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Autonomous Navigation Using Monocular ORB SLAM2

  • Shubham VithalaniEmail author
  • Sneh Soni
  • Param Rajpura
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 618)

Abstract

Simultaneous Localisation and Mapping (SLAM) is the mapping of an unknown environment and at the same time localising the ego body in that environment. ORB SLAM2 (IEEE Trans Robot 33:1255–1262 2017 [1]) is a state of the art visual SLAM algorithm which can calculate camera trajectory using Monocular camera. Since monocular slam has the scale drift issue the source code has been so altered that the map can be saved or previously built map can be reloaded for localisation. To plan optimal trajectory of a vehicle to reach from a source to goal, A* (Computing the shortest path: A search meets graph theory 2005 [2]) search algorithm has been implemented. Gazebo (2004 IEEE/RSJ International Conference on Intelligent Robots and Systems 2004 [3]) is an open-source robot simulation tool on which a Turtlebot robot was used to test algorithm that has been proposed in this paper. Turtlebot is a two-wheeled differential drive robot on which various sensors are mounted. A novel approach has been used for local path planning of the vehicle. ROS (ROS: an open-source Robot Operating System 2009 [4]) framework has been used to communicate between various nodes for performing navigation.

Keywords

ORB slam ROS [3] Gazebo [3] A* search [2] SLAM Mapping Monocular camera 

References

  1. 1.
    Mur-Artal, Raul, and Juan D. Tardós. 2017. ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras. IEEE Transactions on Robotics 33: 1255–1262.CrossRefGoogle Scholar
  2. 2.
    Goldberg, Andrew V., and Chris Harrelson. 2005. Computing the Shortest Path: A Search Meets Graph Theory. SODA.Google Scholar
  3. 3.
    Koenig, Nathan P., and Andrew G. Howard. 2004. Design and Use Paradigms for Gazebo, An Open-Source Multi-Robot Simulator. In 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566) 3, 2149–2154.Google Scholar
  4. 4.
    Quigley, Morgan et al. 2009. ROS: An Open-Source Robot Operating System.Google Scholar
  5. 5.
    Masunga, Nsingi. 1999. Mobile Robot Navigation in Indoor Environments by Using the Odometer and Ultrasonic Data.Google Scholar
  6. 6.
    Biswas, Joydeep, and Manuela M. Veloso. 2012. Depth camera based indoor mobile robot localization and navigation. In 2012 IEEE International Conference on Robotics and Automation 1697–1702.Google Scholar
  7. 7.
    Gatesichapakorn, Sukkpranhachai, et al. 2019. ROS Based Autonomous Mobile Robot Navigation Using 2D LiDAR and RGB-D Camera. In 2019 First International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics (ICA-SYMP) 151–154.Google Scholar
  8. 8.
    Weingarten, Jan W., and Roland Siegwart. 2005. EKF-Based 3D SLAM for Structured Environment Reconstruction. In 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems 3834–3839.Google Scholar
  9. 9.
    Cheng, Jiantong, et al. 2014. Compressed Unscented Kalman Filter-Based SLAM. In 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014) 1602–1607.Google Scholar
  10. 10.
    Törnqvist, David, et al. 2009. Particle Filter SLAM With High Dimensional Vehicle Model. Journal of Intelligent and Robotic Systems 55: 249–266.CrossRefGoogle Scholar
  11. 11.
    Strasdat, Hauke, et al. 2012. Visual SLAM: Why filter? Image Vision Comput 30: 65–77.CrossRefGoogle Scholar
  12. 12.
    Montemerlo, Michael, et al. 2002. FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem. AAAI/IAAI.Google Scholar
  13. 13.
    Triggs, Bill, et al. 1999. Bundle Adjustment—A Modern Synthesis. Workshop on Vision Algorithms.Google Scholar
  14. 14.
    Klein, Georg, and David William Murray. 2007. Parallel Tracking and Mapping for Small AR Workspaces. In 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality 225–234.Google Scholar
  15. 15.
    Izadi, Shahram, et al. 2011. Kinect Fusion: Real-Time 3D Reconstruction and Interaction Using a Moving Depth Camera. UIST.Google Scholar
  16. 16.
    Murray, Don Ray, and James J. Little. 2000. Using Real-Time Stereo Vision for Mobile Robot Navigation. Auton Robots 8: 161–171.Google Scholar
  17. 17.
    Rublee, Ethan, et al. 2011. ORB: An Efficient Alternative to SIFT or SURF. In 2011 International Conference on Computer Vision 2564–2571.Google Scholar
  18. 18.
    Wrobel, Bernhard P. 2001. Multiple View Geometry in Computer Vision. KI 15, 41.Google Scholar
  19. 19.
    Smith, Russell L. 2005. Open Dynamics Engine-ODE.Google Scholar
  20. 20.
    Sniedovich, Moshe. 2006. Dijkstra’s Algorithm Revisited: The Dynamic Programming Connexion.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Instrumentation & Control Engineering DepartmentInstitute of Technology Nirma UniversityAhmedabadIndia

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