Towards Collaborative Mapping and Exploration Using Multiple Micro Aerial Robots

  • Sikang Liu
  • Kartik Mohta
  • Shaojie Shen
  • Vijay Kumar
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 109)


In this paper, we present a system for collaborative mapping and exploration with multiple quad rotor robots. The basic architecture and development of the algorithms for mapping and exploration validate our system with both simulation and real-world experiments. We utilize the 2.5-D structure of typical indoor environments and demonstrate the deployment of multiple autonomous quadrotors equipped with IMUs and laser scanners engaged in collaborative exploration. Estimation, control and planing algorithms are highly integrated in our system to achieve robust and efficient exploration behaviors.


Multi-robot Mapping Exploration SLAM Quadrotor 



We gratefully acknowledge support from ARL Micro Autonomous Systems and Technology Collaborative Technology Alliance Grant no. W911NF-08-2-0004.


  1. 1.
    Shen, S., Mulgaonkar, Y., Michael, N., Kumar, V.: Multi-sensor fusion for robust autonomous flight in indoor and outdoor environments with a rotorcraft mav. In: 2014 IEEE International Conference on Robotics and automation (ICRA). IEEE (2014)Google Scholar
  2. 2.
    Howard, A.: Multi-robot simultaneous localization and mapping using particle filters. Int. J. Robot. Res. 25(12), 1243–1256 (2006)CrossRefGoogle Scholar
  3. 3.
    Thrun, S., Liu, Y.: Multi-robot slam with sparse extended information filers. In: Robotics Research, pp. 254–266. Springer (2005)Google Scholar
  4. 4.
    Shen, S., Michael, N., Kumar, V.: Stochastic differential equation-based exploration algorithm for autonomous indoor 3d exploration with a micro-aerial vehicle. I. J. Robot. Res. 31(12), 1431–1444 (2012)CrossRefGoogle Scholar
  5. 5.
    Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: Part I. IEEE Robot. Autom. Mag. 13(2), 99–110 (2006)CrossRefGoogle Scholar
  6. 6.
    Dellaert, F., Kaess, M.: Square root sam: simultaneous localization and mapping via square root information smoothing. Int. J. Robot. Res. 25(12), 1181–1203 (2006)CrossRefzbMATHGoogle Scholar
  7. 7.
    Shen, S., Michael, N., Kumar, V.: Autonomous multi-floor indoor navigation with a computationally constrained mav. In: 2011 IEEE International Conference on Robotics and automation (ICRA), pp. 20–25. IEEE (2011)Google Scholar
  8. 8.
    Stachniss, C., Kretzschmar, H.: Pose graph compression for laser-based slam. In: International Symposium of Robotics Research (ISRR) (2011)Google Scholar
  9. 9.
    Van Der Merwe, R., Wan, E.A.: Sigma-point kalman filters for integrated navigation. In: Institute of Navigation (ION) (2004)Google Scholar
  10. 10.
    Michael, N., Mellinger, D., Lindsey, Q., Kumar, V.: The grasp multiple micro-uav testbed. IEEE Robot. Autom. Mag. 17(3), 56–65 (2010)CrossRefGoogle Scholar
  11. 11.
    Lee, T., Leoky, M., McClamroch, N.H.: Geometric tracking control of a quadrotor uav on se (3). In: 2010 49th IEEE Conference on Decision and Control (CDC), pp. 5420–5425. IEEE (2010)Google Scholar
  12. 12.
    Shen, S., Michael, N., Kumar, V.: Autonomous indoor 3d exploration with a micro-aerial vehicle. In: 2012 IEEE International Conference on Robotics and Automation (ICRA), May 2012, pp. 9–15Google Scholar
  13. 13.
    Bachrach, A., He, R., Roy, N.: Autonomous flight in unknown indoor environments. Int. J. Micro Air Veh. 1(4), 217–228 (2009)CrossRefGoogle Scholar
  14. 14.
    Kim, B., Kaess, M., Fletcher, L., Leonard, J., Bachrach, A., Roy, N., Teller, S.: Multiple relative pose graphs for robust cooperative mapping. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 3185–3192. IEEE (2010)Google Scholar
  15. 15.
    Rogers, J.G., Nieto-Granda, C., Christensen, H.I.: Coordination strategies for multi-robot exploration and mapping (2012)Google Scholar
  16. 16.
    Trevor, A.J.B., Rogers III J.G., Christensen, H.I.: Omnimapper: a modular multimodal mapping framework. In: 2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2014)Google Scholar
  17. 17.
    Dellaert, F.: Factor graphs and gtsam: a hands-on introduction (2012)Google Scholar
  18. 18.
    Cunningham, A., Paluri, M., Dellaert, F.: Ddf-sam: fully distributed slam using constrained factor graphs. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3025–3030. IEEE (2010)Google Scholar
  19. 19.
    Censi, A.: An icp variant using a point-to-line metric. In: IEEE International Conference on Robotics and Automation, ICRA 2008, pp. 19–25. IEEE (2008)Google Scholar
  20. 20.
    Kaess, M., Ranganathan, A., Dellaert, F.: isam: Incremental smoothing and mapping. IEEE Trans. Robot. 24(6), 1365–1378 (2008)CrossRefGoogle Scholar
  21. 21.
    Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J.J., Dellaert, F.: isam2: Incremental smoothing and mapping using the bayes tree. Int. J. Robot. Res. 31(2), 216–235 (2012)CrossRefGoogle Scholar
  22. 22.
    Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30(7), 846–894 (2011)CrossRefzbMATHGoogle Scholar
  23. 23.
    Sucan, I., Moll, M., Kavraki, E.: The open motion planning library. IEEE Robot. Autom. Mag. 19(4), 72–82 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sikang Liu
    • 1
  • Kartik Mohta
    • 1
  • Shaojie Shen
    • 1
  • Vijay Kumar
    • 1
  1. 1.GRASP LaboratoryUniversity of PennsylvaniaPhiladelphiaUSA

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