This tutorial will focus on installing Galactic Geochelone on Ubuntu Focal.

Though it’s possible to install ROS 2 from source or archive, the easiest way to get ROS running on your Ubuntu machine is to install its corresponding Debian packages using apt, a command line utility for installing and managing packages on Linux distributions.

In this tutorial, we’ll walk you through installing the Debian packages for ROS 2 Galactic Geochelone on Ubuntu Focal (20.04.4 LTS). If you are on Ubuntu Jammy (22.04 LTS), the latest LTS release, you can opt for ROS 2’s Rolling Ridley or Humble Hawksbill release instead.

Set up your source repos

Ubuntu programs are stored in repositories that make installing new software easy and secure. Ubuntu’s four main repositories are as follows:

Main (Canonical-supported open-source software)

Universe (community-maintained open source software)

Restricted (proprietary device drivers)

Multiverse (proprietary software)

For the purposes of installing ROS, we need to enable the Ubuntu Universe repository:



Now you can add the ROS 2 repository to your system. Authorize the public GPG (GNU Privacy Guard) key provided by ROS, then add the ROS 2 repository to your sources list:



Install ROS 2 packages

Install your ROS 2 Galactic desktop setup with the following commands:



This will install a few different components like the core ROS libraries, developer tools like RViz, and a set of beginner-friendly tutorials and demos to help you get started.

Source your setup file

Typically, you would need to source your setup file every time you open a terminal to use ROS:



To save yourself the time, you can automatically trigger this step every time you launch a new shell. Run the following commands if you’re using a bash shell:



Run the following commands if you’re using a zsh shell:



Run some examples

After completing the desktop install, let’s make sure that our APIs are working properly.

\*\*Run the example C++ talker \*\*at /opt/ros/galactic/lib/demo\_nodes\_cpp/talker:



The output should confirm that the talker is successfully publishing messages:



In another terminal window, \*\*run the example Python listener \*\*at /opt/ros/galactic/lib/demo\_nodes\_py/listener:



The output should confirm that the listener is hearing the published messages:



Both the C++ and Python APIs are working properly!



NOTE: If you’re experiencing issues here, make sure that you’ve enabled multicast – the network interface needs this in order to communicate via DDS.

Run the following commands in two separate terminal windows:



If the first command does not return a successful output (i.e. Received from xx.xxx.xxx.xx:43751: 'Hello World!'), try updating your firewall configuration:



Then, restart your terminal and try starting up your talker and listener again.

What’s next

Continue with the official ROS 2 tutorials to configure your environment, record data, and create a workspace and packages.

To uninstall ROS 2, remove the repository from your system completely:



Visualize and analyze your data with Foxglove

Once you’ve gotten the lay of the land and started recording your robots’ data, you’ll need to start visualizing your data to understand what’s going on under the hood of your robots.

This would be a good time to check out the specs for the MCAP file format. Not only are MCAP files smaller (and more performant!) than standard ROS 2 files, they’re also completely self-contained (unlike ROS 2 files that draw from message definitions living on your hard drive), making it possible for you to share these files with teammates for them to visualize. By adhering to a common container format, the MCAP file format encourages interoperability among third-party developer tools like Foxglove Studio and Foxglove Data Platform.

Foxglove provides a convenient mcap CLI tool to help you convert your ROS 2 (.db3) files into MCAP (.mcap) files. These files can then be imported directly into Foxglove Data Platform, so your whole team can collaborate on organizing and tagging your data:

Foxglove Studio

They can also be dragged-and-dropped into Foxglove Studio for instant rich visualizations. Load up your data to view camera images, display 3D markers, review log messages, and more:

Foxglove Data Platform

Join the Foxglove Slack community to ask questions and learn more.



Additional resources

ROS 2 Galactic docs and tutorials

Ubuntu repositories

ROS 2 distribution variants

MCAP file format

Foxglove Studio docs

Foxglove Data Platform docs


In my spare time for the past several months, I’ve been developing an autonomous RC car for DIYRobocars – a community of makers who race their DIY autonomous cars of all sizes. At the Robot Block Party hosted by Silicon Valley Robotics this past April, my car came in second in the Robocar race and also won the demolition derby.

