Extended Kalman Filter Project for Self-Driving Car ND
The goal of this project is to use an Extended Kalman Filter to estimate the state of a moving object of interest with noisy lidar and radar measurements. The system will output the estimated x, y position from the Kalman filter's state vector, along with the RMSE, to the simulator provided by Udacity. Passing the project requires obtaining RMSE values that are lower than the tolerance outlined in the project rubric.
Starting to work on this project consists of the following steps:
- Install uWebSocketIO and all the required dependencies
- Clone this repository
- Build the main program
mkdir buildcd buildcmake ..make
- Launch
./ExtendedKF - Launch the Udacity Term 2 simulator
- Enjoy!
This project involves the Udacity Term 2 Simulator which can be downloaded here
This repository includes two files that can be used to set up and install 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. Please see this concept in the classroom for the required version and installation scripts.
- cmake >= 3.5
- All OSes: click here for installation instructions
- make >= 4.1 (Linux, Mac), 3.81 (Windows)
- 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
- Windows: recommend using MinGW
Once all the dependencies have been installed clone the project:
git clone https://github.com/gdangelo/CarND-Extended-Kalman-Filter-Projectand follow the steps 3 to 6 of the Overview section in order to build and run the main program.
Contact me anytime for anything about my projects or machine learning in general. I'd be happy to help you 😉
- Twitter: @gdangel0
- Linkedin: Grégory D'Angelo
- Email: gregory@gdangelo.fr