The project has been forked from https://github.com/arsalan311/fusion-ekf/commit/ef91a249ce343017f11e502534ce1b4353a0aac8
This version is the comparison of working of Kalman and Extended kalman Filter.
This is an extended Kalman Filter implementation in C++ for fusing lidar and radar sensor measurements. A Kalman filter can be used anywhere you have uncertain information about some dynamic system, and you want to make an educated guess about what the system is going to do next.
In this case, we have two 'noisy' sensors:
- A lidar sensor that measures our position in cartesian-coordinates
(x, y) - A radar sensor that measures our position and velocity in polar coordinates
(rho, phi, drho)
We want to predict our position, and how fast we are going in what direction at any point in time:
- In essence: the position and velocity of the system in cartesian coordinates:
(x, y, vx, vy) - Note that we are assuming a constant velocity model (CV) for this particular system
This extended kalman filter does just that.
- Check the Jupyter notebooks to see sample usage and visualizations
- Please use the following format for your input file:
[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