论文标题
室内使用脚踩的IMU和当地磁场
Indoor SLAM Using a Foot-mounted IMU and the local Magnetic Field
论文作者
论文摘要
在本文中，提出了同时定位和映射（大满贯）算法，用于跟踪具有脚部惯性测量单元（IMU）的行人运动的运动。该算法使用两个地图，即运动图和磁场图。运动图捕获了在建筑物中受到限制的建筑物中行人的典型运动模式。走廊和门。磁图使用高斯工艺（GP）模型在环境中模拟局部磁场异常，并将其用作位置信息。这些地图用于Rao-Blackwellized粒子过滤器（RBPF），以纠正行人死刑（PDR）的行人位置和方向估计。 PDR是使用具有零速度更新（ZUPT-EKF）的扩展Kalman过滤器计算的。该算法使用实际实验序列进行了验证，结果表明该算法在室内环境中定位行人方面的功效。
In this paper, a simultaneous localization and mapping (SLAM) algorithm for tracking the motion of a pedestrian with a foot-mounted inertial measurement unit (IMU) is proposed. The algorithm uses two maps, namely, a motion map and a magnetic field map. The motion map captures typical motion patterns of pedestrians in buildings that are constrained by e.g. corridors and doors. The magnetic map models local magnetic field anomalies in the environment using a Gaussian process (GP) model and uses them as position information. These maps are used in a Rao-Blackwellized particle filter (RBPF) to correct the pedestrian position and orientation estimates from the pedestrian dead-reckoning (PDR). The PDR is computed using an extended Kalman filter with zero-velocity updates (ZUPT-EKF). The algorithm is validated using real experimental sequences and the results show the efficacy of the algorithm in localizing pedestrians in indoor environments.
