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Real-Time and Robust Monocular SLAM Using Predictive Multi-resolution Descriptors

  • Denis Chekhlov
  • Mark Pupilli
  • Walterio Mayol-Cuevas
  • Andrew Calway
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4292)

Abstract

We describe a robust system for vision-based SLAM using a single camera which runs in real-time, typically around 30 fps. The key contribution is a novel utilisation of multi-resolution descriptors in a coherent top-down framework. The resulting system provides superior performance over previous methods in terms of robustness to erratic motion, camera shake, and the ability to recover from measurement loss. SLAM itself is implemented within an unscented Kalman filter framework based on a constant position motion model, which is also shown to provide further resilience to non-smooth camera motion. Results are presented illustrating successful SLAM operation for challenging hand-held camera movement within desktop environments.

Keywords

Augmented Reality Scale Invariant Feature Transform Erratic Motion Search Region Unscented Kalman Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Denis Chekhlov
    • 1
  • Mark Pupilli
    • 1
  • Walterio Mayol-Cuevas
    • 1
  • Andrew Calway
    • 1
  1. 1.Department of Computer ScienceUniversity of BristolUK

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