Object tracking system by integrating multi-sensored data

We propose an object tracking system which recognizes everyday objects and estimates their positions by using distributed sensors in a room and mobile robots. The placement of objects is frequently changed according to human activities. Although a passive RFID tag is attached to each object for the object's recognition, the placement is often not uniquely determined due to the deficiency of measured data. We have already proposed a method for estimating the placement of objects by using the moving trajectories of objects. This estimation result is expressed as the probability distribution of the object placement. However intersections of trajectories cause the decrease of the estimation accuracy. So we propose a new method based on Bayesian inference to improve the estimation accuracy by using the size and the shape of an object measured by laser range finder. Then a mobile robot settles the placement with small workload by using the mounted sensor. The system successfully recognized and localized 10 objects in the experiment.