ASC localization in noisy environment based on wireless sensor network

Abstract

This study investigates indoor localization problem of robot or a customer at shopping mall environment. To improve the localization accuracy, a sensor fusion-based approach is employed, which combines data from ZigBee, odometry of active shopping cart (ASC), and QR marker. The proposed algorithm employs Gaussian probability estimation method and thus it is adaptive to localization problem even at noisy environment such as the shopping mall. To implement the localization service, an ASC which is equipped with motors for navigation, a laser sensor for tracking, and a tablet computer for human-computer interaction is designed. Through experimental work, we corroborate the feasibility of the proposed localization algorithms.