外观
论文arXiv 2608.091462 个月前
面向大规模场景重建的带局部到全局闭环的多子地图隐式神经 SLAM
原文链接：Multi-Submap Implicit Neural SLAM with Local-to-Global Loop Closure for Large-Scale Scene Reconstruction（arXiv 2608.09146 · 发布日期：2026-08-10）
Multi-Submap Implicit Neural SLAM with Local-to-Global Loop Closure for Large-Scale Scene Reconstruction
针对大规模复杂环境神经 SLAM 的遗忘与漂移，提出多子地图和双层闭环系统，并在公开基准及室内外数据上验证。
核心要点
- NeRF-based SLAM 在小规模重建表现良好，但扩展到复杂大环境仍受灾难性遗忘和轨迹漂移影响。
- 方法采用动态分配神经子地图的渐进建图，结合光流跟踪、局部到全局闭环和子地图间在线蒸馏。
- 在公开基准和大规模室内外数据上评估，并在板载计算单元直接部署，重建质量和定位鲁棒性优于现有神经 SLAM 方法。
- 作者表示将通过摘要所列 GitHub 仓库公开发布代码。
验证范围与局限：验证范围包括公开基准、自有大规模室内外数据、定制手持机电平台和板载计算单元部署。
英文摘要 (Abstract)
Neural Radiance Fields (NeRF)-based SLAM has demonstrated impressive results in small-scale scene reconstruction, yet scaling these methods to extensive, complex environments remains challenging due to catastrophic forgetting and accumulated trajectory drift. This paper presents a robust, large-scale neural SLAM system featuring a multi-submap architecture and a dual-tier loop closure mechanism. Specifically, we propose a progressive mapping strategy that dynamically allocates neural submaps to maintain high-fidelity representations without memory explosion. For robust pose estimation, an optical-flow-based tracking module is integrated to handle aggressive motions. To address global consistency, we introduce a local-to-global loop closure framework leveraging the foundation model for high-performance global descriptor extraction, significantly enhancing relocalization accuracy under varying viewpoints. Furthermore, an inter-submap online distillation algorithm is designed during back-end optimization to enforce geometric and appearance consistency across overlapping submap boundaries. To validate the system, we developed a customized handheld mechatronic platform and conducted extensive evaluations on both public benchmarks and our large-scale indoor-outdoor datasets. Experimental results, including direct deployment on an onboard computing unit, demonstrate that our approach outperforms state-of-the-art neural SLAM methods in reconstruction quality and localization robustness, providing a scalable solution for real-world robotic perception and digital twinning. We will release the code publicly on \href{https://github.com/dtc111111/MSN-SLAM}{https://github.com/dtc111111/MSN-SLAM} .
作者：Tianchen Deng, Chongdi Wang, Nailin Wang, Lei Zhao, Ziqi Ma, Tianjun Zhang, Zhe Liu, Danwei Wang, Hesheng Wang
来源
本页事实对应的公开页面，以原文为准。