Simultaneous localization and mapping (SLAM) stands as one of the critical challenges in robot navigation. Recent advancements suggest that methods based on supervised learning deliver impressive performance in front-end odometry, while traditional optimization-based methods still play a vital role in the back-end for minimizing estimation drift. In this paper, we found that such decoupled paradigm can lead to only sub-optimal performance, consequently curtailing system capabilities and generalization potential. To solve this problem, we proposed a novel self-supervised learning framework, imperative SLAM (iSLAM), which fosters reciprocal correction between the front-end and back-end, thus enhancing performance without necessitating any external supervision. Specifically, we formulate a SLAM system as a bi-level optimization problem so that the two components are bidirectionally connected. As a result, the front-end model is able to learn global geometric knowledge obtained through pose graph optimization by back-propagating the residuals from the back-end. This significantly improves the generalization ability of the entire system and thus achieves the accuracy improvement up to 45%. To the best of our knowledge, iSLAM is the first SLAM system showing that the front-end and back-end can learn jointly and mutually contribute to each other in a self-supervised manner.

翻译：同时定位与地图构建（SLAM）是机器人导航中的关键挑战之一。最新进展表明，基于监督学习的方法在前端里程计中展现出卓越性能，而传统基于优化的方法在后端最小化估计漂移方面仍发挥着重要作用。本文发现，这种解耦范式可能导致仅能实现次优性能，从而削弱系统能力与泛化潜力。为解决该问题，我们提出了一种新型自监督学习框架——命令式SLAM（iSLAM），该框架促进前端与后端之间的相互校正，从而在无需任何外部监督的情况下提升性能。具体而言，我们将SLAM系统建模为双层优化问题，使得两个组件实现双向连接。因此，前端模型能够通过反向传播后端残差，学习经由位姿图优化获得的全局几何知识。这显著提升了整个系统的泛化能力，并实现了高达45%的精度提升。据我们所知，iSLAM是首个证明前端与后端能够以自监督方式联合学习并相互促进的SLAM系统。