  How many times does a human have to drive through the same area to become
familiar with it? To begin with, we might first build a mental model of our
surroundings. Upon revisiting this area, we can use this model to extrapolate
to new unseen locations and imagine their appearance. Based on this, we propose
an approach where an agent is capable of modelling new environments after a
single visitation. To this end, we introduce "Deep Imagination", a combination
of classical Visual-based Monte Carlo Localisation and deep learning. By making
use of a feature embedded 3D map, the system can "imagine" the view from any
novel location. These "imagined" views are contrasted with the current
observation in order to estimate the agent's current location. In order to
build the embedded map, we train a deep Siamese Fully Convolutional U-Net to
perform dense feature extraction. By training these features to be generic, no
additional training or fine tuning is required to adapt to new environments.
Our results demonstrate the generality and transfer capability of our learnt
dense features by training and evaluating on multiple datasets. Additionally,
we include several visualizations of the feature representations and resulting
3D maps, as well as their application to localisation.
