Udacity - Term2 - Project 1

This project consists of C++ implentation of Extended Kalam Filter as part of Udacity Self-Driving Car Nanodegree - Extended Kalman Filter project (Term2). Based on LIDAR and RADAR data as input, we use EKF to Predict and Update Position and Velocity information of the Car. A simulator is provided to test on two different Data Sets. Communication between simulator and EKF is done using WebSocket.

• cmake >= 3.5
• make >= 4.1
• gcc/g++ >= 5.4
• Udacity's simulator

For detailed instructions on how to install ref: https://github.com/udacity/CarND-Extended-Kalman-Filter-Project

• Clone the repo and cd to the project using Ubuntu Terminal
• cd to build
• Run 'cmake ..." and "make" commands
• Above commands will create "ExtendedKF" executable

• From build directory, execute .'/ExtendedKF'. Output should be:

Listening to port 4567
Connected!!!

• Once you see above output, on simulator select "Dataset 1/2" and press "start" button.
• You should see RMSE value on right side on the Simulator window

• As stated RMSE value (px, py, vx and vy) should be <= [.11, .11, 0.52, 0.52]

• DataSet1 = [0.09, 0.08, 0.45, 0.43]
• DataSet2 = [0.07, 0.09, 0.42, 0.49]