silvermind20/usdc-system-integration

Final Project for Udacity Self Driving Car 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.

Team Members

Name	Email Address
David Escolme	escolme@bartlebooth.co.uk (Lead)
Simin Farrokhi	simin.farro@gmail.com
Suresh Rangarajulu	surya501@gmail.com
Chenglin Zhang	chenglinzhang@yahoo.com
Branimir Malnar	branimir_malnar@yahoo.com

Notes

This repo is the base walk-through code with the following modifications (19-11-2018):

• In waypoint_updater the LOOKHEAD setting is 20 and the Rate is set to 10 - both trying to contain latency
• In tl_detector there is a switch to control whether to use the classifier and also a loop at 10Hz to control how frequently to publish images
• the classifier is implemented in light_classification, the path to the .pb graph is loaded from a parameter in tl_detector

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/escolmebartlebooth/usdc-system-integration.git

2. Install python dependencies

cd usdc-system-integration
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