive, this idea
has not been fully explored in recent works. To this end, we propose two
filtering approaches to exploit the temporal smoothness of image sequences: i)
filtering on discrete domain with Hidden Markov Model, and ii) filtering on
continuous domain with Monte Carlo-based visual localization. Our approaches
rely on local features with an encoding technique to represent an image as a
single vector. The experimental results on synthetic and real datasets show
that our proposed methods achieve better results than state of the art (i.e.,
deep learning-based pose regression approaches) for the task on visual
localization under significant appearance change. Our synthetic dataset and
source code are made publicly available at
https://sites.google.com/view/g2d-software/home and
https://github.com/dadung/Visual-Localization-Filtering.
