Indoor Localization of Mobile Robots Through QR Code Detection and Dead Reckoning Data Fusion

Many techniques for robot localization rely on the assumption that both process and measurement noises are uncorrelated, white, and normally distributed. However, if this assumption does not hold, these techniques are no longer optimal and, in addition, the maximum estimation errors can be hardly kept under control. In this paper, this problem is addressed by means of a tailored extended H∞filter (EHF) fusing odometry and gyroscope data with position and heading measurements based on quick response (QR) code landmark recognition. In particular, it is shown that, by properly tuning EHF parameters and by using an adaptive mechanism to avoid finite escape time phenomena, it is possible to achieve a higher localization accuracy than using other dynamic estimators, even if QR codes are detected sporadically. Also, the proposed approach ensures a good tradeoff in terms of computational burden, convergence time, and deployment complexity.