  Localization in topological maps is essential for image-based navigation
using an RGB camera. Localization using only one camera can be challenging in
medium-to-large-sized environments because similar-looking images are often
observed repeatedly, especially in indoor environments. To overcome this issue,
we propose a learning-based localization method that simultaneously utilizes
the spatial consistency from topological maps and the temporal consistency from
time-series images captured by the robot. Our method combines a convolutional
neural network (CNN) to embed image features and a recurrent-type graph neural
network to perform accurate localization. When training our model, it is
difficult to obtain the ground truth pose of the robot when capturing images in
real-world environments. Hence, we propose a sim2real transfer approach with
semi-supervised learning that leverages simulator images with the ground truth
pose in addition to real images. We evaluated our method quantitatively and
qualitatively and compared it with several state-of-the-art baselines. The
proposed method outperformed the baselines in environments where the map
contained similar images. Moreover, we evaluated an image-based navigation
system incorporating our localization method and confirmed that navigation
accuracy significantly improved in the simulator and real environments when
compared with the other baseline methods.
