Implement Error-State Extended Kalman Filter on fusing data from IMU, Lidar and GNSS.
This is a module assignment from State Estimation and Localization course of Self-Driving Cars Specialization on Coursera.org.
This assginment implements Error-State Extended Kalman Filter on fusing IMU, Lidar and GNSS data coming from different frequencies.
IMU data is used for motion model prediction and Lidar and GNSS data is used for measurement model correction.
For more details for ES-EKF please refer to this paper
Simply do python3 es_ekf.py, the data will be loaded and fused.
Please see the fused 3D trajectory below.
Fusion Result
