发表机构
New York University（纽约大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Chain-SLAM提出链式回环机制，通过邻接图传播几何约束，实现大规模多会话LiDAR SLAM的全局一致后端，提升轨迹精度与鲁棒性。
AI 中文摘要
在长空间和时间范围内保持一致性仍然是大规模LiDAR SLAM中的一个基本挑战，尤其是在整合跨多个会话收集的地图时。我们提出了Chain-SLAM，一个LiDAR SLAM后端，能够实现在线多会话地图对齐和重用，并在大规模下保持全局一致性。我们实现了一种链式回环机制，通过邻接图在会话间关键帧之间高效传播几何约束，从而由可靠的短时回环触发稳健的长时一致性。该系统利用GNSS邻近位置识别初始化会话间对齐，然后在一个统一的因子图中进行实时回环检测和加载地图与新获取轨迹的联合优化，无需动态物体移除即可保持会话间和会话内的几何一致性，并且只需极少的超参数调整即可实现跨平台鲁棒性。实验结果表明，在大规模数据集上轨迹精度得到提高，多会话集成稳健。我们发布了源代码以支持大规模多会话LiDAR SLAM的可复现研究。项目网站：此https URL
英文摘要
Maintaining consistency over long spatial and temporal horizons remains a fundamental challenge in large-scale LiDAR SLAM, particularly when integrating maps collected across multiple sessions. We present Chain-SLAM, a LiDAR SLAM backend enabling online multi-session map alignment and reuse with global consistency at large scale. We implement a chained loop closure mechanism that efficiently propagates geometric constraints across inter-session keyframes through an adjacency graph, enabling robust long-horizon consistency triggered by reliable short-horizon loop closures. The system initializes inter-session alignment with GNSS-proximity place recognition, then performs on-the-fly loop closure detections and joint optimization of loaded maps and newly acquired trajectories within a unified factor graph, maintaining both inter- and intra-session geometric consistency without dynamic object removal, and cross-platform robustness with minimal hyperparameter tuning. Experimental results show improved trajectory accuracy and robust multi-session integration on large-scale datasets. We release our source code to support reproducible research in large-scale multi-session LiDAR SLAM. Project site: https://ai4ce.github.io/Chain-SLAM/
CommentsAccepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2026. 8 pages, 6 figures
