Radar and Lidar Sensor Fusion using Extended, and Unscented Kalman Filter for Object Tracking and State Prediction.
[L(for lidar)] [m_x] [m_y] [t] [r_x] [r_y] [r_vx] [r_vy]
[R(for radar)] [m_rho] [m_phi] [m_drho] [t] [r_px] [r_py] [r_vx] [r_vy]
Where:
(m_x, m_y) - measurements by the lidar
(m_rho, m_phi, m_dho) - measurements by the radar in polar coordinates
(t) - timestamp in unix/epoch time the measurements were taken
(r_x, r_y, r_vx, r_vy) - the real ground truth state of the system
Example:
R 8.60363 0.0290616 -2.99903 1477010443399637 8.6 0.25 -3.00029 0
L 8.45 0.25 1477010443349642 8.45 0.25 -3.00027 0
Since here for fusing Radar and Lidar data, we need to have Extended KF for working with Radar data since the position is obtained by converting the polar co-ordinates using non-linear equations to (x,y) position.
Lidar data can be treated via simple Kalman Filter.
Helper methods and input data file adapted from https://github.com/mithi/Fusion-EKF-Python