  Loop closure detection is an important building block that ensures the
accuracy and robustness of simultaneous localization and mapping (SLAM)
systems. Due to their generalization ability, CNN-based approaches have
received increasing attention. Although they normally benefit from training on
datasets that are diverse and reflective of the environments, new environments
often emerge after the model is deployed. It is therefore desirable to
incorporate the data newly collected during operation for incremental learning.
Nevertheless, simply finetuning the model on new data is infeasible since it
may cause the model's performance on previously learned data to degrade over
time, which is also known as the problem of catastrophic forgetting. In this
paper, we present AirLoop, a method that leverages techniques from lifelong
learning to minimize forgetting when training loop closure detection models
incrementally. We experimentally demonstrate the effectiveness of AirLoop on
TartanAir, Nordland, and RobotCar datasets. To the best of our knowledge,
AirLoop is one of the first works to achieve lifelong learning of deep loop
closure detectors.
