Progress for week 42 (2018)

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(Accounting)
Linje 20: Linje 20:
** Found out you can calculate inertia tensors with MeshLab
** Found out you can calculate inertia tensors with MeshLab
* Created CameraObjectLocalizer class, but have not compiled with D2CO yet.
* Created CameraObjectLocalizer class, but have not compiled with D2CO yet.
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== Anders Rønningstad ==
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=== Budget ===
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* Read about and understand how to use mathematical optimization packages in python.
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* Implement mathematical optimization in program.
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** Hopefully start to run tests.
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=== Accounting ===
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* Found out how to implement the optimization.
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** Ran some tests, with bad results.
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* Tried to implement the optimization using bruteforce, but computation was to slow for it to be useful.
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* Started to implement a self made method, which combines bruteforce and some assumptions.
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** Ran som tests using a simple version of the new method, with some better results.

Versjonen fra 19. okt 2018 kl. 07:08

Innhold

Jonas S. Waaler

Budget

  • Continue training dense neural networks of different sizes
  • Develop convolutional neural networks and train
  • Compare dense and convolutional, and write about it

David Kolden

Budget

  • Implement D2CO algorithm in a ROS node.
  • Create Gazebo simulation model of a camera.
  • Find a way to create a Gazebo model of a part that should be registered by the algorithm.
    • Find a way to calculate inertia from a CAD file.
  • Finish introduction of the essay.
  • Finish the robotic assembly section of essay.

Accounting

  • Personal page created.
  • Camera model implemented in ROS/Gazebo
  • Imported a part object into ROS/Gazebo from CAD file.
    • Found out you can calculate inertia tensors with MeshLab
  • Created CameraObjectLocalizer class, but have not compiled with D2CO yet.


Anders Rønningstad

Budget

  • Read about and understand how to use mathematical optimization packages in python.
  • Implement mathematical optimization in program.
    • Hopefully start to run tests.

Accounting

  • Found out how to implement the optimization.
    • Ran some tests, with bad results.
  • Tried to implement the optimization using bruteforce, but computation was to slow for it to be useful.
  • Started to implement a self made method, which combines bruteforce and some assumptions.
    • Ran som tests using a simple version of the new method, with some better results.
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