My robocar taking out the competition at the demolition derby.

As you might expect, a good data logging and visualization infrastructure was crucial to helping me develop my successful autonomous driver. It took several iterations and a few false starts before I landed on my final solution – using Foxglove’s MCAP C++ reader-writer with Protobuf messages. I found the MCAP writer to be a solid foundation for my logging architecture, affording me a lightweight solution for both out-of-the-box support and open-ended flexibility.

Initial experiments with Python and ROS

I decided against using ROS in my robocar stack for a variety of reasons. For one, I wanted to write most of the core software from scratch – it’s part of the fun! I also wanted to get started quickly, and I decided that the time I’d invest in learning the ecosystem probably would not pay off for me in my future projects.

Furthermore, it wasn’t feasible to simply import the rosbag writer into my existing C++ build. Getting ROS working with my C++ build would likely have been a nightmare, given my desire to both cross-compile and run Raspbian for the Raspberry Pi (instead of Ubuntu).

In my first foray into logging and visualization, I used a custom binary format to log video frames and CSV lines to log sensor data, PID control outputs, and all other data. I then wrote a Python script, leveraging Python’s dependency management and standalone rosbag writer, to export ROS 2 files that could then be read by Foxglove Studio.

This scheme quickly fell apart. Schema evolution for logged messages and topics was manual, tedious, and error-prone. Older log files quickly became unreadable. The additional Python conversion step also made things more complicated and annoying.

With this first failed experiment, I tried introducing minimal dependencies on sqlite and FastCDR, which let me cobble together my own rosbag2 writer. This let me natively log rosbag2 files which could then be viewed directly in Foxglove Studio.

However, there still remained a few pain points. With this direction, I was maintaining a significant amount of code – the rosbag2 writer and especially reader – that didn’t add much core value to my autonomous car. Since FastCDR’s IDL is not widely adopted, I had to write my own definitions for the standard ROS 2 messages I wanted to publish. Foxglove Studio also could not support custom ROS 2 messages, due to an inherent ROS 2 design decision. Finally, there was no easy way to do compression, which inevitably led to unwieldy data logs and longer data offloading times.

Using MCAP with Protobuf messages

Around this time, Foxglove released an early version of their MCAP reader and writer, with C++ and Python support (they currently also support TypeScript, Go, and Swift). Given the hurdles I’d encountered with my previous iterations, I decided to give the library a try, using custom Protobuf messages.

I immediately noticed a few advantages. For one, Protobuf is well documented, with robust C++ libraries and backwards- and forwards-compatible semantics for message schema evolution. I also found myself enjoying just how easy it was to integrate MCAP into my stack. It is a small library with relatively few dependencies, yet surprisingly flexible. I was able to leverage Foxglove’s already-defined protobuf message schemas for standard ROS messages, but also define my own custom messages. I also found the ability to tune compression settings incredibly useful for reducing the size of my data logs and for accelerating data offloading.

The only con I found was that with MCAP being fairly new, there hasn’t been much widespread adoption yet. With that said, I think anyone else who gives the library a try will have as positive of an experience as I had.

Creating an MCAP writer in C++

An MCAP file has messages, channels, and schemas.

In the same way that ROS publishes messages to a topic, MCAP writes messages to a channel. Messages for a given channel always follow the same schema, but a particular schema can be shared by multiple channels.

Creating an MCAP writer is relatively straightforward:



Configure your writer to your desired specifications using McapWriterOptions. For example, opts.compressionLevel = mcap::CompressionLevel::Fast customizes your writer to use a faster compression level.

Before we can write messages, we need to register a schema and a channel to write our messages to.

To register a schema in Protobuf, you must use the fully-qualified name of the message type (e.g. ros.nav\_msgs.Path) and provide a serialized google::protobuf::FileDescriptorSet for the schema itself. Generated Protobuf messages will contain enough information to reconstruct this FileDescriptorSet schema at runtime.



