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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

My python script,, 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

pointcloud registration

$ rosmake register_pointclouds $ roscore $ rosrun register_pointclouds register $


Running the original data set through pointcloud registration only: File:RegistrationOriginal.png 500px

Running the data set through pose registration only:

Running the data set through pose registration and then pointcloud registration: File:RegistrationPrecomputed.png 500px

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.