Design and performance analysis of an indoor position tracking technique for smart rollators

Abstract

Abstract-This paper presents a position tracking technique based on multisensor data fusion for rollators helping elderly people to move safely in large indoor spaces such as public buildings, shopping malls or airports. The proposed technique has been developed within the FP7 project DALi, and relies on an extended Kalman filter processing data from dead-reckoning sensors (i.e. encoders and gyroscopes), a short-range radio frequency identification (RFID) system and a front Kinect camera. As known, position tracking based on dead-reckoning sensors only is intrinsically affected by growing uncertainty. In order to keep such uncertainty within wanted boundaries, the position values are occasionally updated using a coarse-grained grid of low-cost passive RFID tags with known coordinates in a given map-based reference frame. Unfortunately, RFID tag detection does not provide any information about the orientation of the rollator. Therefore, a front camera detecting some markers on the walls is used to adjust direction. Of course, the data rate from both the RFID reader and the camera is not constant, as it depends on the actual user's trajectory and on the distance between pairs of RFID tags and pairs of markers. Therefore, the average distance between tags and markers should be properly set to achieve a good trade-off between overall deployment costs and accuracy. In the paper, the results of a simulation-based performance analysis are reported in view of implementing the proposed localization and tracking technique in a real environment.

Indoor positioning of wheeled devices for Ambient Assisted Living: A case study

“…To a first approximation, some experiments conducted using real sensors generally confirm this assumption, although the joint probability density function associated with RFID tag detection exhibits more a uniform quasi-circular symmetry around the tag than a Gaussian shape [19]. Stationarity is not an issue for EKFs.…”

“…Passive RFID tags are inexpensive, have a limited reading range and can be stuck on the floor at known positions to adjust the estimated coordinates, regardless of the number of people in the environment. Although the idea of using passive RFIDs for localization is not new [10], [16], [17], the solution presented in this paper extends and improves the general idea described in [18], [19], by estimating and compensating possible systematic contributions due to leftside/right-side differences. Unfortunately, the tags cannot provide any direct measure about the direction of motion, and a gyroscope alone is not able to observe orientation.…”

“…In fact, EKFs keep on working properly even when the process noise covariance and the measurement noise covariance grow over time, as it happens when the data collected from encoders and gyroscope are integrated. Using a notation similar to the one reported in [19], the predicted state estimate results from…”

Indoor Positioning of a Robotic Walking Assistant for Large Public Environments

“…Assuming, to a first approximation, that the probability of reading a tag is constant within the circle centered in the tag itself and with radius R equal to the maximum nominal range of the RFID reader, then the uncertainty contributions associated with coordinates x and y are uncorrelated and σ 2 x k = σ 2 y k = R 2 /4 [23].…”

“…In Section III, the localization problem is formulated and the underlying system and measurement models are described. Such models partially derive from the results of preliminary studies reported in [22] and [23], but they have been improved in order to estimate and compensate the effect of possible left-/right-side mechanical asymmetries of the device. Section IV focused on the description of the chosen estimator.…”