An Extended Kalman Filter (that uses a constant velocity model) in Python. This EKF fuses LIDAR and RADAR sensor readings to estimate location (x,y) and velocity (vx, vy).
main.py- Can run the tracker.tracker.py- Instance that tracks and uses EKF to predict and update state.efk.py- EKF implementation lives here.utils.py- Helper methods.
- pandas
- numpy
#L(for laser) meas_px meas_py timestamp gt_px gt_py gt_vx gt_vy
#R(for radar) meas_rho meas_phi meas_rho_dot timestamp gt_px gt_py gt_vx gt_vy
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
python main.py
Started Tracker for sample-laser-radar-measurement-data-1.txt
estimations count: 1224
measurements count: 1224
RMSE: [[ 0.06516487 0.06053792 0.53321165 0.5441927 ]]
Metric: [0.08, 0.08, 0.6, 0.6]
RMSE PASSED metric
Started Tracker for sample-laser-radar-measurement-data-2.txt
estimations count: 200
measurements count: 200
RMSE: [[ 0.18549633 0.19030227 0.47675529 0.80446808]]
Metric: [0.2, 0.2, 0.5, 0.85]