In this project, a demonstration of the Unscented Kalman filter is presented to estimate the position of a vehicle with LIDAR & RADAR sensors. This demonstration is based on a simulator developed by Udacity and was completed as part of Udacity's Self-Driving Car Nanodegree. The Udacity github repo for this project has all the details about the software used in the simulator and the installation instructions.
- The project uses uWebSocketIO for communication between the user-written algorithm and the simulator. Udacity has provided bash scripts to install this in both Linux/Mac environments. These scripts are included in this repo.
- The simulator can be downloaded from here.
- Clone this repo.
- Make a build directory:
mkdir build && cd build - Compile:
cmake .. && make - Run it:
./UnscentedKFThis should setup a listener for the code to get data from the simulator. - Launch the simulator from a terminal:
./ - Select the Kalman Filter project and click start to start the simulation.
These steps should get any user in a Linux/Mac environment up and running with the code.
Refer to UnscentedKalmanFilter.ipynb for documentation of theory and code.