论文标题
持续的大满贯：超越终身同时定位和通过持续学习映射
Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping Through Continual Learning
论文作者
论文摘要
在开放世界中运行的机器人会遇到各种不同的环境，这些环境可能彼此之间有很大的不同。该域差距也对同时本地化和映射（SLAM）构成了挑战，它是导航的基本任务之一。尤其是，已知基于学习的大满贯方法概括为无法看到的环境阻碍其一般采用。在这项工作中，我们介绍了连续猛击的新任务，即从单个动态变化的环境到几个截然不同的环境中的顺序部署，将终身猛烈抨击的概念扩展到了。为了解决这一任务，我们提出了CL-SLAM利用双网络体系结构来适应新环境，并保留有关以前访问的环境的知识。我们将CL-SLAM与基于学习的和经典的大满贯方法进行比较，并显示了利用在线数据的优势。我们在三个不同的数据集上广泛评估CL-SLAM，并证明它的表现优于几个受现有基于基于学习的视觉光学方法的基准。我们在http://continual-slam.cs.uni-freiburg.de上公开提供工作代码。
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.
