Extended Kalman Filter Project Starter Code

Self-Driving Car Engineer Nanodegree Program In this project a Extended Kalman Filter is implemented to esitmate the position and velocity of a bicycle that travel around a simulated car. The performance of EKF is evaluated by Root Mean Squared Error(RMSE).

In the picture, lidar measurements are red circles, radar measurements are blue circles with an arrow pointing in the direction of the observed angle, and estimation markers are green triangles.

File Structure

In src:

• Eigen is a library to support matrix computation.
• FusionEKF.cpp is the code to implement sensor fusion logic.
• kalman_filter.cpp defines the Kalman Filter for Laser measurement and Extended Kalman Filter for Radar measurement.
• main.cpp is the code to send messages to the simulator.
• tool.cpp is the code to compute RMSE and Jacobian of the H matrix in EKF.

Dependencies

• This project involve using an open source package called uWedSocketIO. You can install it on linux by ./install-ubuntu.sh in this project folder.
• You need this simulator. After open it, select the EKF/UKF simulation.