Abstract
In the indoor environment, variation of the magnetic field is caused by building structures, and magnetic field map navigation is based on this feature. In order to estimate position using this navigation, a three-axis magnetic field must be measured at every point to build a magnetic field map. After the magnetic field map is obtained, the position of the mobile robot can be estimated with a likelihood function whereby the measured magnetic field data and the magnetic field map are used. However, if only magnetic field map navigation is used, the estimated position can have large errors. In order to improve performance, we propose a particle filter system that integrates magnetic field map navigation and an encoder system. In this paper, multiple magnetic sensors and three magnetic field maps (a horizontal intensity map, a vertical intensity map, and a direction information map) are used to update the weights of particles. As a result, the proposed system estimates the position and orientation of a mobile robot more accurately than previous systems. Also, when the number of magnetic sensors increases, this paper shows that system performance improves. Finally, experiment results are shown from the proposed system that was implemented and evaluated.
Highlights
As the need for indoor navigation grows, various techniques for indoor navigation are rapidly developing
At the in Atexperiment, the beginning the experiment, the estimation from odometry the beginning of the theof estimation results from odometry results using the encoders wasusing less than encoders was lesssystem, than with theestimated proposedposition system,from but odometry the estimated position from We odometry with the proposed but the diverged over time
We proposed a positioning system using a magnetic field map navigation and an encoder system
Summary
As the need for indoor navigation grows, various techniques for indoor navigation are rapidly developing. Magnetic field map navigation is able to estimate absolute position without infrastructure and the divergence problem. Since the measurement probability density function (PDF) is non-Gaussian in magnetic field map navigation, a particle filter was used for this paper [13,16]. Self-localization magnetic field map methods based on a particle filter have been researched. These systems propagate the particles by specific situations, such as the speed limit, without any other positioning system in the propagation step [13,16]. In order to solve these problems and to increase performance, we propose a particle filter system that integrates magnetic field map navigation and an encoder system.
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