用磁场变化检测闭环，修正里程计漂移
Online One-Dimensional Magnetic Field SLAM with Loop-Closure Detection
- 将磁场读数建模为一维轨迹，实时匹配当前观测
- 在真实手持设备上实现厘米级定位精度
- 适合无地图依赖的室内定位场景
我们提出一种轻量级磁力场同时定位与地图构建（SLAM）方法，用于修正里程计路径中的漂移问题，重点在于里程计优化而非地图构建。将历史磁力场读数表示为一维轨迹，与当前观测进行匹配，实现序列化的闭环检测与决策，基于当前位姿估计和磁力场信息。该方法结合扩展卡尔曼平滑器路径估计框架，融合里程计增量与检测到的闭环时间点。通过多种手持iPad在室内场景下的实际测试，验证了该模型的实用性。
原文摘要 · Abstract (English)
We present a lightweight magnetic field simultaneous localisation and mapping (SLAM) approach for drift correction in odometry paths, where the interest is purely in the odometry and not in map building. We represent the past magnetic field readings as a one-dimensional trajectory against which the current magnetic field observations are matched. This approach boils down to sequential loop-closure detection and decision-making, based on the current pose state estimate and the magnetic field. We combine this setup with a path estimation framework using an extended Kalman smoother which fuses the odometry increments with the detected loop-closure timings. We demonstrate the practical applicability of the model with several different real-world examples from a handheld iPad moving in indoor scenes.
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