PCL :: Point Cloud Library
This is a post on one of my current projects (that seems to keep me up late these days) called PCL, short for Point Cloud Library. First, thanks to everyone that e-mailed and expressed interest on helping out to develop PCL faster. We were pleasantly surprised at ICRA to see how many of you consider this important, and I was personally overwhelmed by some of the e-mails that I received after meeting with you. Again, sincerely thank you!
Because not a lot of people know why we are doing this or how this started, here’s a few random bits on PCL…
- PCL is a complete open source software package built as part of ROS, which stands for Robot Operating System/Robot Open Source. If you don’t know what ROS is yet, don’t worry, you will soon. We’re growing bigger everyday, and adding more functionality that will make your robots go “wheeeee” sooner than you think. Check out a few of them today to see them moving around, picking up objects, or simply just acting cool. ROS is a community driven open source effort (supported by Willow Garage) and the best way to think about it is a massive collection of tightly integrated software packages that enable robotics research and development. There’s probably over 100 useful packages in there, including OpenCV and OpenRave so don’t just take my word for it, go check it out yourself!
- PCL was created as a successor to an earlier attempt of designing PointCloud data types, structures and algorithms that proved to be inefficient. Those days are gone now, and we’re proud to announce PointCloud…2. Yes, our new underlying data type that represents point cloud data is _really_ called PointCloud2. Call it an uninspired day to name things if you wish. PointCloud2 was designed with PCL in mind (or vice versa - same thing ;), and supports alignment (e.g. SSE), has a compact data size, and has a lower computational overhead in general (especially when used with nodelets - more on that later).
- Because we want PCL to become for 3D and point cloud data processing what OpenCV is for 2D image processing, we are developing a large number of algorithms for operations such as: filtering, downsampling, 3D feature estimation, registration, surface reconstruction, segmentation, and many more, into PCL. To make things flexible and efficient on the long run, we designed PCL as a fully templated C++ library, with an Eigen backend, and lots of Boost goodies. Yes, porting it to an Android (or whatever) might be a bit of a challenge, but we’ll worry about that later. Right now, we just want robots to perceive and understand their surrounding environment better. The “robot sharing a cluster of cell phones” app will come naturally once we manage to correctly identify all the cell phones in a building
- We try to use explicit parallelization where possible in PCL, via OpenMP and TBB. We try to avoid parallelizing everything implicitly, because saving CPU cycles on those extra cores that your computer might have is a good thing ®. Basically you want to know when something is going all “parallel” and eating up all your resources on a robot. We ran into decent issues in the past, and learned a valuable lesson. I think.
- Because we know that learning something new is hard, we’re trying to help you out. There’s documentation, API documentation, tutorials, videos, and more. We already have examples for you on how to do object recognition, and environment modeling, to name a few. And you need to understand that we just started. PCL is roughly 3-4 months old, give or take.
There’s lots more to be said, but for now, just go check it out! If you’re asking yourselves why bother, well… why not? It’s open source, it’s fast, and it’s easy to use (or so we hope). If you don’t like it, just e-mail us and we’ll fix it for you. Yup, just like that!
Here’s a teaser for one of the many things that can be done as of today with PCL. Though the video was generated before PCL was conceived, all the algorithms that are needed to obtain these results are present in PCL. In fact, we’ll create a tutorial soon to show you how to do it too in case you get stuck.
Finally, don’t forget to check out the slides from our class at Stanford for more information about PCL and point clouds. I’ve also uploaded my ICRA 2010 PCL talk/presentation slides here.