Parallel Tracking and Mapping for Small AR Workspaces



This is the first work splitting tracking and mapping into two separate tasks in the literature.

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 optimization 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 rivaling that of state-of-the-art model-based systems.

  1. Klein, Georg, and David Murray. "Parallel tracking and mapping for small AR workspaces." 6th IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR 2007).
  • Active Vision Laboratory, Department of Engineering Science, University of Oxford

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