Unscented Kalman Filter using IMU and GNSS data for vehicle or mobile robot localization.

Requirements: Eigen3, ROS

Input: sensor_msgs/NavSatFix, sensor_msgs/Imu

Output: nav_msgs/Odometry

Key Features:

1. Assumes 2D motion.
2. Initializes the state{position x, position y, heading angle, velocity x, velocity y} to (0.0, 0.0, yaw, 0.0, 0.0) with the yaw from IMU at the start of the program if no initial state is provided.
3. Uses acceleration and yaw rate data from IMU in the prediction step.
4. GNSS data is used for correction.
5. Yaw from the IMU is only used to initialize and not in correction step.

How to use:

1. Clone the repository in to your workspace
2. catkin_make #compile
3. Edit the parameters in config/ukf.yaml to your suiting. Note that not all the parameters used in the code are not present in the yaml file.
4. roslaunch ukf ukf.launch #to run the code