@article{
 title = {Parallel Tracking and Mapping for Small AR Workspaces},
 type = {article},
 year = {2007},
 id = {1a58891b-0674-36c9-bd0b-2114a618766d},
 created = {2022-08-19T09:18:02.657Z},
 accessed = {2022-08-19},
 file_attached = {true},
 profile_id = {48fc0258-023d-3602-860e-824092d62c56},
 group_id = {5ec9cc91-a5d6-3de5-82f3-3ef3d98a89c1},
 last_modified = {2022-08-28T12:49:27.287Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 folder_uuids = {97693603-b330-4e3e-8cf5-d549e6474921},
 private_publication = {false},
 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.},
 bibtype = {article},
 author = {Klein, Georg and Murray, David},
 journal = {Sixth IEEE/ACM International Symposium on Mixed and Augmented Reality, ISMAR 2007, 13-16 November 2007, Nara, Japan}
}

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