用磁力计数据实现无需校准的高精度室内定位。
Magnetic Indoor Localization through CNN Regression and Rotation Invariance
- 用磁力大小和重力方向投影构建旋转不变特征。
- 在三个建筑中，旋转超过阈值后性能优于传统三维输入。
- 模型轻量适合手机部署，定位无需设备朝向校准。
室内定位在无GNSS环境下至关重要，如导航与物联网系统。结合卷积神经网络（CNN）与磁场特征可提供低成本、无需基础设施的精准定位方案。尽管磁指纹具有潜力，但基于原始3D磁力计数据的模型对设备朝向敏感。本文提出两种旋转不变特征：磁力模长（Mn）和投影到重力轴的分量（Mg），并训练轻量级7层空洞CNN（MagNetS/XL）直接回归（x, y）坐标。基于MagPie数据集（三栋建筑，手持轨迹），系统评估了固定或随机旋转下的表现。原始3D输入（Mx, My, Mz）在90°固定旋转下误差显著上升，随机旋转加剧退化；而2D输入（Mn, Mg）保持旋转不变性，在三栋建筑中分别于0°（Loomis，大）、5°（Talbot，中）、6°（CSL，小）以上时超越3D输入。MagNetXL达到或超过当前最优水平，而MagNetS参数量仅为三分之一，更适合移动端部署。结果表明，旋转不变输入带来的鲁棒性优势超过维度损失，在真实场景中实现无需朝向对齐或额外设施的建图与定位。
原文摘要 · Abstract (English)
Indoor positioning is an essential technology for a wide range of applications in GNSS-denied environments, including indoor navigation and IoT systems. Combining convolutional neural networks (CNNs) and magnetic field-based features offers a low-cost, infrastructure-free solution for precise positioning. While magnetic fingerprints are a promising approach for indoor positioning, models trained on raw 3D magnetometer data are highly sensitive to device orientation. We address this by using two rotation invariant features derived from the 3D magnetic field: the norm (Mn) and the projection onto the gravity axis (Mg). We train a lightweight 7-layer dilated CNN (MagNetS/XL) on magnetic sequences to directly regress (x, y) positions. Using the MagPie dataset (three buildings, handheld trajectories), we systematically evaluate fixed and random rotations of test and/or train data. Raw 3D inputs (Mx, My , Mz) exhibit isotropic error increases under fixed 90° rotations and further degrade with growing random rotations. In contrast, 2D (Mn, Mg) inputs maintain rotation invariant accuracy and surpass the 3D inputs once rotation exceeds building-specific thresholds for three reference buildings: 0° for Loomis (large), 5° for Talbot (medium), and 6° for CSL (small). MagNetXL achieves or exceeds state-of-the-art accuracy on the MagPie dataset, and MagNetS delivers similar performance with roughly one third of the parameters, favoring mobile deployment. These results show that the robustness gained from rotation invariant inputs outweighs the loss of input dimensionality in realistic usage, allowing mapping and localization without orientation alignment or added infrastructure.
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