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Ekaterina Marova
Data Scientist - ML, Python

An aspiring data scientist with a mathematical and research background.

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Comparing Robotics Visualization Tools: RViz, Foxglove, Rerun

· 24 min read
Ekaterina Marova
Data Scientist - ML, Python

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In robotics development, effective visualization and analysis tools are essential for monitoring, debugging, and interpreting complex sensor data. Platforms like RViz, Foxglove, and Rerun play a key role at the visualization layer of the observability stack. They help developers interact with both live and recorded data. These tools rely on timely, well-structured access to the underlying data streams. That's where ReductStore comes in. It handles the data logging, storage, and processing, with a focus on capturing high-volume time-series data efficiently. ReductStore aims to integrate with tools like RViz, Foxglove, and Rerun, supporting a complete observability pipeline: from raw data ingestion to actionable insights.

Getting Started with LeRobot

· 16 min read
Ekaterina Marova
Data Scientist - ML, Python

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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.

Getting Started with MetriCal

· 17 min read
Ekaterina Marova
Data Scientist - ML, Python

Intro image

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.