Visual guide to the Kalman Filter - optimal recursive estimator for linear systems with Gaussian noise. Intuition The Kalman Filter combines two sources of information: Prediction from a model (physics/dynamics) Measurement from sensors (noisy observations) It finds the optimal balance between trusting the model vs …

Comprehensive guide to the Madgwick filter - an efficient gradient descent algorithm for IMU orientation estimation. Overview The Madgwick filter is an orientation estimation algorithm that fuses: Gyroscope (angular velocity) Accelerometer (gravity direction) Magnetometer (magnetic north) - optional Key Innovation: …

Comprehensive guide to the Mahony filter - a complementary filter with PI feedback for IMU orientation estimation. Overview The Mahony filter is an orientation estimation algorithm that uses a complementary filter approach with PI (Proportional-Integral) feedback to fuse: Gyroscope (angular velocity) Accelerometer …

Comprehensive guide to sensor fusion using Kalman filters for combining data from multiple sensors. What is Sensor Fusion? Sensor Fusion: Combining data from multiple sensors to produce more accurate, reliable, and complete information than any single sensor alone. Why Fuse Sensors? Individual Sensor Limitations Sensor …