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Learning Compact Visual SLAM Representation with CodeSLAM

Explore a novel representation for dense visual SLAM using code learning, enabling efficient reconstruction of high-frequency details. This alternative to depth maps is beneficial in SfM or SLAM scenarios, with an architecture that includes color feature extraction and uncertainty prediction. Learn about the benefits of variational autoencoders and small code changes for depth reconstruction accuracy.

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Learning Compact Visual SLAM Representation with CodeSLAM

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Presentation Transcript

  1. Vision Reading Group, 2nd of May 2018 Martin Rünz CodeSLAM — Learning a Compact, Optimisable Representation for Dense Visual SLAM

  2. In a nutshell An alternative representation to depth maps is presented

  3. Background – Common Map Representations Surfels Mesh Pose-Graph + Depth-Maps TSDF

  4. Ideas • Depth-maps are notrandom

  5. Ideas • Depth-maps are notrandom(especially in man-made environments)

  6. Ideas • Depth-maps are notrandom • A compact representation – code – can be learned by encoder • Decoders are differentiable → code can be optimised (w.r.t. photometric loss...) • This is useful in SfM or SLAM scenarios • Reconstruct high-frequency details from color

  7. Depth Reconstruction • Depth-from-mono • Depth-from-mono + code • Given this differentiable function, warping constraints can be used to optimise c and pose

  8. Architecture Color feature extractor + uncertainty predictor Laplace distribution Only for training, ground-truth depth • Variational autoencoder, to increase smoothness of mapping between code and depth • Small changes in code → small changes in depth Decoder: CODE

  9. Video

  10. Inverse depth parametrization average • Error behaves moreGaussian original depth

  11. Warping Transform → B 3D Photometric error: • Both functions differentiable to inputs Expensive(convolutions) Pre-computed if decoder is linear!

  12. Application • SfM • Initialise poses and code with zero-vector • Use residuals + Jacobians in Gauss-Newton style optimisation • Cost function: • Functionality of : • Mask invalid correspondences • Relative weighting • Huber weighting • Down-weight slanted surfaces • Down-weight occluded pixels

  13. Experiments • SfM

  14. Experiments • SfM

  15. Experiments • SLAM

  16. Experiments • Setups

  17. Experiments • Setups

  18. Experiments • Influence code entries

  19. Video

  20. Thanks for listening!

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