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。近期研究表明，基于监督学习的方法在前端里程计中表现出色，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重要作用。本文发现，这种解耦范式仅能实现次优性能，从而限制了系统能力与泛化潜力。为解决此问题，我们提出了一种新颖的自监督学习框架——命令式SLAM（iSLAM），该方法促进前端与后端之间的相互修正，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，使两个组件实现双向连接。通过反向传播后端的残差，前端模型能够学习通过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相互学习、彼此促进的SLAM系统。