News Articles

We are proud to to announce the release of python-pcl Python bindings for PCL.

Now you can use the power and performance of PCL from the comfort of Python. Currently the following features of PCL, using PointXYZ point clouds, are available;

• I/O and integration; saving and loading PCD files
• segmentation
• sample consensus model fittting (RANSAC + others, cylinders, planes, common geometry)
• smoothing (median least squares)
• filtering (voxel grid downsampling, passthrough, statistical outlier removal)
• exporting, importing and analysing pointclouds with numpy

An simple demonstration showing the statistical outlier filter:

import pcl
p = pcl.PointCloud()
p.from_file("table_scene_lms400.pcd")
fil = p.make_statistical_outlier_filter()
fil.set_mean_k (50)
fil.set_std_dev_mul_thresh (1.0)
fil.filter().to_file("inliers.pcd")

For a more complete example showing how to combine filtering, plane and cylinder segmentation (the code used to generate the logo above), see this example.

For more information please see the examples, tests,…

 

It is our immense pleasure to announce the beginning of a new partnership between Open Perception and Leica Geosystems, and a series of new code sprints: PCL-LGSCS!

We are looking for talented contributors that are willing to develop open source efficient compression mechanisms for organized and unorganized 3D point cloud data in two separate code sprint projects. In both cases, the ultimate goal is to achieve as much compression as possible, but we will require an analysis with respect to the de/compression speed that can be obtained. A requirement is to be able to process a few million points per second on a standard laptop. In addition we will analyze and compare different lossy vs lossless compression techniques.

The data sources are variate, from terrestrial to mobile and aerial point clouds. Besides XYZ coordinates we expect the datasets to contain an additional intensity and/or color per point. Each dataset will contain standard meta information, and in the case of lossy compression, we will need to specify certain error limits to be satisfied.

The organized data format consists of a series…

PCL has a nice data repository, that contains point clouds in the PCD format, which can be easily read using PCL tools. However, there are many other sites with free downloadable data (in various formats). The following is a short list of such sites containing links to data that is free to use for educational work; for other purposes you should see licenses for detail. Also, if you wish to add to this list feel free to submit to the forum or email me directly (hielsber).

  - Robotic 3D Scan Repository
  - Radish: The Robotics Data Set Repository
  - Canadian Planetary Emulation Terrain 3D Mapping Dataset
  -

Happy New Year fellow PCL-ers! We are proud to announce that 1.7 years after our launch as a stand-alone project, we boast impressive stats:

• more than 100 million hits on our domains
• more than half a million unique visitors
• more than 500 contributors and developers

The last year was also marked by the launch of our non-profit foundation: Open Perception. This allows us to make it easy for our developers to get paid for their awesome work in the field of open-source 3D perception. As PCL is gathering more and more attention in the worlds of computer vision, robotics and digital entertainment, we are being flooded with projects and full-fledged job offers for our community. These offers come in three flavors:

• code sprints - 1 to 6 month paid jobs that you can do from the comfort of your home/school, for which a company sponsors you to work on open-source development for a project of their interest. (Developer blog)
• actual full-time jobs and internships in companies that are collaborating with PCL/OP and/or using PCL in their products, and would like to hire experts from the community. (Jobs page)
• invitation-based projects that are not publicly…

The joint Open Perception-Ocular Robotics code sprint is ready to start! The sprint will cover an efficient pcl::Grabber driver interface for the RE0x laser sensors, as well as various enhancements to our PCL visualization libraries to be able to handle both larger datasets as well as process data packets coming from these sensors faster. The developer working on the sprint is Pat Marion from Kitware. Pat is already a seasoned PCL coder and has contributed significantly to the iOS and Android port of PCL (see this for more information) and the PCL plugin for ParaView.

We would like to thank all the other candidates for their excellent proposals! We already started following up with each of them individually, as there are many more sprints to come!

[Note:…

PCL-VLCS is ready to start! The sprint will cover a Plug-n-Play interface for the Velodyne HDL series to make these sensors much easier to use and the high-density point clouds easily accessible by developers. The developers working on the sprint are Keven Ring from MITRE and Kuk Cho from Korea Institute of Industrial Technology.

We would like to thank all the other candidates for their excellent proposals! We already started following up with each of them individually, as there are many more sprints to come!

[Note: the new blogging page for VLCS will be up within the next few days at http://pointclouds.org/blog/vlcs/.]

Project Description & Motivation: Velodyne's HDL-32 LiDAR sensor features a 360° unmatched vertical field of view with a range of 100 meters and typical accuracy of ±2 cm. Rotating at 10 Hz, this sensor produces upwards of 700,000 points per second over an Ethernet connection in UDP packets from 32 individual lasers and sources. GPS information is also be included and used for clock synchronization. Capturing and converting these packets into usable points is something that, until now, customers in academia and industry, have done themselves with code integrated from their own research projects. Velodyne wishes to significantly expand the reach and audience for the HDL sensor products by making the these sensors Plug-n-Play -- attach the sensor to a system, load the drivers, and capture point clouds in PCL formats within minutes - thereby leveraging the significant and growing availability of PCL viewers.

The most apt description of this sprint is that we want to make the HDL series into the "Kinect- equivalent" for LiDAR systems -- a Plug-n-Play interface will make these sensors much easier to use and the high-density point clouds easily accessible by developers. The advantages…

It is our immense pleasure to announce the beginning of a new PCL Code Sprint sponsored by Ocular Robotics: PCL-ORCS!

Ocular Robotics is looking for talented contributors that are willing to develop an efficient pcl::Grabber driver interface for the RE0x laser sensors.

PCL-ORCS will run for 3 months during this fall. Potential candidates should submit the following information to jobs@pointclouds.org:

• a brief resume
• a list of existing PCL contributions (if any)
• a list of projects (emphasis on open source projects please) that they contributed to in the past

This project requires good C++ programming skills, knowledge of PCL internals and a basic understanding of laser sensors and 3D visualization.

We are happy to announce that the Fraunhofer Institute for Physical Measurement Techniques (IPM) and Open Perception (OP) are partnering for the purpose of Point Cloud Library (PCL) development.

Fraunhofer IPM develops tailor-made measuring techniques, systems and materials for industry. The Railway Measurement Group within IPM is developing advanced optical measuring systems for monitoring the condition of rail and road infrastructure. The group has been specialized on laser-based sensor systems, e.g. high speed laser scanner unit.

To capture the environment of a railway track, trains which are equipped with laser scanners are used. A new conceptual design developed by IPM extends the common setup by scanners which are capturing data ahead in driving direction. IPM selected the Point Cloud Library (PCL) project as their algorithmic core for optimizing the operating process and their software infrastructure.

For this task we are looking for two master students. Please see the images/PDFs below for more detailed information.

…

The Georgia Tech Multi-Agent Robotics and Systems Lab (www.bio-tracking.org) is pleased to announce the release of their bio-tracking software, The Biotrack Pack. This open-source project makes use of our own Point Cloud Library, as well as other open source software such as OpenCV and QT.

If you would like to stay updated and receive more announcements about the Biotrack Pack, please subscribe to announce@bio-tracking.org via the following link: http://www.bio-tracking.org/sign-up-for-bio-tracking-announcements/…