  Visual odometry is a fundamental task for many applications on mobile devices
and robotic platforms. Since such applications are oftentimes not limited to
predefined target domains and learning-based vision systems are known to
generalize poorly to unseen environments, methods for continual adaptation
during inference time are of significant interest. In this work, we introduce
CoVIO for online continual learning of visual-inertial odometry. CoVIO
effectively adapts to new domains while mitigating catastrophic forgetting by
exploiting experience replay. In particular, we propose a novel sampling
strategy to maximize image diversity in a fixed-size replay buffer that targets
the limited storage capacity of embedded devices. We further provide an
asynchronous version that decouples the odometry estimation from the network
weight update step enabling continuous inference in real time. We extensively
evaluate CoVIO on various real-world datasets demonstrating that it
successfully adapts to new domains while outperforming previous methods. The
code of our work is publicly available at
http://continual-slam.cs.uni-freiburg.de.
