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Anthony Cavin
Data Scientist - ML/AI, Python, TypeScript

A data scientist specializing in machine learning, AI, Python, and TypeScript, with a strong interest in applying these technologies to data-driven projects and innovative AI solutions.

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How to Store and Manage ROS Data

· 23 min read
Anthony Cavin
Data Scientist - ML/AI, Python, TypeScript

 

At ReductStore, we specialize in the high-performance storage and streaming of robotics data from edge devices to the cloud. In this tutorial, we will demonstrate how to develop a robust ROS 2 data logging pipeline for practical robotics applications.

First, we will set up a Raspberry Pi with a USB camera running a lightweight YOLOv5n object detection model via ONNX Runtime. Then, a recorder node will capture selected ROS 2 topics, including images, detection results, and logs. Next, these topics will be saved as segmented MCAP files locally with ReductStore. Finally, we will configure automatic replication to stream data to another ReductStore instance.

This minimal setup shows how to efficiently capture, store, and replicate ROS 2 data from a robot to a central server or cloud instance. These techniques can be applied to any ROS 2 system, whether it's a single robot or a fleet of autonomous systems.

Let's get started!

How to Store Images in ROS2

· 11 min read
Anthony Cavin
Data Scientist - ML/AI, Python, TypeScript

 

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.

How to Choose the Right MQTT Database

· 16 min read
Anthony Cavin
Data Scientist - ML/AI, Python, TypeScript

Photo by Jan Antonin Kolar

Photo by Jan Antonin Kolar on Unsplash.

At a previous company, we used MQTT to send industrial data, such as vibration readings, images and log files. However, maintaining a history of this data proved challenging. Initially, we used a combination of a time-series database and an object store, but we struggled to ingest blob data quickly enough, and the system was difficult to maintain.

To help you avoid a similar experience, this article will recommend the most suitable database for your IoT or Industrial IoT (IIoT) project. We will look at different ways of storing data from IoT devices that communicate with each other via MQTT.

MQTT stands for Message Queuing Telemetry Transport and is a lightweight messaging protocol designed to be efficient, reliable, and scalable, making it ideal for collecting and transmitting data from sensors in real time.

Why is this important when choosing a database?

Well, MQTT is format-agnostic, but it works in a specific way. We should therefore be aware of its architecture, how it works, and its limitations to make the right choice. This is what this article is about, we will try to cut through the fog and explore some key factors to consider when selecting the right option.

Let's get started!