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the goal is to implement Extended Kalman filter to determine the state of a Quadrotor given the IMU and Vicon data. The Vicon being the world frame provides information about the position, orientation, linear and angular velocities. The IMU onboard, through the accelerometer and gyroscope provides the state values.

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andrew-dave/Implementation-of-Extended-Kalman-Filter

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Implementation of Extended Kalman Filter

The goal is to implement Extended Kalman filter to estimate the pose of a Quadrotor given the IMU and Vicon data. The Vicon being the world frame provides information about the position, orientation, linear and angular velocities. The IMU onboard, through the accelerometer and gyroscope provides the state values. The idea is to make use of the data, use them as control inputs, and determine the state estimates of the quadrotor.

Run this implementation

To execute this, clone this repo and open the directory within MATLAB. Run KalmanFilt_Part1.m to execute the first part and KalmanFilt_Part2.m for the second part. For detailed implementation refer to the report provided

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the goal is to implement Extended Kalman filter to determine the state of a Quadrotor given the IMU and Vicon data. The Vicon being the world frame provides information about the position, orientation, linear and angular velocities. The IMU onboard, through the accelerometer and gyroscope provides the state values.

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