项目作者: sandeeppaulraj

项目描述 :
高级语言: CMake
项目地址: git://github.com/sandeeppaulraj/CarND-Capstone.git
创建时间: 2018-07-02T07:56:51Z
项目社区:https://github.com/sandeeppaulraj/CarND-Capstone

开源协议:MIT License

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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.

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

  • Dataspeed DBW
  • Download the Udacity Simulator.

Docker Installation

Install Docker

Build the docker container

  1. docker build . -t capstone

Run the docker file

  1. 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

    1. git clone https://github.com/udacity/CarND-Capstone.git
  2. Install python dependencies

    1. cd CarND-Capstone
    2. pip install -r requirements.txt
  3. Make and run styx
    1. cd ros
    2. catkin_make
    3. source devel/setup.sh
    4. 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
    1. unzip traffic_light_bag_file.zip
  3. Play the bag file
    1. rosbag play -l traffic_light_bag_file/traffic_light_training.bag
  4. Launch your project in site mode
    1. cd CarND-Capstone/ros
    2. roslaunch launch/site.launch
  5. Confirm that traffic light detection works on real life images