Evaluating pcl/registration

This is a collection of ideas on how to build an evaluation framework of pcl/registration.

Data generation

  • synthetic data

  • real word data (how to get ground truth?) - Kinect - PR2 laser scanner - SICK laser data - small range 3D scanner - mid range 3D scanner (Faro) - high end 3D scanner (Riegl, Velodyne)

  • Point Types - 2D(?) - 3D - RGB

  • dynamics - static scans - scanning while driving (e.g. robots)

  • size - room - building - outdoor (street)

Architecture

  • some lib for polygonal data

  • modeling different sensors

  • modeling noise

  • add a trajectory file

  • output a pile of .pcd files

  • integrate command line tools from PCL grandfather

Evaluating different algorithms

ICP

  • how does the algorithm cope with outliers

  • how are the point pairs evaluated:

    • does it use normal or RGB information

    • does it weight the pairs differently

    • which kind of point pairs are used:

      • one-to-one

      • one-to-many

      • many-to-many

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