Chen 2018 SLAM-based dense surface reconstruction in MIS with AR

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Authors Chen et al

Abstract

  • Intra-operative dense surface reconstruction framework to provide geometry information from only monocular videos
  • The proposed framework works well with rapid camera movements, however is not suitable for large deformations
  • Only tweaks ORBSLAM to adjust between point density and computational performance

Contents/Chapters

Problems in medical AR:

  • tissue surface illumination
  • tissue deformation
  • rapid movements of the medical tool e.g. endoscope (s. also kidnapped robot problem for relocalisation, tracking mus therefore be robust)
  • field of view often very small

“A typical human uses 14 visual cues to perceive depth, only 3/14 are binocular vision related.” s. also: Monocular depth perception in humans

Traditional feature-tracking for AR in MIS:

  • SIFT
  • SURF
  • Optical flow tracking
  • Other approaches for soft tissues

However, these are designed for 2D tracking

--> ORB descriptor

  • binary feature descriptor
  • faster than SURF and SIFT with better accuracy
  • invariant to rotation, illumination and scale

ORB-SLAM uses Bag of Words algo. for relocalisation

Task of the SLAM algo in this framework:

  • Recovers camera POSE
  • Generates a sparse point cloud, based on which 3D geometric information is generated

Inialisation is problematic for monocular SLAM because the depth isn’t recoverable from a single image --> automatic approach in ORBSLAM:

  • for planar scenes: calculate homography
  • for non-planar scenes: calculate a fundamental matrix dynamically

Takeaway

Not suitable for large deformations