Skip to main content
SpringerLink
Log in

Autonomous Navigation for Micro Aerial Vehicles in Complex GNSS-denied Environments

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

Micro aerial vehicles, such as multirotors, are particular well suited for the autonomous monitoring, inspection, and surveillance of buildings, e.g., for maintenance in industrial plants. Key prerequisites for the fully autonomous operation of micro aerial vehicles in restricted environments are 3D mapping, real-time pose tracking, obstacle detection, and planning of collision-free trajectories. In this article, we propose a complete navigation system with a multimodal sensor setup for omnidirectional environment perception. Measurements of a 3D laser scanner are aggregated in egocentric local multiresolution grid maps. Local maps are registered and merged to allocentric maps in which the MAV localizes. For autonomous navigation, we generate trajectories in a multi-layered approach: from mission planning over global and local trajectory planning to reactive obstacle avoidance. We evaluate our approach in a GNSS-denied indoor environment where multiple collision hazards require reliable omnidirectional perception and quick navigation reactions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Achtelik, M.W., Lynen, S., Weiss, S., Chli, M., Siegwart, R.: Motion- and uncertainty-aware path planning for micro aerial vehicles. J. Field Robot. 31(4), 676–698 (2014)

    Article  Google Scholar 

  2. Anderson, S., Barfoot, T.D.: Towards relative continuous-time SLAM. In: Proc. of the IEEE Int. Conference on Robotics and Automation (ICRA), pp. 1033–1040 (2013)

  3. Applegate, D., Bixby, R., Chvatal, V., Cook, W: Concorde TSP solver (2006)

  4. Bachrach, A., He, R., Roy, N.: Autonomous flight in unstructured and unknown indoor environments. In: Proc. of European Micro Aerial Vehicle Conf. (EMAV) (2009)

  5. Bachrach, A., Prentice, S., He, R., Henry, P., Huang, A.S., Krainin, M., Maturana, D., Fox, D., Roy, N.: Estimation, planning, and mapping for autonomous flight using an RGB-D camera in GPS-denied environments. Int. J. Robot. Res. 31(11), 1320–1343 (2012)

    Article  Google Scholar 

  6. Beul, M., Behnke, S., Worst, R.: Nonlinear model-based 2D-position control for quadrotor UAVs. In: Proceedings of the Joint Int. Symposium on Robotics (ISR) and the German Conference on Robotics (ROBOTIK) (2014)

  7. Beul, M., Krombach, N., Zhong, Y., Droeschel, D., Nieuwenhuisen, M., Behnke, S.: A high-performance MAV for autonomous navigation in complex 3D environments. In: Proc. of the Int. Conference on Unmanned Aircraft Systems (ICUAS) (2015)

  8. Bosse, M., Zlot, R.: Continuous 3D scan-matching with a spinning 2D laser. In: Proc. of the IEEE Int. Conference on Robotics and Automation (ICRA), pp. 4312–4319 (2009)

  9. Cover, H., Choudhury, S., Scherer, S., Singh, S.: Sparse tangential network (SPARTAN): Motion planning for micro aerial vehicles. In: Proc. of the IEEE Int. Conference on Robotics and Automation (ICRA) (2013)

  10. Droeschel, D., Nieuwenhuisen, M., Beul, M., Holz, D., Stückler, J.: Multi-layered mapping and navigation for autonomous micro aerial vehicles. J. Field Robot. (2015)

  11. Droeschel, D., Stückler, J., Behnke, S.: Local multi-resolution representation for 6D motion estimation and mapping with a continuously rotating 3D laser scanner. In: Proc. of the IEEE Int. Conference on Robotics and Automation (ICRA) (2014a)

  12. Droeschel, D., Stückler, J., Behnke, S.: Local multi-resolution surfel grids for MAV motion estimation and 3D mapping. In: Proc. of the Int. Conference on Intelligent Autonomous Systems (IAS) (2014b)

