无需手动旋转手机，可在SLAM过程中自动校准磁力计。
Saying goodbyes to rotating your phone: Magnetometer calibration during SLAM
- 将磁力计校准融入SLAM过程，用粒子滤波分离校准参数与地图。
- 在商场和办公室场景中达到与人工校准相当的精度。
- 适合无法手动操作的移动设备或机器人导航场景。
尽管室内定位仍以Wi-Fi为主，但利用磁场特征作为替代或补充信息已广泛应用。磁力计偏差是磁场导航与SLAM中的主要挑战。传统校准依赖球体或椭球拟合方法，并需用户手动执行如八字形旋转等操作，但在设备笨重、高速运动或用户行为不可控时难以实现。近期研究提出利用地图数据进行校准，本文进一步验证：无需预存地图，校准可作为SLAM过程的一部分完成。所提方法采用因子化粒子滤波，同时估计磁力计偏差与磁场地图。在商场智能手机数据和办公室移动机器人数据上验证，结果表明该方法在精度上可媲美人工校准；且在人工校准基础上叠加使用时略有提升，表明多种校准方式存在融合潜力。
原文摘要 · Abstract (English)
While Wi-Fi positioning is still more common indoors, using magnetic field features has become widely known and utilized as an alternative or supporting source of information. Magnetometer bias presents significant challenge in magnetic field navigation and SLAM. Traditionally, magnetometers have been calibrated using standard sphere or ellipsoid fitting methods and by requiring manual user procedures, such as rotating a smartphone in a figure-eight shape. This is not always feasible, particularly when the magnetometer is attached to heavy or fast-moving platforms, or when user behavior cannot be reliably controlled. Recent research has proposed using map data for calibration during positioning. This paper takes a step further and verifies that a pre-collected map is not needed; instead, calibration can be done as part of a SLAM process. The presented solution uses a factorized particle filter that factors out calibration in addition to the magnetic field map. The method is validated using smartphone data from a shopping mall and mobile robotics data from an office environment. Results support the claim that magnetometer calibration can be achieved during SLAM with comparable accuracy to manual calibration. Furthermore, the method seems to slightly improve manual calibration when used on top of it, suggesting potential for integrating various calibration approaches.
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