Real-Time Camera Pose Estimation for Wide-Area Augmented Reality Applications

Achieving accurate registration between real and synthetic worlds is one of augmented reality's biggest challenges. A real-time camera-pose estimation method, based on multiple maps and local bundle adjustment, enables the registration to work without prior knowledge of natural scenes. This method can significantly enhance AR systems' usability.

[1]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[2]  Michel Dhome,et al.  Real Time Localization and 3D Reconstruction , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[3]  David W. Murray,et al.  Improving the Agility of Keyframe-Based SLAM , 2008, ECCV.

[4]  Soh-Khim Ong,et al.  Registration using natural features for augmented reality systems , 2006, IEEE Transactions on Visualization and Computer Graphics.

[5]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[6]  Vincent Lepetit,et al.  Multiple 3D Object tracking for augmented reality , 2008, 2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality.

[7]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[8]  Vincent Lepetit,et al.  Fast Keypoint Recognition in Ten Lines of Code , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Dieter Schmalstieg,et al.  Pose tracking from natural features on mobile phones , 2008, 2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality.

[10]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[11]  G. Klein,et al.  Parallel Tracking and Mapping for Small AR Workspaces , 2007, 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality.

[12]  David Nister,et al.  Recent developments on direct relative orientation , 2006 .

[13]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.