Please use this identifier to cite or link to this item:
|Title:||Robust pose estimation for outdoor mixed reality with sensor fusion|
|Authors:||Zhou, Z. |
|Citation:||Zhou, Z.,Karlekar, J.,Hii, D.,Schneider, M.,Lu, W.,Wittkopf, S. (2009). Robust pose estimation for outdoor mixed reality with sensor fusion. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5616 LNCS (PART 3) : 281-289. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-02713-0_30|
|Abstract:||We present a sensor fusion based technique for outdoor augmented reality system for mobile devices using GPS, gyroscope, and geo-referenced 3D models of the urban environment. Geo-spatial interaction not only provides overlays of the existing environment but compliments with other data such as location-specific photos, videos and other information from different time periods enhancing the overall user experience of augmented reality. To provide robust pose estimation of the camera relative to the world coordinates, firstly, GPS and gyroscope are used to obtain the rough estimation. Secondly, model based silhouette tracking and sensor fusion approach is used to refine the rough estimation and to provide seamless media rich augmentation of 3D textured models. © 2009 Springer Berlin Heidelberg.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Nov 16, 2018
checked on Nov 17, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.