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PointCloudProcessor

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Commits

List of commits on branch master.
Unverified
bdf2be11aaea2b64cdf1b7469694bd8dc90ed00a

Added more ROS logging messages to document the stages of setting up the data feed, visualizer, and publisher. Helps with debugging.

committed 10 years ago
Unverified
68d0e92cd5843c1e51ae688e7454db80dd9ba981

Added more ROS logging messages to document the stages of setting up the data feed, visualizer, and publisher. Helps with debugging.

committed 10 years ago
Unverified
054345de09e84d42ea9d59074c146adb77bae7a3

Found some possible memory leaks and cleared them up. Updated the constructor to include an argument to let the user decide if they want to publish teh incoming data via a rOS publisher or if they would also like to visualize that data in real time with a cloud viewer object.

committed 10 years ago
Unverified
4bfa21d11018cb07738d3afff2183f857c5e90b1

Changed around some formatting

committed 10 years ago
Unverified
02ba92eb4ab7f5e5313e377cb8b9b4c266a7c229

Changed around the way ROS interacts with the program. The CloudGrabber class now has it's own spin() call inside to keep the thread going, so no longer is a ros::spin() call needed inside the file where the class is instantiated. Also, we removed the separability of the publishing and data grabbing/visualization functionality. Now the user just calls CloudGrabber::start() and this will call startFeed() and startPublishing(). The program is now set up to publish at user defined intervals, instead of just when they push a button. The publishing can be toggled on or off by pressing the 'p' button with focus on the visualization window.

committed 10 years ago
Unverified
e352188a7b4dfa82fd10aa1c27e566ad905869a3

Formatting updates

committed 10 years ago

README

The README file for this repository.

PointCloudProcessor

This program is a real attempt to make a program that combined the Point Cloud Library, OpenNI, OpenCV, and ROS. It's goal is to design modular and reusable classes and files to import data from a kinect or other range camera, then convert this data into a point cloud. This point cloud is displayed in real time to the user, who can then choose to save the current point cloud for editing. When the user saves the current PC, the data gets piped to another program via the ROS publish and subscription functions. This next program will import the PCL data and allow the user to run this data through various filters and statistical outlier removers.