bahia14/kalman-filter-unscented

Unscented Kalman Filter

This code utilize an Unscented Kalman Filter to estimate the state of a moving object of interest with noisy lidar and radar measurements.

A video of the Unscented Kalman Filter in action can be seen below.

Prerequisites

• 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

Simulator

This project involves the Udacity Term 2 Simulator which can be downloaded here

Build Instructions

1. Clone this repo.

$ git clone https://github.com/Heych88/udacity-sdcnd-unscented-kalman-filter.git

2. This repository includes two files that can be used to set up and intall uWebSocketIO for either Linux or Mac systems. For windows you can use either Docker, VMware, or even Windows 10 Bash on Ubuntu to install uWebSocketIO.

Install uWebSocketIO by running the script

For Linux bash install-ubuntu.sh

For Mac sh install-mac.sh

3. ./UnscentedKF

If the above fails, install intall uWebSocketIO seperatly, then build and ran by doing the following from the project top directory.

1. mkdir build
2. cd build
3. cmake ..
4. make
5. ./UnscentedKF

Running in the Simulator

The following assumes the Build Instructions was followed and produced no errors.

Download and extract the simulator.

1. Navigate to the extrated simulator directory and run the simulator.
2. Select the settings best for your computer and click ok.
3. In a terminal window run the UnscentedKF executable

$ cd <local directory>/udacity-sdcnd-unscented-kalman-filter
$ ./UnscentedKF

License

This project is licensed under the MIT License - see the LICENSE.md file for details.