  13. Elseberg, J., Borrmann, D., Nuechter, A.: 6DOF semi-rigid SLAM for mobile scanning. In: Proc. of the IEEE/RSJ Int. Conference on Intelligent Robots and Systems (IROS), pp. 1865–1870 (2012)

  14. Flores, G., Zhou, S., Lozano, R., Castillo, P.: A vision and GPS-based real-time trajectory planning for a MAV in unknown and low-sunlight environments. J. Intell. Robot. Syst. 74(1-2), 59–67 (2014)

    Article  Google Scholar 

  15. Fossel, J., Hennes, D., Claes, D., Alers, S., Tuyls, K.: OctoSLAM: A 3D mapping approach to situational awareness of unmanned aerial vehicles. In: Proc. of the Int. Conference on Unmanned Aircraft Systems (ICUAS), pp. 179–188 (2013)

  16. Ge, S., Cui, Y.: Dynamic motion planning for mobile robots using potential field method. Auton. Robot. 13(3), 207–222 (2002)

    Article  MATH  Google Scholar 

  17. Grzonka, S., Grisetti, G., Burgard, W.: Towards a navigation system for autonomous indoor flying. In: Proc. of the IEEE Int. Conference on Robotics and Automation (ICRA) (2009)

  18. Grzonka, S., Grisetti, G., Burgard, W.: A fully autonomous indoor quadrotor. IEEE Trans. on Robotics 28(1), 90–100 (2012)

    Article  Google Scholar 

  19. Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. on Systems Science and Cybernetics, vol. 4 (1968)

  20. Heng, L., Honegger, D., Lee, G.H., Meier, L., Tanskanen, P., Fraundorfer, F., Pollefeys, M.: Autonomous visual mapping and exploration with a micro aerial vehicle. J. Field Robot. 31(4), 654–675 (2014)

    Article  Google Scholar 

  21. Holz, D., Nieuwenhuisen, M., Droeschel, D., Schreiber, M., Behnke, S.: Towards mulimodal omnidirectional obstacle detection for autonomous unmanned aerial vehicles. In: Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. (ISPRS), vol. XL-1/W2, pp. 201–206 (2013)

  22. Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W.: OctoMap: An efficient probabilistic 3D mapping framework based on octrees. Auton. Robot. 34, 189–206 (2013)

    Article  Google Scholar 

  23. Huh, S., Shim, D., Kim, J.: Integrated navigation system using camera and gimbaled laser scanner for indoor and outdoor autonomous flight of UAVs. In: Proc. of the IEEE/RSJ Int. Conference on Intelligent Robots and Systems (IROS), pp. 3158– 3163 (2013)

  24. Israelsen, J., Beall, M., Bareiss, D., Stuart, D., Keeney, E., van den Berg, J.: Automatic collision avoidance for manually tele-operated unmanned aerial vehicles. In: Proc. of the IEEE Int. Conference on Robotics and Automation (ICRA) (2014)

  25. Kohlbrecher, S., Meyer, J., von Stryk, O., Klingauf, U.: A flexible and scalable SLAM system with full 3D motion estimation. In: Proc. of the IEEE Int. Symposium on Safety, Security and Rescue Robotics (SSRR) (2011)

  26. Kuemmerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: g 2o: A general framework for graph optimization. In: Proc. of the IEEE Int. Conference on Robotics and Automation (ICRA), pp. 3607–3613 (2011)

  27. Luo, C., Espinosa, A.P., Pranantha, D., Gloria, A.D.: Multi-robot search and rescue team. In: Proc. of the IEEE Int. Symposium on Safety, Security and Rescue Robotics (SSRR) (2011)

  28. MacAllister, B., Butzke, J., Kushleyev, A., Pandey, H., Likhachev, M.: Path planning for non-circular micro aerial vehicles in constrained environments. In: Proc. of the IEEE Int. Conference on Robotics and Automation (ICRA) (2013)

