外观
论文arXiv 2607.219863 个月前
Mag4D-SLAM数据集：用于定位与建图的重复遍历多模态4D地磁数据集
原文链接：Mag4D-SLAM Dataset: A Repeated-Traversal Multi-Modal 4D Geomagnetic Dataset for Localization and Mapping（arXiv 2607.21986 · 发布日期：2026-07-24）
Mag4D-SLAM Dataset: A Repeated-Traversal Multi-Modal 4D Geomagnetic Dataset for Localization and Mapping
Mag4D-SLAM面向室外地磁SLAM，提供多模态重复遍历数据，并在校园轨迹上分析地磁可重复性、航向估计与地点识别。
核心要点
- 现有磁数据集多限于小规模室内，缺少室外地磁SLAM系统研究所需数据。
- 数据含14段、超18 km，同步LiDAR、相机、IMU、三轴磁力计与GNSS。
- 提供SE(3)真值姿态，并在校园轨迹按昼夜、正反方向成对采集。
- 重复遍历实验分析磁场可重复性、无漂移全局航向和跨会话地点识别。
验证范围与局限：验证范围为结构化校园室外轨迹，包含昼夜条件和正反方向重复遍历。
英文摘要 (Abstract)
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.
作者：Bibhutibhusan Nayak, Hyoseok Ju, G D Kim
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