A Monte Carlo Localization algorithm for 2-D indoor self-localization based on magnetic field

Evidence shows that a large variety of animals use Earth's magnetic field for navigation. Inspired by this intriguing ability of animals, we propose a MCL algorithm that utilizes local anomalies of magnetic field to achieve 2-D indoor self-localization. Monte Carlo Localization (MCL) is one of the most popular probabilistic techniques due to the high efficiency and accuracy, but one potential problem is particle impoverishment. In order to further improve the performance of MCL, we employ a clustering approach to get the clustering information and thus resolving the problem of losing effective particles. The approach has been implemented and intensively tested in real-world environments. The result shows that the proposed approach provides a simple, robust, low-cost solution for indoor localization.