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Machine Learning for Robotics
TUM Department of Informatics
Technical University of Munich

Technical University of Munich

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Informatik IX

Professorship for Machine Learning for Robotics

Smart Robotics Lab

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

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Machine Learning (Including Deep Learning)

Volumetric Occupancy Mapping With Probabilistic Depth Completion

We have been using Machine Learning, often Deep Learning, extensively as part of SLAM approaches, particularly in mapping. This includes semantic segmentation and instance segmentation, as well as prediction of geometry, e.g. image-based depth prediction and depth completion. Furthermore, understanding uncertainty of predicted quantities allows for robust interfaces with other components running on-board a mobile robot.

As an example of most recent works, we are performing depth completion with a Convolutional Neural Network that also learns to predict depth uncertainty, which is then readily used in probabilistic occupancy mapping. Such systems may allow a robot to safely navigate into space where no depth camera measurements are available (e.g. because the structure is too far away), while maintaining the ability to accurately map the area where actual measurements are available.

Current collaborators:

  • Dr Marija Popovic (previously SRL at Imperial, now University of Bonn)
  • Florian Thomas (previously SRL at Imperial)
  • Sotris Papatheodorou (SRL at Imperial)
  • Nils Funk (SRL at Imperial and SLAMcore)
  • Teresa Vidal-Calleja (Sidney University)


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2020
Preprints
[]Efficient Volumetric Mapping Using Depth Completion With Uncertainty for Robotic Navigation (M Popovic, F Thomas, S Papatheodorou, N Funk, T Vidal-Calleja and S Leutenegger), In arXiv preprint arXiv:2012.03023, 2020.  [bibtex]
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Informatik IX

Professorship for Machine Learning for Robotics

Smart Robotics Lab

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

Follow us on:
SRL  CVG  DVL