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Title: Positioning, tracking and mapping for outdoor augmentation
Authors: Karlekar, J. 
Zhou, S.Z. 
Lu, W. 
Loh, Z.C. 
Nakayama, Y. 
Hii, D. 
Keywords: 3d mapping
Augmented reality
H.5.1 [information systems]: multimedia information systems - augmented reality; I.4.8 [image processing and computer vision]:
Robust tracking
Scene analysis - sensor fusion, tracking
Sensor fusion
Shape matching
User positioning
Issue Date: 2010
Citation: Karlekar, J.,Zhou, S.Z.,Lu, W.,Loh, Z.C.,Nakayama, Y.,Hii, D. (2010). Positioning, tracking and mapping for outdoor augmentation. 9th IEEE International Symposium on Mixed and Augmented Reality 2010: Science and Technology, ISMAR 2010 - Proceedings : 175-184. ScholarBank@NUS Repository.
Abstract: This paper presents a novel approach for user positioning, robust tracking and online 3D mapping for outdoor augmented reality applications. As coarse user pose obtained from GPS and orientation sensors is not sufficient for augmented reality applications, sub-meter accurate user pose is then estimated by a one-step silhouette matching approach. Silhouette matching of the rendered 3D model and camera data is carried out with shape context descriptors as they are invariant to translation, scale and rotational errors, giving rise to a non-iterative registration approach. Once the user is correctly positioned, further tracking is carried out with camera data alone. Drifts associated with vision based approaches are minimized by combining different feature modalities. Robust visual tracking is maintained by fusing frame-to-frame and model-to-frame feature matches. Frame-to-frame tracking is accomplished with corner matching while edges are used for model-to-frame registration. Results from individual feature tracker are fused using a pose estimate obtained from an extended Kalman filter (EKF) and a weighted M-estimator. In scenarios where dense 3D models of the environment are not available, online 3D incremental mapping and tracking is proposed to track the user in unprepared environments. Incremental mapping prepares the 3D point cloud of the outdoor environment for tracking. ©2010 IEEE.
Source Title: 9th IEEE International Symposium on Mixed and Augmented Reality 2010: Science and Technology, ISMAR 2010 - Proceedings
ISBN: 9781424493449
DOI: 10.1109/ISMAR.2010.5643567
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