At Foxglove, our guiding mission is to accelerate robotics development for everyone. With Foxglove Studio, we tackled data visualization and debugging; with Foxglove Data Platform, we tackled data storage and management.

For the last several months, our team has been working to streamline and standardize another key component of the robotics stack: data recording. We noticed many companies spending valuable in-house resources to develop custom file formats, only to create more future work and complicate third-party tool integrations. That’s why we’re thrilled to introduce MCAP (pronounced “em-cap”), a standardized container format for storing heterogeneous robotics data. With MCAP, teams are empowered to focus on their important robotics problems and avoid wasting precious time building commodity tools.

Recording robotics data before MCAP

Which format robotics teams use to store their log data today depends largely on their framework. Those using ROS 1 default to the “bag file” format; those on ROS 2 default to a SQLite-based format. Companies that don’t use ROS at all often employ a custom in-house binary format such as length-delimited protobuf, or store their messages as opaque bytes inside existing file formats such as HDF5.

These existing storage options have several shortcomings. The ROS 1 bag format is difficult to work with outside of the ROS ecosystem, while the ROS 2 SQLite format is not fully self-contained, making it difficult for third-party tools to read. Custom in-house formats lack interoperability and require developing corresponding libraries in multiple languages to read and write files.

We evaluated many existing data storage formats in the industry and identified a clear need for a general-purpose, open source data container format – specifically optimized for robotics use cases. Designing this format would not only solve an industry-wide problem, but also make it easier for teams to leverage third-party tools and share their own tooling.

Benefits of recording with MCAP

As a container format, MCAP can store message data encoded in a variety of serialization formats. While message serialization formats (e.g. Protobuf, MessagePack, JSON, etc.) translate your data into bits for efficient storage, a container format can embed multiple heterogeneous data streams, with corresponding metadata, into a single file.

MCAP is optimized for high-performance writing – because it leverages an append-only structure, data can be streamed to disk or over the network without seeking backwards. This makes it possible to recover partially-written files, even if on-device recording was interrupted by a software crash or power loss. MCAP files are also completely self-contained – message schemas are embedded in the file, and no additional dependencies are required for decoding. They also support the ability to embed attachments containing calibration information, core dumps, or any other application-specific data.

MCAP is also designed to support efficient reading and seeking within files, even over remote connections. Similar to ROS 1 bag files, MCAP supports indexed reading of messages and fast local or remote access to summary data. MCAP readers can efficiently extract data for a given topic and timestamp without having to scan the entire file.

Try MCAP today

MCAP is currently in beta, and we’d love your feedback! Check out our GitHub repo to get started, and share your thoughts in our Slack community to help us build towards v1.0.

If you’re currently using an in-house recording format or storing binary data directly, MCAP could be a great alternative to consider. We provide libraries in C++, Python, Go and TypeScript, as well as a suite of conformance tests, to facilitate interoperability with other tools. We also support visualizing MCAP files in Foxglove Studio, as well as importing them into Foxglove Data Platform.

If you’re currently on ROS, use our mcap CLI tool to convert your .bag (ROS 1) or .db3 files (ROS 2) to self-contained MCAP files that include all the necessary message schema definitions. You can also track progress on our ROS 2 MCAP plugin, coming soon.

Special thanks

We want to extend special thanks to Emerson Knapp, Michael Orlov, and other members of the ROS Tooling Working Group for collaborating with us on the MCAP spec. This work would not have been possible without your feedback and contributions.


We’re excited to announce that Foxglove has been invited to join the ROS Technical Steering Committee (TSC). ROS (Robot Operating System) is the leading robotics software development framework, and the ROS TSC is responsible for its roadmap and technical direction, developer policies and process, and other matters that require technical supervision.

Prior to founding Foxglove, my cofounder Roman and I were early employees at Cruise. There, we spent years working on one of the hardest technical challenges of our generation: deploying fully autonomous vehicles on the streets of San Francisco. Cruise’s software was originally built with ROS, giving us unique insights into the challenges of scaling ROS development across areas as diverse as the build system, data recording, visualization, fleet management, and deterministic replay.

We founded Foxglove in 2021 with a team of industry experts, to accelerate development in the robotics and automation industry. We’re strong believers in open source, and have contributed to the community with our open source robotics visualization software, MCAP data format, and ROS 2 support for rosbridge. We’re thrilled to join the ROS TSC, and look forward to continuing to improve the robotics ecosystem!


