mez / extended_kalman_filter_cpp

An Extended Kalman Filter (that uses a constant velocity model) in C++. This EKF fuses LIDAR and RADAR sensor readings to estimate location (x,y) and velocity (vx, vy).

Extended Kalman Filter C++

An Extended Kalman Filter (that uses a constant velocity model) in C++. This EKF fuses LIDAR and RADAR sensor readings to estimate location (x,y) and velocity (vx, vy).

Check out the Python version

** Extended Kalman Filter Python **

Data log input file format

#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

Dependencies

• cmake >= 3.5
• All OSes: click here for installation instructions
• make >= 4.1
  • 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]((https://developer.apple.com/xcode/features/)
  • Windows: recommend using MinGW

Basic Build Instructions

1. Clone this repo.
2. Make a build directory: mkdir build && cd build
3. Compile: cmake .. && make
   • On windows, you may need to run: cmake .. -G "Unix Makefiles" && make
4. Run it: ./ExtendedKF path/to/input.txt path/to/output.txt. You can find some sample inputs in 'data/'.
   • eg. ./ExtendedKF ../data/sample-laser-radar-measurement-data-1.txt output.txt