发表机构
Wangxuan Institute of Computer Technology, Peking University; School of Computer Science and Technology, Beihang University; Yuanpei College, Peking University（北京大学王选计算机研究所; 北京航空航天大学计算机科学与技术学院; 北京大学元培学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对高保真结构光深度定制实用SLAM系统，核心方法是强化深度感知并构建以深度为中心的管道，主要贡献是提升跟踪精度、降低轨迹偏差，实现实用稳健的SLAM且运行速度达20.9 FPS。
AI 中文摘要
结构光（SL）相机为数百万设备提供深度感知，近期神经SL解码方法显著提升了深度质量。SLAM系统能从这种强大深度感知中大幅受益。本文提出NSL-SLAM，一种针对高保真结构光深度定制的实用SLAM系统。先强化SL深度感知，将单目深度先验融入SL立体解码，在Replica-SL上深度RMSE比NSL降低35%。然后构建以深度为中心的SLAM管道，利用结构光几何的密集性和精确性，以其为主要跟踪信号，补充稀疏视觉对应和轻量级束调整。实验表明，在合成基准上NSL-SLAM跟踪精度最佳，重建F分数提高1.6分；在真实基准上，它是唯一在所有序列避免灾难性失败且轨迹偏差比基线低43.3%的方法，且在线运行速度达20.9 FPS，证明更强结构光深度和以深度为中心的系统设计实现了实用、稳健的SLAM。
英文摘要
Structured-light (SL) cameras power depth sensing in millions of devices, and recent neural SL decoding methods have substantially improved their depth quality. SLAM systems can benefit greatly from such strong depth sensing, where reliable geometry enables stable tracking and faithful reconstruction. In this work, we present NSL-SLAM, a practical SLAM system tailored for high-fidelity structured-light depth. We first strengthen SL depth sensing: inspired by the neural structured-light (NSL) method, we further incorporate strong monocular depth priors into the SL stereo decoding, reducing depth RMSE by 35% on Replica-SL compared to NSL. We then build a depth-centric SLAM pipeline with this stronger depth: because structured-light geometry is dense and metrically accurate, we keep it as the primary tracking signal, and add only sparse visual correspondences for geometrically degenerate cases and lightweight bundle adjustment for long-range drift. Our depth estimator and SLAM design reinforce each other: stronger depth makes a simple SLAM pipeline effective, and the depth-centric pipeline ensures this advantage transfers to downstream reconstruction. Experimentally, on the synthetic Replica-SL benchmark, NSL-SLAM achieves the best tracking accuracy and improves reconstruction F-score by 1.6 points over the SOTA baseline under a shared-depth protocol. On a real benchmark of 8 challenging scenes, it is the only method that avoids catastrophic failure on all sequences while achieving 43.3% lower trajectory deviation than selected baselines. The SLAM system runs online at 20.9 FPS, demonstrating that stronger structured-light depth and depth-centric system design together enable practical, robust SLAM.
Comments13 pages, 9 figures
