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.
- Clone the repository and change directory into the repository folder
- Create new folder, build
mkdir build - Change directory into the newly built folder, build
cd build - Compile
cmake .. make- 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"]
- cmake >= 3.5
- All OSes: click here for installation instructions
- make >= 4.1 (Linux, Mac), 3.81 (Windows)
- Linux: make is installed by default on most Linux distros
- Mac: install Xcode command line tools to get make
- Windows: Click here for installation instructions
- gcc/g++ >= 5.4
- Linux: gcc / g++ is installed by default on most Linux distros
- Mac: same deal as make - install Xcode command line tools
- Windows: recommend using MinGW