Robot kits are a great way to learn the basics of robotics development, because you can leverage pre-built platforms to focus on the field's most exciting problems. There are several companies that curate, assemble, and provide these kits. Yahboom, for example, sells a range of educational robot kits for everyone from the casual hobbyist to professional researcher. After recently trying out their Transbot platform, I can say that I'm a big fan. Based on how easy it was to get up and running, I would recommend it to anyone interested in ditching the textbooks and getting their hands dirty with some hands-on development.

Assembling the Transbot

I ordered the Transbot kit with a Jetson NANO, HD camera, and Depth (LIDAR) camera. My Transbot and its accompanying sensors and accessories arrived in a conveniently insulated carrying case, along with a microSD card pre-installed with ROS.

Transbot case
Here I’ve taken out all the accessories to reveal the securely packed Transbot at the bottom of the shipped case.

First, I unpacked all the various accessories – a wireless controller with alkaline batteries included, a camera calibration card, the Jetson NANO, and more. The kit also includes a rechargeable battery for the bot, as well as an expansion board with all the motor and servo controllers.

Transbot accessories

I really appreciated that the bot comes 90% assembled – this lets you get started with the platform without struggling through complex assembly instructions. For the rest, the instruction manual (Chinese and English) was relatively easy to understand, especially for a kit of this complexity. I was able to assemble the entire bot within an hour – this involved mounting the camera and LIDAR assembly, the expansion board, and Jetson NANO on top of the base.

assembled Transbot
The Transbot base as shipped vs. after assembly.

Connecting your Transbot to Foxglove Studio

After assembling the physical hardware components, I realized that I needed software to help me understand what my robot was seeing and doing. I decided to connect my Transbot to Foxglove Studio, a cross-platform robotics visualization tool compatible with many robotics platforms, to inspect and debug my robot as I went.

By default, the Transbot sets up a Wi-Fi network. By connecting my laptop to this same network ("Transbot"), I am ready to talk directly to my robot's ROS system and inspect it with Foxglove Studio.

connecting to Transbot

Download the Studio desktop app – use the dialog that appears on app open to open a new ROS 1 connection (Open connection → ROS 1):

Studio's data source dialog

The ROS\_MASTER\_URI should be set to your Transbot's IP address. The ROS\_HOSTNAME should be set to your laptop's IP address on the "Transbot" WiFi network – this is how your bot's ROS nodes will send data to Studio on your laptop.

Transbot's OLED screen
Check your Transbot's OLED screen for its IP address.

You’re now ready to start visualizing your robot's data!

Visualizing your Transbot’s data in Foxglove Studio

In Foxglove Studio, let’s create a layout of panels that we'll use to visualize data our bot data. Open the Layouts menu from the app's sidebar, and add a new layout.

Next, let's add a 3D panel to this newly created layout to visualize the Transbot's LIDAR data, which the Depth camera collects via a 360° scan around the robot. Open the Add panel menu, also from the app's sidebar, to see a list of all available panels.

grid of panels in Studio

Once we’ve selected the 3D panel, let's toggle on the /scan topic in the topic picker. Let's also update the orientation controls on the right (base\_link → laser), so we can view the LIDAR data from the POV of the sensor.

We now see the LIDAR from our robot! The raw scan details match the room my Transbot is in – I can see outlines of the walls, door, and random boxes, as well as the areas that the sensor isn't able to "see".

zoomed in view of 3D panel

Before we drive the robot around with the included wireless controller, let’s set up some charts to plot the controller's velocity commands. Again, using the Add panel menu, add a Plot and Raw Messages panel. The Plot panel will show me the history of values, while the Raw Messages panel will display the current value, for a given message path. In the Plot panel, let's plot /cmd\_vel.linear.x and /cmd\_vel.angular.z over time. In the Raw Messages panel, let's inspect the /cmd\_vel topic.

final Studio layout

Now, driving the robot with your controller joysticks will publish those commands to the /cmd\_vel topic. When moving the joysticks on the controller, we can see the values in the Plot and Raw Messages panels change.

Try adding other panels (like the Diagnostics and Log panels) to view other data being published by your robot!

Get support on your robotics journey

If you liked this review, be sure to also check out our review of Duckiebot, another educational robotics platform geared towards robotics beginners.

Whether your goals are to develop the next great SLAM algorithm, conduct vision processing, or build a new control loop, a robotics platform like Duckiebot or the Yahboom Transbot will give you a great jumpstart on the process.

Join us on Slack to ask questions, give feedback, and get other recommendations on the other robot kits you should try!


Review: Getting Started with the Yahboom Transbot

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