Real-Time Self-Positioning with the Zero Moment Point Model and Enhanced Position Accuracy Using Fiducial Markers

Many companies are turning their attention to digitizing the work efficiency of employees in large factories and warehouses, and the demand for measuring individual self-location indoors is increasing. While methods combining wireless network technology and Pedestrian Dead Reckoning (PDR) have been developed, they face challenges such as high infrastructure costs and low accuracy. In this study, we propose a novel approach that combines high-accuracy fiducial markers with the Center of Gravity Zero Moment Point (COG ZMP) model. Combining fiducial markers enables precise estimation of self-position on a map. Furthermore, the use of high-accuracy fiducial markers corrects modeling errors in the COG ZMP model, enhancing accuracy. This method was evaluated using an optical motion capture system, confirming high accuracy with a relative error of less than 3%. Thus, this approach allows for high-accuracy self-position estimation with minimal computational load and standalone operation. Moreover, it offers a cost-effective solution, contributing to society by enabling low-cost, high-performance self-positioning. This research enables high-accuracy standalone self-positioning and contributes to the advancement of indoor positioning technology.