29 posts tagged with "tutorials"

ROS2 is widely used for building robotic systems with sensors like cameras, LiDAR, and IMUs. While it's great for communication (e.g., publishing and subscribing to topics), it lacks a built-in solution for storing large amounts of unstructured data, such as images.

Bag files are commonly used to store data in ROS2, but they aren't a good fit for long-term storage or real-time streaming. They're mainly meant for recording and replaying mission data or episodes, not for managing large volumes of unstructured data.

Addressing this challenge, this blog post will guide you through setting up ROS2 with ReductStore a high-performance storage and streaming solution optimized for unstructured, time-series data.

We will focus specifically on image data, but if you are interested in a more general overview you can read How to Store and Manage Robotic Data which covers the challenges and strategies for storing and managing robotic data in general.

For the full code example, we will be using the reduct-ros-example repository, which provides a complete implementation of the concepts discussed in this article.

Your robot has a camera. You are storing what it sees as one image per frame, and that works until someone asks to watch it. Then you write a script that extracts ten thousand JPEGs from storage, sorts them by timestamp, and calls ffmpeg. You write that script again the next time anyone wants thirty seconds of footage.

Storing the encoded stream removes that step. ReductStore stores H.264 chunks as time-indexed records, and the ReductVideo extension returns an MP4 you can open in a player.

If you're running Zenoh, whether as rmw_zenoh in ROS 2 or as the transport between a robot, a gateway, and the cloud, you'll eventually run into the same question:

"Where does this data actually go? I need to replay yesterday's run, debug a fault from a remote robot, and pull a few thousand frames for training."

There's plenty of material on the communication layer itself: pub/sub primitives, how Zenoh compares to DDS, multi-node setups down to a Raspberry Pi. rmw_zenoh reached Tier-1 status starting with ROS 2 Kilted Kaiju, and it's getting real attention at events like ROSCon.

What's harder to find is the next layer: what happens to a sample after it's published. Connecting a live Zenoh network to something persistent and queryable usually means working around the limits of Zenoh's existing storage options.

This post shows another way. By the end you'll have a Python publisher sending data over Zenoh, a storage backend persisting it automatically, and a query client pulling exact time ranges back out, all without a custom backend or a bridge process.