  Robots operating in the open world encounter various different environments
that can substantially differ from each other. This domain gap also poses a
challenge for Simultaneous Localization and Mapping (SLAM) being one of the
fundamental tasks for navigation. In particular, learning-based SLAM methods
are known to generalize poorly to unseen environments hindering their general
adoption. In this work, we introduce the novel task of continual SLAM extending
the concept of lifelong SLAM from a single dynamically changing environment to
sequential deployments in several drastically differing environments. To
address this task, we propose CL-SLAM leveraging a dual-network architecture to
both adapt to new environments and retain knowledge with respect to previously
visited environments. We compare CL-SLAM to learning-based as well as classical
SLAM methods and show the advantages of leveraging online data. We extensively
evaluate CL-SLAM on three different datasets and demonstrate that it
outperforms several baselines inspired by existing continual learning-based
visual odometry methods. We make the code of our work publicly available at
http://continual-slam.cs.uni-freiburg.de.
