用磁力计阵列实现室内定位、校准与建图同步，大幅降低漂移。
SL(C)AMma: Simultaneous Localisation, (Calibration) and Mapping With a Magnetometer Array
- 用磁力计阵列直接估计运动轨迹，减少漂移积累。
- 在10个数据集上验证，相比惯性传感器积分，漂移减少超80%。
- 可同步校准磁力计参数，适用于复杂运动场景。
室内定位受GNSS信号衰减和本体感知传感器累积漂移影响。基于磁场的同步定位与建图（SLAM）通过回环检测缓解漂移，但探索未知区域仍具挑战。磁力计阵列相比单个磁力计可直接估计位姿，但测量不一致会干扰位姿估计与回环检测。本文提出两种滤波算法：一是基于磁力计阵列的SLAM（SLAMma），二是联合估计磁力计校准参数的SLCAMma。蒙特卡洛仿真表明，在足够姿态激励下可准确估计校准参数，且不同运动类型下传感器间测量保持一致。在10个数据集上的实验验证了上述结果，当单磁力计SLAM失效时，SLAMma与SLCAMma仍能提供良好轨迹估计，相比惯性传感器积分，漂移减少超过80%。
原文摘要 · Abstract (English)
Indoor localisation techniques suffer from attenuated Global Navigation Satellite System (GNSS) signals and from the accumulation of unbounded drift by integration of proprioceptive sensors. Magnetic field-based Simultaneous Localisation and Mapping (SLAM) reduces drift through loop closures by revisiting previously seen locations, but extended exploration of unseen areas remains challenging. Recently, magnetometer arrays have demonstrated significant benefits over single magnetometers, as they can directly estimate the odometry. However, inconsistencies between magnetometer measurements negatively affect odometry estimates and complicate loop closure detection. We propose two filtering algorithms: The first focuses on magnetic field-based SLAM using a magnetometer array (SLAMma). The second extends this to jointly estimate the magnetometer calibration parameters (SLCAMma). We demonstrate, using Monte Carlo simulations, that the calibration parameters can be accurately estimated when there is sufficient orientation excitation, and that magnetometers achieve inter-sensor measurement consistency regardless of the type of motion. Experimental validation on ten datasets confirms these results, and we demonstrate that in cases where single magnetometer SLAM fails, SLAMma and SLCAMma provide good trajectory estimates with, more than 80% drift reduction compared to integration of proprioceptive sensors.
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