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