Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping Through Continual Learning

Understand

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.