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Waypoint Navigation by EuROS team

Udacity Carla (2016 Lincoln MKZ) self-driving car waypoint navigation project

NAME E-MAIL SLACK TIME ZONE COUNTRY ROLE
Nikolay Dimitrov nikidimi@gmail.com @nikidimi UTC+2 BGR
Brian Holt bdholt1@gmail.com @bdholt1 UTC+1 GBR
Sangxia Huang huang.sangxia@gmail.com @sangxia UTC+2 SWE
Luca Massaron lucamassaron@gmail.com @lmassaron UTC+2 ITA
Peter Skvarenina peter.skvarenina@gmail.com @squared9 UTC+2 GER [LEAD]

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.

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.

Usage

  1. Clone the project repository
git clone https://github.com/EuROS-SDC17/Waypoint_Navigation.git
  1. Install python dependencies
cd Waypoint_Navigation
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
unzip linus_sys_int.zip
cd linus_sys_int
chmod +x system_integration.x86_64
./system_integration.x86_64

Real world testing

  1. Download training bag that was recorded on the Udacity self-driving car
  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
catkin_make
source devel/setup.sh
roslaunch launch/site.launch

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Udacity Carla (2016 Lincoln MKZ) Waypoint Navigation Project

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  • Python 37.8%
  • Jupyter Notebook 24.2%
  • CMake 22.8%
  • C++ 15.0%
  • Shell 0.2%