  29. Maddern, W., Harrison, A., Newman, P.: Lost in translation (and rotation): Fast extrinsic calibration for 2D and 3D LIDARs. In: Proc. of the IEEE Int. Conference on Robotics and Automation (ICRA) (2012)

  30. Magnusson, M., Duckett, T., Lilienthal, A.J.: Scan registration for autonomous mining vehicles using 3D-NDT. Journal of Field Robotics 24(10), 803–827 (2007)

    Article  Google Scholar 

  31. Magree, D., Mooney, J.G., Johnson, E.N.: Monocular visual mapping for obstacle avoidance on UAVs. J. Intell. Robot. Syst. 74(1-2), 17–26 (2014)

    Article  Google Scholar 

  32. Meier, L., Tanskanen, P., Heng, L., Lee, G., Fraundorfer, F., Pollefeys, M.: PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision. Auton. Robot. 33(1-2), 21–39 (2012)

    Article  Google Scholar 

  33. Michael, N., Shen, S., Mohta, K., Kumar, V., Nagatani, K., Okada, Y., Kiribayashi, S., Otake, K., Yoshida, K., Ohno, K., Takeuchi, E., Tadokoro, S.: Collaborative mapping of an earthquake-damaged building via ground and aerial robots. In: Int. Conf. on Field and Service Robotics (FSR) (2012)

  34. Montemerlo, M., Becker, J., Bhat, S., Dahlkamp, H., Dolgov, D., Ettinger, S., Haehnel, D., Hilden, T., Hoffmann, G., Huhnke, B., et al.: Junior: The stanford entry in the urban challenge. J. Field Robot. 25(9), 569–597 (2008)

    Article  Google Scholar 

  35. Mori, T., Scherer, S.: First results in detecting and avoiding frontal obstacles from a monocular camera for micro unmanned aerial vehicles. In: Proc. of the IEEE Int. Conference on Robotics and Automation (ICRA) (2013)

  36. Morris, W., Dryanovski, I., Xiao, J., Member, S.: 3D indoor mapping for micro-UAVs using hybrid range finders and multi-volume occupancy grids. In: RSS 2010 workshop on RGB-D: Advanced Reasoning with Depth Cameras (2010)

  37. Nieuwenhuisen, M., Behnke, S.: Hierarchical planning with 3D local multiresolution obstacle avoidance for micro aerial vehicles. In: Proceedings of the Joint Int. Symposium on Robotics (ISR) and the German Conference on Robotics (ROBOTIK) (2014a)

  38. Nieuwenhuisen, M., Behnke, S.: Layered mission and path planning for MAV navigation with partial environment knowledge. In: Proc. of the Int. Conference on Intelligent Autonomous Systems (IAS) (2014b)

  39. Nolan, A., Serrano, D., Sabaté, A.H., Mussarra, D.P., Pena, A.M.L.: Obstacle mapping module for quadrotors on outdoor search and rescue operations. In: Int. Micro Air Vehicle Conf. and Flight Competition (IMAV) (2013)

  40. Nuechter, A., Lingemann, K., Hertzberg, J., Surmann, H.: 6D SLAM with approximate data association. In: Int. Conf. on Advanced Robotics (ICAR), pp. 242–249 (2005)

  41. Olson, E.: AprilTag: A robust and flexible visual fiducial system. In: Proc. of the IEEE Int. Conference on Robotics and Automation (ICRA) (2011)

  42. Park, J., Kim, Y.: 3D shape mapping of obstacle using stereo vision sensor on quadrotor UAV. In: AIAA Guidance, Navigation, and Control Conference (2014)

  43. Quigley, M., Conley, K., Gerkey, B.P., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software (2009)

  44. Ross, S., Melik-Barkhudarov, N., Shankar, K.S., Wendel, A., Dey, D., Bagnell, J.A., Hebert, M.: Learning monocular reactive uav control in cluttered natural environments. In: Proc. of the IEEE Int. Conference on Robotics and Automation (ICRA) (2013)

