基于地磁场的室内定位和地图构建

提出了一种利用室内地磁场的空间波动来实现即时定位和地图构建的方法。为了能增加定位精度和减少计算量,本文依据地磁场传感器能够测量地磁3个正交方向上的分量和不同的权重计算方式改进了粒子滤波算法,并结合克里金法对地磁地图进行更新。在定位阶段,利用改进的粒子滤波来估计机器人的位置,算法的收敛速度每次约加快0.5 s和定位误差约减少3.5 m;在构造地图阶段,利用克里金空间插值法来实现的空间波动地磁地图的更新较其他插值算法更灵活,且经过插值后的地图更有助于提高机器人的定位精度。通过MATLAB仿真实验,证明了该方法能够准确快速地对机器人定位,并且通过机器人观测值和里程计数据实时地构建连续一致的地图。

Simultaneous localization and mapping based on indoor magnetic anomalies

This paper presents a simultaneous localization and mapping (SLAM) algorithm that utilizes the local spatial anomalies of the ambient magnetic field present in many indoor environments. In order to increase the positioning accuracy and reduce the amount of calculation, we improved particle filter algorithm, according to the characteristics of the magnetic field sensor measuring geomagnetic component on the three orthogonal directions and different weights calculation. In the localization stage, we use the improved particle filter to estimate the pose distribution of the robot. During the period, the convergence rate of the algorithms each iteration speeds up about 0.5s and positioning error reduces about 3.5m. And in the mapping stage, Kriging interpolation method is more flexible than other interpolation algorithms, when it used to update the fluctuant magnetic field map. The interpolated map improves the positioning accuracy of the robot. The feasibility of the proposed approach is validated by MATLAB simulations, which demonstrate that the approach can quickly and accurately locate the robot and construct the consistent map using only odometric data and measurements obtained from the ambient magnetic field.