Skip to content

Latest commit

 

History

History
65 lines (53 loc) · 1.86 KB

README.md

File metadata and controls

65 lines (53 loc) · 1.86 KB
Raw

SES_Group_Repo

About:

Prerequisites:

  1. ROS Melodic on Ubuntu 18.04 or ROS Kinetic on Ubuntu 16.04.
  2. Python catkin-tools
  3. OpenCV - Computer Vision library
  4. Boost - C++ library

Set up

  1. Create a ROS workspace
mkdir -p ~/catkin_ws/src
cd catkin_ws
catkin_init_workspace
  1. Clone this repository in your ROS workspace recursively along with the submodules
cd ~/catkin_ws/src
git clone --recurse-submodules -j8 https://github.com/mihirk284/SES_Group_Repo
  1. Build your packages
cd ~/catkin_ws
catkin build

Usage

1) Test the installation

Try launching a sample test launch file

roslaunch rotors_gazebo mav_hovering_example.launch

You should see a drone hovering some distance above the ground in your Gazebo simulator.

2) YOLO Implementation

Launch your drone in a Gazebo environment

roslaunch rotors_gazebo mav_hovering_example.launch

Visualise the camera feed in RViz

rosrun rviz rviz

Click on "Add" and add the camera topic, you'll see a camera feed of the environment in which the drone is. Launch the YOLO object detector.

roslaunch darknet_ros yolo_v3.launch

Try placing some objects like the aeroplane, table, turtlebot in the Gazebo environment in front of the camera and you'll see YOLO detecting it with a bounding box around that object.

Contributors:

  1. Mihir Kulkarni
  2. SriSreyas Sundaresan
  3. Shivangi Gupta
  4. Vishal Singh
  5. Aditya Bidwai