  45. Roy, N., Burgard, W., Fox, D., Thrun, S.: Coastal navigation-mobile robot navigation with uncertainty in dynamic environments. In: Proc. of the IEEE Int. Conference on Robotics and Automation (ICRA) (1999)

  46. Ryde, J., Hu, H.: 3D mapping with multi-resolution occupied voxel lists. Auton. Robot. 28, 169–185 (2010)

    Article  Google Scholar 

  47. Schadler, M., Stückler, J., Behnke, S.: Multi-resolution surfel mapping and real-time pose tracking using a continuously rotating 2D laser scanner. In: Proceedings of 11th IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) (2013)

  48. Schauwecker, K., Zell, A.: On-board dual-stereo-vision for the navigation of an autonomous MAV. J. Intell. Robot. Syst. 74(1-2), 1–16 (2014)

    Article  Google Scholar 

  49. Schmid, K., Lutz, P., Tomic, T., Mair, E., Hirschmüller, H.: Autonomous vision-based micro air vehicle for indoor and outdoor navigation. J. Field Robot. 31(4), 537–570 (2014)

    Article  Google Scholar 

  50. Schneider, J., Läbe, T., Förstner, W.: Incremental real-time bundle adjustment for multi-camera systems with points at infinity. In: ISPRS Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XL-1/W2 (2013)

  51. Shen, S., Michael, N., Kumar, V.: Autonomous multi-floor indoor navigation with a computationally constrained micro aerial vehicle. In: Proc. of the IEEE Int. Conference on Robotics and Automation (ICRA) (2011)

  52. 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: Proc. of the IEEE Int. Conference on Robotics and Automation (ICRA) (2014)

  53. Stoyanov, T., Lilienthal, A.: Maximum likelihood point cloud acquisition from a mobile platform. In: Proc. of the Int. Conf. on Advanced Robotics (ICAR), pp. 1–6 (2009)

  54. Stückler, J., Behnke, S.: Multi-resolution surfel maps for efficient dense 3D modeling and tracking. J. Vis. Commun. Image Represent. 25(1), 137–147 (2014)

    Article  Google Scholar 

  55. Takahashi, M., Schulein, G., Whalley, M.: Flight control law design and development for an autonomous rotorcraft. In: Proceedings of the 64th Annual Forum of the American Helicopter Society (2008)

  56. Thrun, S., Diel, M., Hähnel, D.: Scan alignment and 3-D surface modeling with a helicopter platform. In: Int. Conf. on Field and Service Robotics (FSR) of Springer Tracts in Advanced Robotics, vol. 24, pp. 287–297. Springer (2003)

  57. Tomić, T., Schmid, K., Lutz, P., Domel, A., Kassecker, M., Mair, E., Grixa, I., Ruess, F., Suppa, M., Burschka, D.: Toward a fully autonomous UAV: Research platform for indoor and outdoor urban search and rescue. IEEE Robot. Autom. Mag. 19(3), 46–56 (2012)

    Article  Google Scholar 

  58. Tripathi, A., G Raja, R., Padhi, R.: Reactive collision avoidance of UAVs with stereovision camera sensors using UKF. In: Advances in Control and Optimization of Dynamical Systems, pp. 1119–1125 (2014)

Download references

Author information

Authors and Affiliations

  1. Autonomous Intelligent Systems Group, University of Bonn, Bonn, Germany

    Matthias Nieuwenhuisen, David Droeschel, Marius Beul & Sven Behnke

Authors
  1. Matthias Nieuwenhuisen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Droeschel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marius Beul
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sven Behnke
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matthias Nieuwenhuisen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nieuwenhuisen, M., Droeschel, D., Beul, M. et al. Autonomous Navigation for Micro Aerial Vehicles in Complex GNSS-denied Environments. J Intell Robot Syst 84, 199–216 (2016). https://doi.org/10.1007/s10846-015-0274-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-015-0274-3

Keywords

Search

Navigation