In this letter, we present a neural field-based real-time monocular mapping framework for accurate and dense Simultaneous Localization and Mapping (SLAM). Recent neural mapping frameworks show promising results, but rely on RGB-D or pose inputs, or cannot run in real-time. To address these limitations, our approach integrates dense-SLAM with neural implicit fields. Specifically, our dense SLAM approach runs parallel tracking and global optimization, while a neural field-based map is constructed incrementally based on the latest SLAM estimates. For the efficient construction of neural fields, we employ multi-resolution grid encoding and signed distance function (SDF) representation. This allows us to keep the map always up-to-date and adapt instantly to global updates via loop closing. For global consistency, we propose an efficient Sim(3)-based pose graph bundle adjustment (PGBA) approach to run online loop closing and mitigate the pose and scale drift. To enhance depth accuracy further, we incorporate learned monocular depth priors. We propose a novel joint depth and scale adjustment (JDSA) module to solve the scale ambiguity inherent in depth priors. Extensive evaluations across synthetic and real-world datasets validate that our approach outperforms existing methods in accuracy and map completeness while preserving real-time performance.

翻译：本文提出了一种基于神经场的实时单目建图框架，用于实现高精度稠密同步定位与地图构建。现有神经建图框架虽展现出良好性能，但依赖RGB-D或位姿输入，且无法实时运行。为解决上述局限，本文方法将密集SLAM与神经隐式场相结合。具体而言，密集SLAM方法并行执行追踪与全局优化，同时基于最新SLAM估计值逐步构建神经场地图。为实现神经场的高效构建，本文采用多分辨率网格编码与符号距离函数表示方法，使地图始终处于最新状态并能通过闭环检测即时适应全局更新。针对全局一致性，我们提出基于Sim(3)的高效位姿图光束法平差方法来执行在线闭环检测，从而缓解位姿与尺度漂移。为提升深度精度，进一步融入单目深度先验知识，并设计新颖的联合深度与尺度调整模块以解决深度先验固有的尺度模糊性。在合成数据集与真实数据集上的综合评估表明，本方法在保持实时性能的同时，在建图精度与完整性方面均优于现有方法。