Abstract
This paper presents a method of estimating camera pose in an unknown scene. While this has previously been attempted by adapting SLAM algorithms developed for robotic exploration, we propose a system specifically designed to track a hand-held camera in a small AR workspace. We propose to split tracking and mapping into two separate tasks, processed in parallel threads on a dual-core computer: one thread deals with the task of robustly tracking erratic hand-held motion, while the other produces a 3D map of point features from previously observed video frames. This allows the use of computationally expensive batch optimisation techniques not usually associated with real-time operation: The result is a system that produces detailed maps with thousands of landmarks which can be tracked at frame-rate, with an accuracy and robustness rivalling that of state-of-the-art model-based systems.
Keywords
WorkspaceComputer scienceComputer visionRobustness (evolution)Artificial intelligenceThread (computing)Frame rateTracking systemSimultaneous localization and mappingTracking (education)Frame (networking)Computer graphics (images)RobotKalman filterMobile robot
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Publication Info
- Year
- 2007
- Type
- article
- Citations
- 4184
- Access
- Closed
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Cite This
Georg Klein,
David W. Murray
(2007).
Parallel Tracking and Mapping for Small AR Workspaces.
.
https://doi.org/10.1109/ismar.2007.4538852
Identifiers
- DOI
- 10.1109/ismar.2007.4538852