In this comprehensive guide, we will walk you through the process of installing PCL (Point Cloud Library) version 1.9 on Ubuntu 20.04. PCL is a standalone, large scale, open-source project for 2D/3D image and point cloud processing.
To install PCL 1.9 on Ubuntu 20.04, you need to enable the Universe repository, update the package list, and install the necessary packages using the apt command. Once installed, you can verify the installation by checking the version number and running pcl_viewer. If you specifically require version 1.9, you may need to build it from source.
Before we start, ensure that you have a running Ubuntu 20.04 system with superuser privileges.
Step 1: Enable the ‘Universe’ Repository
The first step is to enable the Universe repository, which is a free and open-source software maintained by the community. To do this, open your terminal and run the following command:
sudo add-apt-repository universe
You will be prompted to enter your password. This command adds the Universe repository to your list of apt sources.
Step 2: Update Your Package List
Next, we need to update our system’s package list to ensure we’re accessing the most up-to-date packages. This can be done using the
apt update command:
sudo apt update
apt update command fetches detailed information about the software packages from all configured sources, such as version number, size, and whether or not new versions of currently installed packages are available.
Step 3: Install the Necessary Packages
Now that our system is up to date, we can install PCL and its tools. Run the following command:
sudo apt install libpcl-dev pcl-tools
libpcl-dev package contains the PCL library and its development files, while
pcl-tools includes useful utilities for manipulating point cloud data.
Step 4: Verify the Installation
Once the installation process is complete, you can check the installed version of PCL by running:
dpkg -l libpcl-dev
dpkg -l command lists all packages matching the pattern
libpcl-dev. Look for the version number in the output.
To verify the installation of pcl-tools, you can run
pcl_viewer in the terminal. If the viewer opens without any errors, the installation was successful.
Building PCL 1.9 from Source
The above steps will install the latest version of PCL available in the Ubuntu repositories, which might not be version 1.9. If you specifically require version 1.9, you may need to build it from source. The process of building from source is beyond the scope of this article, but you can find detailed instructions in the PCL documentation.
We hope this guide has been helpful in installing PCL 1.9 on your Ubuntu 20.04 system. If you encounter any errors during the installation process, please provide the specific error messages for further assistance.
The steps provided in this guide are specifically for Ubuntu 20.04. The process may vary for other versions of Ubuntu.
To uninstall PCL, you can use the
apt remove command followed by the package name. For example, to uninstall
libpcl-dev, you can run
sudo apt remove libpcl-dev.
Yes, PCL can be used with various programming languages, including C++, Python, and MATLAB. However, the installation process may differ depending on the language you choose to work with.
Yes, PCL can be used for both 2D and 3D image processing. It provides a comprehensive set of tools and algorithms for point cloud and image processing tasks.
If you’re interested in contributing to the PCL project, you can visit their official website and join their community. They provide guidelines for contributing code, documentation, and bug reports.
Yes, PCL is capable of real-time processing. However, the performance may vary depending on the complexity of the algorithms used and the hardware capabilities of your system.
Yes, PCL supports a wide range of point cloud file formats, including PCD (Point Cloud Data), PLY (Polygon File Format), and LAS (LIDAR Data Exchange Format). It also provides tools for converting between different formats.
To learn more about using PCL, you can refer to the official PCL documentation, which includes tutorials, examples, and API references. The documentation is available on the PCL website.