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 trade-off in terms of computational burden, convergence time and deployment complexity.

The Devices for Assisted Living(DALi ) project is a research initiative sponsored by the European Commission under the FP7 programme aiming for the development of a robotic device to assist people with cognitive impairments in navigating complex environments. The project revisits the popular paradigm of the walker enriching it with sensing abilities (to perceive the environment), with cognitive abilities (to decide the best path across the space) and with mechanical, visual, acoustic and haptic guidance devices (to guide the person along the path). In this paper, we offer an overview of the developed system and describe in detail some of its most important technological aspects

Indoor localization and position tracking are essential to support applications and services for ambient-assisted living. While the problem of indoor localization is still open and already quite complex per se, in large public places, additional issues of cost, accuracy, and scalability arise. In this paper, the position estimation and tracking technique developed within the project devices for assisted living (DALi) is described, analyzed through simulations, and finally validated by means of a variety of experiments on the field. The goal of the DALi project is to design a robotic wheeled walker guiding people with psychomotor problems. Indeed, people with motor or cognitive impairments are often afraid of moving in large and crowded environments (e.g., because they could lose the sense of direction). In order to mitigate this problem, the position tracking approach described in this paper is based on multisensor data fusion and it is conceived to assure a good tradeoff between target accuracy, level of confidence, and deployment costs. Quite interestingly, the same approach could be used for indoor automated guided vehicles and robotics.

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Indoor navigation is a well-known research topic whose relevance has been steadily growing in the last years thrust by considerable commercial interests as well as by the need for supporting and guiding users in large public environments, such as stations, airports or shopping malls. People with motion or cognitive impairments could perceive large crowded environments as intimidating. In such situations, a smart wheeled walker able to estimate its own position autonomously could be used to guide users safely towards a wanted destination. Two strong requirements for this kind of applications are: low deployment costs and the capability to work in large and crowded environments. The position tracking technique presented in this paper is based on an Extended Kalman Filter (EKF) and is analyzed through simulations in view of minimizing the amount of sensors and devices in the environment.