Bundle Adjustment - A Modern Synthesis
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Real Time Localization and 3D Reconstruction
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Parallel Tracking and Mapping for Small AR Workspaces
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Fast Odometry Integration in Local Bundle Adjustment-Based Visual SLAM
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Fusion of GPS and structure-from-motion using constrained bundle adjustments
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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SLAM is the generic name given to the class of methods allowing to incrementally build a 3D representation of an environment while simultaneously using this map to localize a mobile system evolving within this environment. Though quite a mature field, several scientific problems remain open and particularly the reduction of drift. Drift is inherent to SLAM since the task is fundamentally incremental and errors in model estimation are cumulative. In this paper we suggest to take advantage from sparse but accurate knowledge of the environment to periodically reinitialize the system, thus stopping the drift. As it may be of interest in a Augmented reality context, we show this knowledge can be propagated to past estimations through bundle adjustment and present three different strategies to perform this propagation. Experiments carried out in an urban environment are described and demonstrate the efficiency of our approach.