  As the human, we can recognize the places across a wide range of changing
environmental conditions such as those caused by weathers, seasons, and
day-night cycles. We excavate and memorize the stable semantic structure of
different places and scenes. For example, we can recognize tree whether the
bare tree in winter or lush tree in summer. Therefore, the intrinsic features
that are corresponding to specific semantic contents and condition-invariant of
appearance changes can be employed to improve the performance of long-term
place recognition significantly.
  In this paper, we propose a novel intrinsic encoder that excavates the
condition-invariant latent space of different places under drastic appearance
changes. Our method excavates the space of intrinsic structure and semantic
information by proposed self-supervised encoder loss. Different from previous
learning based place recognition methods that need paired training data of each
place with appearance changes, we employ the weakly-supervised strategy to
utilize unpaired set-based training data of different environmental conditions.
  We conduct comprehensive experiments and show that our semi-supervised
intrinsic encoder achieves remarkable performance for place recognition under
drastic appearance changes. The proposed intrinsic encoder outperforms the
state-of-the-art image-level place recognition methods on standard benchmark
Nordland.
