  Place recognition is an essential and challenging task in loop closing and
global localization for robotics and autonomous driving applications.
Benefiting from the recent advances in deep learning techniques, the
performance of LiDAR place recognition (LPR) has been greatly improved.
However, current deep learning-based methods suffer from two major problems:
poor generalization ability and catastrophic forgetting. In this paper, we
propose a continual contrastive learning method, named CCL, to tackle the
catastrophic forgetting problem and generally improve the robustness of LPR
approaches. Our CCL constructs a contrastive feature pool and utilizes
contrastive loss to train more transferable representations of places. When
transferred into new environments, our CCL continuously reviews the contrastive
memory bank and applies a distribution-based knowledge distillation to maintain
the retrieval ability of the past data while continually learning to recognize
new places from the new data. We thoroughly evaluate our approach on Oxford,
MulRan, and PNV datasets using three different LPR methods. The experimental
results show that our CCL consistently improves the performance of different
methods in different environments outperforming the state-of-the-art continual
learning method. The implementation of our method has been released at
https://github.com/cloudcjf/CCL.
