KonstantinosBarmpas/CarND-Capstone-Project

Project of Udacity's "Self-Driving Car Engineer" Nanodegree Through interactive projects in computer vision, robotic controls, localization, path planning, and more, you’ll prepare for a successful career in the incredible field of autonomous systems.

Self-Driving Car Engineer Nanodegree Capstone Project

This is the project repo for the final project of the Udacity Self-Driving Car Nanodegree: Programming a Real Self-Driving Car. For more information about the project, see the project introduction here.

Credits

The team members are:

• Georgios Varisteas varisteas@gmail.com (Team Leader)
• Konstantinos Barmpas ntinosbarmpas@gmail.com
• Daniel Gámez Franco gmzcodes@outlook.com
• Kevin Wen 935413249@qq.com
• Katsumi Inoue katsusea528@gmail.com

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My part

During Udacity's "Self-Driving Car Engineer Nanodegree" Capstone project, my part of the project was to design the two Traffic Light Classifier models for the two modes (simulator and site).

More details can be found here :

https://github.com/KonstantinosBarmpas/Traffic-Light-Classifier

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Installation SetUp

Please use one of the two installation options, either native or docker installation.

Native Installation

• Be sure that your workstation is running Ubuntu 16.04 Xenial Xerus or Ubuntu 14.04 Trusty Tahir. Ubuntu downloads can be found here.

• If using a Virtual Machine to install Ubuntu, use the following configuration as minimum:

  • 2 CPU
  • 2 GB system memory
  • 25 GB of free hard drive space

  The Udacity provided virtual machine has ROS and Dataspeed DBW already installed, so you can skip the next two steps if you are using this.

• Follow these instructions to install ROS

  • ROS Kinetic if you have Ubuntu 16.04.
  • ROS Indigo if you have Ubuntu 14.04.

• Dataspeed DBW

  • Use this option to install the SDK on a workstation that already has ROS installed: One Line SDK Install (binary)

• Download the Udacity Simulator.

Docker Installation

Install Docker

Build the docker container

docker build . -t capstone

Run the docker file

docker run -p 4567:4567 -v $PWD:/capstone -v /tmp/log:/root/.ros/ --rm -it capstone

Port Forwarding

To set up port forwarding, please refer to the instructions from term 2

Usage

1. Clone the project repository

git clone https://github.com/udacity/CarND-Capstone.git

2. Install python dependencies

cd CarND-Capstone
pip install -r requirements.txt

3. Make and run styx

cd ros
catkin_make
source devel/setup.sh
roslaunch launch/styx.launch

4. Run the simulator

Real world testing

1. Download training bag that was recorded on the Udacity self-driving car.
2. Unzip the file

unzip traffic_light_bag_file.zip

3. Play the bag file

rosbag play -l traffic_light_bag_file/traffic_light_training.bag

4. Launch your project in site mode

cd CarND-Capstone/ros
roslaunch launch/site.launch

5. Confirm that traffic light detection works on real life images