Once we have the schema registered, we can register our channel:



We can now finally write messages to the channel via its id:



Don’t forget to close the writer when you’re done:



Now, we can inspect our output MCAP file's messages. I used the Data source dialog in Foxglove Studio to “Open local file”:

Data source dialog

I then added a few relevant panels (Plot, Image, Raw Messages, 3D) to visualize my robocar's performance on the "road".

Foxglove Studio replaying the exact moment my robocar collided with a demolition derby competitor.

Conclusion

I found Foxglove’s MCAP writer to be a solid building block when building my robocar’s data logging system. I used it in tandem with Foxglove Studio to quickly publish, visualize, and iterate on my robot – all by getting a better understanding of the data it recorded.

I was even able to add several bespoke enhancements specific to my project. I wrapped the MCAP writer in a layer that serialized Protobuf messages, wrapped them in a mcap::Message, and registered schemas for unknown Protobuf types in one centralized place. I also moved mcap.write to a separate thread fed by a queue, which avoided the time-sensitive driver control loop being disturbed by the long-tail filesystem i/o latency observed on the Raspberry Pi 3.

If you find yourself unhappy with your current data logging solution, I encourage you to give MCAP a try – it certainly has turned out well for me!


This tutorial will focus on installing Noetic Ninjemys, the latest LTS release for ROS 1, on Ubuntu Focal.

Though it’s possible to install ROS 1 from source, the easiest way to get ROS running on your Ubuntu machine is to install its corresponding Debian packages using apt, a command line utility for installing and managing packages on Linux distributions.

In this tutorial, we’ll walk you through installing the Debian packages for ROS 1 Noetic Ninjemys on Ubuntu Focal (20.04.4 LTS). If you are on Ubuntu Jammy (22.04 LTS), the latest LTS release, you can opt for a ROS 2 installation instead – both Rolling Ridley and Humble Hawksbill can run on Ubuntu Jammy.

Set up your source repos

Ubuntu programs are stored in repositories that make installing new software easy and secure. Ubuntu’s four main repositories are as follows:

Main (Canonical-supported open-source software)

Universe (community-maintained open source software)

Restricted (proprietary device drivers)

Multiverse (proprietary software)

For the purposes of installing ROS, we need to enable the Ubuntu Universe, Restricted, and Multiverse repositories:



Now you can set up your sources to accept software from packages.ros.org and add the PGP key provided by ROS:



Install ROS 1 packages

Install your ROS 1 Noetic desktop setup with the following commands:



This will install a few different components like the core ROS libraries, developer tools like RViz, and a variety of 2D/3D simulators and perception packages.

To install an additional ROS package separately, you can follow the pattern of $ sudo apt install ros-noetic-PACKAGE:



Install additional dependencies

To create and manage your own ROS workspaces, you'll need to install a few extra dependencies:

rosdep – Installs system dependencies for source you want to compile and runs some core ROS components

rosinstall – Downloads source trees for ROS packages

rosinstall-generator – Generates rosinstall files with information about repositories containing ROS packages

wstool – Maintains a workspace of projects from multiple version control systems

build-essential – Links to packages crucial to compiling software



Source your setup file

Typically, you would need to source your setup file every time you open a terminal to use ROS:



To save yourself the time, you can automatically trigger this step every time you launch a new shell. Run the following commands if you’re using a bash shell:



Run the following commands if you’re using a zsh shell:



What’s next

Continue with the official ROS 1 tutorials.

To uninstall ROS 1, remove the repository from your system completely:



Visualize and analyze your data with Foxglove

Once you’ve gotten the lay of the land and started recording your robots’ data, you’ll need to start visualizing your data to understand what’s going on under the hood of your robots.

Your ROS .bag files can be imported directly into Foxglove Data Platform, so your whole team can collaborate on organizing and tagging your data:

Foxglove Studio

They can also be dragged-and-dropped into Foxglove Studio for instant rich visualizations. Load up your data to view camera images, display 3D markers, review log messages, and more:

Foxglove Data Platform

Join the Foxglove Slack community to ask questions and learn more.



Additional resources

ROS 1 Noetic docs and tutorials

Video tutorial

Ubuntu repositories

ROS 1 distribution variants

Foxglove Studio docs

Foxglove Data Platform docs


Installing ROS 1 Noetic on Ubuntu

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