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Simple Localization based on Extended Kalman Filter

Introduction

Note: This project makes use of Udacity Extended Kalman Filter Starter Code and Udacity Workspace. The simulator and data are not included in this repository and can be found in the link above. In this project, a simple extended Kalman filter(EKF) is implemented to acquire position of a moving vehicle in the simulator provided by Udacity with C++. Noisy LIDAR and RADAR is provided in the form of x and y position measurements in the simulator. By fusing the LIDAR and RADAR data, the estimated position is obtained and compared to the actual ground truth to calculate the final RMSE.

Compiling and running simulator

  1. Clone the repository and change directory into the repository folder
  2. Create new folder, build mkdir build
  3. Change directory into the newly built folder, build cd build
  4. Compile cmake ..
  5. make
  6. Execute the generated file ./ExtendedKF

INPUT: values provided by the simulator to the c++ program

["sensor_measurement"] => the measurement that the simulator observed (either lidar or radar)

OUTPUT: values provided by the c++ program to the simulator

["estimate_x"] <= kalman filter estimated position x

["estimate_y"] <= kalman filter estimated position y

["rmse_x"]

["rmse_y"]

["rmse_vx"]

["rmse_vy"]

Dependencies