Quaternion Based Sensor Fusion for 9 Axis IMU's

One of the important part of any robotics system are the sensor fusion and state estimation algorithms. The quality of the sensor fusion algorithm directly influences how well a robotics system will perform. A really well known and widely used sensor fusion algorithm is the Extended Kalman Filter (EKF). Unlike the regular Kalman Filter it can handle non-linearities in Model Dynamics as well as in the measurement models. A popular use of this algorithm is in fusing data from an Inertial Measurement Unit (IMU). A 9 axis IMU consists of 3 sensors, a gyroscope, an accelerometer and a magnetometer. Each of these sensors give us data which can then be fused to estimate the orientation of the sensor itself. For this project I implement an EKF to estimate the orientation (in quaternions) of the sensor. I then use these outputs and compare two different sensors.