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:在开放世界运行的机器人遇到不同的环境,环境可能大不相同。 这个域间差距也给同声定位和绘图(SLAM)带来挑战,因为这是导航的基本任务之一。 特别是,众所周知,以学习为基础的SLAM方法向一般采用这些方法的无形环境普遍不甚普遍。 在这项工作中,我们引入了将终身SLAM概念从单一动态变化的环境扩大到在若干截然不同的环境中相继部署的新任务。 为了应对这项任务,我们提议CL-SLAM利用一个双网络架构,既适应新环境,保留以前访问过的环境的知识。我们将CL-SLAM与基于学习的和传统的SLAM方法进行比较,并展示利用在线数据的好处。我们广泛评价了三个不同的数据集,并表明它超越了现有不断学习的视觉测量方法所启发的若干基线。我们将在http://continual-slam.cs.uni-freiburg.de上公开提供我们的工作代码。</s>