neocsr/CarND-Capstone

This is the project repo for the Autonomous Wizards team from the inaugural (July 7th - October 16th, 2017) cohort of the Udacity Self Driving Car Engineer Nanodegree final & capstone project for said Nanodegree, alternately titled "System Integration" & "Programming a Real Self Driving Car". For more information about the project, see the project introduction here--note: massive "paywall", you have to be registered for the 3rd Term of said Nanodegree, total cost for said activity being $2400, plus having passed the prior two terms / 10 projects.

A video showing a complete run of the virtual track in the simulator by our current--as of October 10th, 2017--version of our repo can be found here, at this link to YouTube video--or just <ctrl>click (to open in a new tab) on thumbail below, same link

Introducing Team Autonomous Wizards (Members in alphabetical order):

Juan Carlos Ortiz ortizjuan2@gmail.com

Chuck S. chuck_s_@outlook.com

Ezra J. Schroeder ezra.schroeder@gmail.com

Christian Sousa neocsr@gmail.com

Calvenn Tsuu calvenn.tsuu@gmail.com

Documenting code

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 127.0.0.1:4567:4567 -v $PWD:/capstone -v /tmp/log:/root/.ros/ --rm -it capstone

Usage

1. Clone the project repository

git clone https://github.com/seneca-wolf/CarND-Capstone 

2. Install python dependencies (Please note: if you do not have ROS installed / experience w/ ROS it may interfere w/ your [e.g. conda] python distributions & environments, which is why Udacity uses a specific preconfigured Virtual Machine for the Capstone project). Also, please note that the setup here (particularly in item 3, below) are different than for the original Udacity repo for this project. C.f. the changelog.txt file maintained by Chuck.

cd CarND-Capstone
pip install -r requirements.txt

3. Make and run styx

cd ros
cd src 
catkin_init_workspace
cd .. 
catkin_make
source devel/setup.bash 
roslaunch launch/styx.launch

4. Run the simulator

Real world testing

1. Download training bag that was recorded on the Udacity self-driving car (a bag demonstraing the correct predictions in autonomous mode can be found here)
2. Unzip the file

unzip traffic_light_bag_files.zip

3. Play the bag file

rosbag play -l traffic_light_bag_files/loop_with_traffic_light.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