A practical approach to Kalman filter and how to implement it
FreePractical Kalman filter implementation for balancing robots
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About A practical approach to Kalman filter and how to implement it
A blog post from TKJ Electronics that provides a practical, hands-on introduction to implementing a Kalman filter for sensor fusion, specifically combining accelerometer and gyroscope data for balancing robots. The author explains the basic theory, highlights the differences between complimentary and Kalman filters, and shares open-source code (including a discovered bug fix). The content is aimed at readers with basic matrix knowledge who want to implement the filter without deep theoretical study.
Key Features
Step-by-step practical approach to Kalman filter implementation
Uses accelerometer and gyroscope measurements for sensor fusion
Compares Kalman filter with complimentary filter
Includes open-source code and a fix for a bug in the original implementation
Explains measurement noise and process noise concepts
Provides a Danish master assignment zip file for reference
Pros & Cons
Pros
- Practical, hands-on guide with real code
- Corrects a known mistake in the original Kalman filter implementation
- Clear comparison between complimentary and Kalman filters
- Assumes only basic matrix knowledge (multiplication, transpose)
- Open-source and free to use
Cons
- Does not cover deeper theoretical derivation of Kalman filter equations
- Content is specific to accelerometer and gyroscope fusion; may require adaptation for other use cases
- Code examples are in C/C++ for embedded systems, not a ready-to-use library
Best For
Balancing robots (e.g., two-wheeled self-balancing robots)Sensor fusion of accelerometer and gyroscope for orientation estimationEducational resource for learning Kalman filtering in embedded systems