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.

翻译：在开放世界中运行的机器人会遇到各种截然不同的环境。这种领域差异对作为导航基础任务的同时定位与地图构建提出了挑战。具体而言，基于学习的SLAM方法通常难以泛化到未见环境，阻碍了其广泛应用。本文提出了持续SLAM这一新任务，将终身SLAM的概念从单一动态环境扩展到连续部署在多个差异显著的环境中。为解决该任务，我们提出了CL-SLAM，利用双网络架构同时适应新环境并保留先前访问环境的知识。我们将CL-SLAM与基于学习及经典SLAM方法进行比较，展示了利用在线数据的优势。我们在三个不同数据集上对CL-SLAM进行了广泛评估，证明其优于多个受现有持续学习视觉里程计方法启发的基线模型。我们已将代码公开发布于http://continual-slam.cs.uni-freiburg.de。