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.

基于神经场的实时单目建图框架与密集 SLAM 相结合，利用多分辨率网格编码和有符号距离函数表征进行神经场的高效构建，并通过环路闭合和深度先验进行全局一致性和精度增强，从而优于现有方法，在保持实时性能的同时提高了准确度和地图完整性。