EKF

LIDAR and RADAR Data Fusion with Extended Kalman Filter

Jun Zhu

Introduction

In this project, a stream of simulated mixed LIDAR and RADAR data will be used to estimate the trajectory of an object moving in a curved trajectory by using the normal Kalman filter for the LIDAR data and the extended Kalman filter (EKF) for the RADAR data. The theory and formulas used in this project can be found here.

The measurement noises for the LIDAR data are Sx = Sy = 0.15 m.

The measurement noises for the RADAR data are Sr = 0.30 m, Sphi = 0.03 rad and Sr'= 0.3 m/s.

Dependencies

Eigen >= 3.3.3

$ git clone --branch 3.3.7 https://github.com/eigenteam/eigen-git-mirror.git
$ cd eigen-git-mirror
$ mkdir build && cd build
$ cmake .. && make install

Build and run

Build

$ mkdir build && cd build
$ cmake .. && make

Run

$ ./ekf input output

Visualize the result

You can use this Jupyter notebook to build the code, process the data and visualize the result.

Check https://nbviewer.jupyter.org/ if github fails to render the notebook.