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Majority Rule Sensor Fusion System with Particle Filter for Robust Robot Localization

This paper discusses a robust localization method that uses particle filtering. A particle filter can suppress the influence of temporary noise on a sensor based on past sensor data. However, localization fails when a sensor is affected by noise that lasts for several minutes even when using a particle filter. We have previously proposed a majority rule-based sensor fusion system that removes sensors affected by such constant noise. To date, we have conducted some experiments to evaluate the effectiveness of this system without particle filter. In this paper, we experimentally confirm the effectiveness of this system with particle filtering. The results show that the proposed system reduces position errors in part of the environment.