%0 Journal Article
%T Smart Localization Using a New Sensor Association Framework for Outdoor Augmented Reality Systems
%A F. Ababsa
%A I. Zendjebil
%A J.-Y. Didier
%A M. Mallem
%J Journal of Robotics
%D 2012
%I Hindawi Publishing Corporation
%R 10.1155/2012/634758
%X Augmented Reality (AR) aims at enhancing our the real world, by adding fictitious elements that are not perceptible naturally such as: computer-generated images, virtual objects, texts, symbols, graphics, sounds, and smells. The quality of the real/virtual registration depends mainly on the accuracy of the 3D camera pose estimation. In this paper, we present an original real-time localization system for outdoor AR which combines three heterogeneous sensors: a camera, a GPS, and an inertial sensor. The proposed system is subdivided into two modules: the main module is vision based; it estimates the userĄ¯s location using a markerless tracking method. When the visual tracking fails, the system switches automatically to the secondary localization module composed of the GPS and the inertial sensor. 1. Introduction The idea of combining several kinds of sensors is not recent. The first multi-sensors system appeared with robotic applications where, for example, in [1] Vieville et al. proposed to combine a camera with an inertial sensor to automatically correct the path of an autonomous mobile robot. This idea has been exploited these last years by the community of Mixed Reality. Several works proposed to fuse vision and inertial data sensors, using a Kalman filter [2¨C6] or a particular filter [7, 8]. The strategy consists in merging all data from all sensors to localize the camera following a prediction/correction model. The data provided by inertial sensors (gyroscopes, magnetometers, etc.) are generally used to predict the 3D motion of the camera which is then adjusted and refined using the vision-based techniques. The Kalman filter is generally implemented to perform the data fusion. Kalman filter is a recursive filter that estimates the state of a linear dynamic system from a series of noisy measurements. Recursive estimation means that only the estimated state from the previous time step and the current measurement are needed to compute the estimate for the current state. So, no history of observations and/or estimates is required. In [2] You et al. developed a hybrid sensor combining a vision system with three gyroscopes to estimate the orientation of the camera in an outdoor environment. Their visual tracking allows refining the obtained estimation. The system described by Ababsa [5] combines an edge-based tracking with inertial measurements (angular velocity, linear acceleration, magnetic fields). The visual tracking is used for accurate 3D localization while the inertial sensor compensates errors due to sudden motion and occlusion. The measurements of
%U http://www.hindawi.com/journals/jr/2012/634758/