Registering pointclouds in ROS with PCL for Robot_Lab_(Spring_2012)
Paths followed, lessons learned, code shared.
Find some data
The data I used is dataset 14 at http://kos.informatik.uni-osnabrueck.de/3Dscans/
My python script, publish_from_file.py, can be easily adjusted to read any similar format, wherein each line gives the coordinates for a point. PLY is another option; PLY pointclouds can be loaded and viewed in Meshlab.
I didn't write any code to read pcl files, but this is the format that the pointcloud library reads and writes, so if you found some pcl data and like c++ that would also work.
Ideally, it would be best to take your own data, then you know everything about it. It turns out the better you know the data (how far did the robot move between images? what unit does your scanner use for measurements -- meters? pixels?) the better off you will be in fine-tuning the registration algorithms.
Learn about registration
The pcl site has a lot of information. I would recommend reading the introductory material on
Try my scripts
publish from file to ROS bus
$ rosmake register_pointclouds $ roscore $ rosrun register_pointclouds register $
The results never came out perfectly, but they do nicely show the effects of different types of translation on the data / stages in the process.
Running the original data set through pointcloud registration only:
Running the data set through pose registration only:
Running the data set through pose registration and then pointcloud registration:
Do something new
- Working odometry into the registration process appears to be key. It would be nice to figure out the best way to do it.
- I don't know whether / how it's possible to send odometry and pointclouds over the ROS bus and correlate them on the other side. Do they need to be combined into a single message? Can the callbacks on the two topics be tied together somehow? It would be easier to start by skipping the ROS bus altogether and just reading in the pointcloud and omdometry data from file.
- A new version of pcl should be coming out any day now as I finish this quarter. A new registration algorithm will take odometry data; it's called Normal Distributions Transform. That would be worth trying out.
- Further research on Euler angles and rotations in 3d space could be useful. I'm not certain that what I did in my python script is without error.
- Cleaning noise from the data could be helpful. I believe the circular artifacts in my images come from noise in the pointclouds. Without them, registration could be more accurate.