LeRobot is an open-source project by Hugging Face that makes it easy to explore the world of robotics with machine learning, even if you’ve never done anything like this before. It gives you pre-trained models, real-world data, and simple tools built with PyTorch, a popular machine learning framework. Whether you're just curious or ready to try your first robotics project, LeRobot is a great place to start.
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.
Sensor calibration is the process of determining the precise mathematical parameters that describe how a sensor perceives or measures the physical world. By comparing sensor outputs to known reference values, we can correct measurement errors and ensure data from different sensors align accurately.
There are two main categories of calibration parameters:
Intrinsic parameters (Intrinsics): These capture the internal characteristics of a sensor, such as lens distortion in cameras or bias and scaling errors in IMUs. Calibrating intrinsics helps eliminate built-in measurement errors.
Extrinsic parameters (Extrinsics): These define a sensor's position and orientation relative to another sensor or the environment. Accurate extrinsics are essential for transforming and combining data from multiple sensors into a shared coordinate system.