首个大规模室外磁力定位与建图数据集，支持多模态长期导航研究。
Mag4D-SLAM Dataset: A Repeated-Traversal Multi-Modal 4D Geomagnetic Dataset for Localization and Mapping
- 采集18公里校园路径的多模态数据，含激光雷达、相机、磁力计等。
- 验证磁信号在昼夜和双向重复轨迹中具有高可复现性。
- 适合研究磁力辅助定位、长期导航及视觉失效场景下的鲁棒方案。
磁力感知提供无需基础设施、抗GNSS失灵与视觉退化的绝对方向参考，但现有大型户外机器人数据集缺乏对其系统性研究的支持。已有磁力数据集局限于小范围室内环境，且缺少同步多模态感知、重复通行结构及高精度6-DoF真值。本文提出首个大规模户外磁力SLAM数据集Mag4D-SLAM，包含14条序列，总长超18公里，涵盖同步的激光雷达、相机、IMU、三轴磁力计与GNSS测量，以及SE(3)真值位姿，采集于校园结构化路径，在昼夜配对条件下进行正反向行驶。通过重复通行实验，分析了三个核心特性：不同记录会话间的磁场可重复性（昼夜对比）、无漂移全局航向估计，以及跨会话位置区分的磁信号特征。该数据集旨在支持航向漂移抑制、磁力回环闭合与长期定位研究，并揭示磁力感知如何补充视觉与激光雷达模态，或在光照变化、结构重复与无GNSS环境下提供备用定位线索。
原文摘要 · Abstract (English)
Geomagnetic sensing offers an infrastructure-free, absolute orientation reference that is robust to GNSS denial and visual degradation, yet no large-scale outdoor robotics dataset supports its systematic study in SLAM. Existing magnetic datasets are confined to small-scale indoor environments and lack the synchronized multi-modal sensing, repeated-traversal structure, and high-precision 6-DoF ground truth required for geomagnetic SLAM research. We present Mag4D-SLAM, the first large-scale outdoor geomagnetic SLAM dataset. It comprises 14 sequences totaling over 18 km of synchronized LiDAR, camera, IMU, tri-axis magnetometer, and GNSS measurements with SE(3) ground-truth poses, collected along structured campus trajectories under paired day/night conditions in both forward and reverse directions. Through repeated-traversal experiments, we analyze three core properties: magnetic field repeatability across different recording sessions (daytime and nighttime), drift-free global heading estimation, and location-discriminative magnetic signatures for cross-session place recognition. Mag4D-SLAM is designed to support research on yaw drift mitigation, magnetic loop closure, and long-term localization and to open new research questions on how geomagnetic sensing can complement visual and LiDAR modalities or provide a fallback cue under illumination changes, structural repetition, and GNSS-denied long-term operation.
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