LIDAR and RADAR Data Fusion with Unscented 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 unscented Kalman filter for both data sets. The theory and formulas used in this project are summarized 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.

The motion model used in this project is the CTRV (constant turn rate and velocity) model, as shown below:

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

$ ./ukf 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.