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CarND Capstone Project: System Integration

ros-graph

Team

Name E-Mail Github Role
Benjamin Söllner benjamin.soellner@daimler.com benjaminsoellner tl_detector, tf graph integration, Azure integration VM with GPU
Bernhard Rode mail@bernhardrode.de bernhardrode wp_updater, dbw_node
Carsten Mielenz carsten.mielenz.france@gmail.com carstenMIELENZ dbw_node, twist_controller
Johannes Hallier johannes.hallier@gmail.com jhallier tl_classifier, tf graph integration
Tianran Wang tr.wang236@gmail.com TrW236 Project Lead, wp_updater, tl_detector, dbw_node

System overview

ros-graph

Overview

There are 3 ROS modules modified:

  • waypoint_updater

  • tl_detector

  • twist_controller

Waypoint Updater

Traffic Light Detector

Twist Controller

Udacity

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.

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

  • Dataspeed DBW

  • 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

Usage

  1. Clone the project repository
git clone https://github.com/udacity/CarND-Capstone.git
  1. Install python dependencies
cd CarND-Capstone
pip install -r requirements.txt
  1. Make and run styx
cd ros
catkin_make
source devel/setup.sh
roslaunch launch/styx.launch
  1. 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
  1. Play the bag file
rosbag play -l traffic_light_bag_files/loop_with_traffic_light.bag
  1. Launch your project in site mode
cd CarND-Capstone/ros
roslaunch launch/site.launch
  1. Confirm that traffic light detection works on real life images