Journal of Intelligent & Robotic Systems

, Volume 84, Issue 1–4, pp 199–216 | Cite as

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

  • Matthias Nieuwenhuisen
  • David Droeschel
  • Marius Beul
  • Sven Behnke
Article

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.

Keywords

GNSS-denied localization Muliresolutional mapping Obstacle detection Multi-layered planning Trajectory generation State estimation 

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References

  1. 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)CrossRefGoogle Scholar
  2. 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)Google Scholar
  3. 3.
    Applegate, D., Bixby, R., Chvatal, V., Cook, W: Concorde TSP solver (2006)Google Scholar
  4. 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)Google Scholar
  5. 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)CrossRefGoogle Scholar
  6. 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)Google Scholar
  7. 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)Google Scholar
  8. 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)Google Scholar
  9. 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)Google Scholar
  10. 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)Google Scholar
  11. 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)Google Scholar
  12. 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)Google Scholar
  13. 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)Google Scholar
  14. 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)CrossRefGoogle Scholar
  15. 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)Google Scholar
  16. 16.
    Ge, S., Cui, Y.: Dynamic motion planning for mobile robots using potential field method. Auton. Robot. 13(3), 207–222 (2002)CrossRefMATHGoogle Scholar
  17. 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)Google Scholar
  18. 18.
    Grzonka, S., Grisetti, G., Burgard, W.: A fully autonomous indoor quadrotor. IEEE Trans. on Robotics 28(1), 90–100 (2012)CrossRefGoogle Scholar
  19. 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)Google Scholar
  20. 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)CrossRefGoogle Scholar
  21. 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)Google Scholar
  22. 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)CrossRefGoogle Scholar
  23. 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)Google Scholar
  24. 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)Google Scholar
  25. 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)Google Scholar
  26. 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)Google Scholar
  27. 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)Google Scholar
  28. 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)Google Scholar
  29. 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)Google Scholar
  30. 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)CrossRefGoogle Scholar
  31. 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)CrossRefGoogle Scholar
  32. 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)CrossRefGoogle Scholar
  33. 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)Google Scholar
  34. 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)CrossRefGoogle Scholar
  35. 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)Google Scholar
  36. 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)Google Scholar
  37. 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)Google Scholar
  38. 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)Google Scholar
  39. 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)Google Scholar
  40. 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)Google Scholar
  41. 41.
    Olson, E.: AprilTag: A robust and flexible visual fiducial system. In: Proc. of the IEEE Int. Conference on Robotics and Automation (ICRA) (2011)Google Scholar
  42. 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)Google Scholar
  43. 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)Google Scholar
  44. 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)Google Scholar
  45. 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)Google Scholar
  46. 46.
    Ryde, J., Hu, H.: 3D mapping with multi-resolution occupied voxel lists. Auton. Robot. 28, 169–185 (2010)CrossRefGoogle Scholar
  47. 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)Google Scholar
  48. 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)CrossRefGoogle Scholar
  49. 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)CrossRefGoogle Scholar
  50. 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)Google Scholar
  51. 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)Google Scholar
  52. 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)Google Scholar
  53. 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)Google Scholar
  54. 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)CrossRefGoogle Scholar
  55. 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)Google Scholar
  56. 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)Google Scholar
  57. 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)CrossRefGoogle Scholar
  58. 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)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Matthias Nieuwenhuisen
    • 1
  • David Droeschel
    • 1
  • Marius Beul
    • 1
  • Sven Behnke
    • 1
  1. 1.Autonomous Intelligent Systems GroupUniversity of BonnBonnGermany

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