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.
Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping Through Continual Learning
Springer Proceedings in Advanced Robotics
The International Symposium of Robotics Research ; 2022 ; Geneva, Switzerland September 25, 2022 - September 30, 2022
2023-03-08
17 pages
Article/Chapter (Book)
Electronic Resource
English
Excellence through continual improvement (ETCI)
AIAA | 1989
|NTRS | 1994
NTIS | 1994
CONTINUAL PROACTIVE LEARNING FOR AUTONOMOUS ROBOT AGENTS
European Patent Office | 2021
|