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3D Mapping Hexacopter Simulation using Gazebo and Robot Operating Sytem(ROS)

Published:24 February 2017Publication History

ABSTRACT

This paper present a simulation of Unmanned Aerial Vehicle which is type of hexacopter for building 3D maps of exploration environment. This simulation using Gazebo Simulator environment with Software In the Loop (SITL) ardupilot that is integrated with Robot Operating System as a open source flexible framework for writing robot software. To proceed 3D maps construction, we have installed Intel Realsense R200 RGB-D camera on hexacopter for getting RGB image data and Depth data that will be computed by open source octomap ROS package to result 3D Maps. Octomap using octree data structure to form 3D Map of voxel with odometry of hexacopter.

References

  1. N. Koenig and A. Howard.2004. "Design and Use Paradigms for Gazebo, An Open-source Multi-Robot Simulator," in International Conference on Intelligent Robots and Systems, Sendal, Japan.Google ScholarGoogle Scholar
  2. Joseph, Lentin.2015. "Mastering ROS for Robotics Programming". Packt Publishing.Google ScholarGoogle Scholar
  3. W. Garage, ROS: Robot Operating System, 2011{J}. URL: http://www.ros.orgGoogle ScholarGoogle Scholar
  4. Quigley M, Conley K, Gerkey B, Faust J, Foote T, Leibs J and Ng A Y. 2009. ROS: an open-source robot operating system. In ICRA Workshop on Open Source Software, 3(3.2): 5.Google ScholarGoogle Scholar
  5. Hornung A, Wurm K M, Bennewitz M, Stachniss C and Burgard W. 2013. OctoMap: An efficient probabilistic 3D mapping framework based on octrees. Autonomous Robots, 34(3): 189--206. Octomap Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Moravec HP and Elfes A. 1985. High resolution maps from wide angle sonar. In Robotics and Automation. Proceedings. 1985 IEEE International Conference on IEEE, 2: 116--121.Google ScholarGoogle Scholar

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  • Published in

    cover image ACM Other conferences
    ICMLC '17: Proceedings of the 9th International Conference on Machine Learning and Computing
    February 2017
    545 pages
    ISBN:9781450348171
    DOI:10.1145/3055635

    Copyright © 2017 ACM

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 24 February 2017

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