Learn about the Kalman filter - a "smart" signal processing algorithm that has revolutionized fields ranging from space exploration to modern robotics.
The Kalman Filter allows us to estimate future states of a system despite uncertainty and noise. We'll explore how this algorithm, first used during the Apollo space missions, continues to predict the future in autonomous navigation systems like Robot Localization in ROS, object tracking in OpenCV, and precise control in servomotors.
What is the Kalman Filter?
The Kalman Filter allows us to estimate future states of a system despite uncertainty and noise. We'll explore how this algorithm, first used during the Apollo space missions, continues to predict the future in autonomous navigation systems like Robot Localization in ROS, object tracking in OpenCV, and precise control in servomotors.
What is the Kalman Filter?
- Overview of Moving Average, Low Pass, and Gaussian Filters
- Why Kalman filter predicts future state
- Understanding Variance
- The Filter Algorithm
- Starting with an Initial Estimate
- Integrating Measurements
- Tuning the Kalman Gain
- Modeling Systems
- Transitioning System Models
- Understanding Covariance
- Estimate Covariance
- Noise Covariance
- Transitioning Covariance
- System Identification
- Simulating Systems
- Kalman Filter in Python
- Kalman Filter in C++
- Fusing Measurements from Multiple Sensors
- Handling Rate Mismatch
- Additional Resources
