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LSD-SLAM: Large-Scale Direct Monocular SLAM

  • Jakob Engel
  • Thomas Schöps
  • Daniel Cremers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8690)

Abstract

We propose a direct (feature-less) monocular SLAM algorithm which, in contrast to current state-of-the-art regarding direct methods, allows to build large-scale, consistent maps of the environment. Along with highly accurate pose estimation based on direct image alignment, the 3D environment is reconstructed in real-time as pose-graph of keyframes with associated semi-dense depth maps. These are obtained by filtering over a large number of pixelwise small-baseline stereo comparisons. The explicitly scale-drift aware formulation allows the approach to operate on challenging sequences including large variations in scene scale. Major enablers are two key novelties: (1) a novel direct tracking method which operates on \(\mathfrak{sim}(3)\), thereby explicitly detecting scale-drift, and (2) an elegant probabilistic solution to include the effect of noisy depth values into tracking. The resulting direct monocular SLAM system runs in real-time on a CPU.

Keywords

Augmented Reality Image Alignment Visual Odometry Convergence Radius Inverse Depth 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Jakob Engel
    • 1
  • Thomas Schöps
    • 1
  • Daniel Cremers
    • 1
  1. 1.Technical University MunichGermany

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