Abstract
The challenge for unmanned aerial vehicles to sense and avoid obstacles becomes even harder if narrow passages have to be crossed. An approach to solve a mission scenario that tackles the problem of such narrow passages is presented here. The task is to fly an unmanned helicopter autonomously through a course with gates that are only slightly larger than the vehicle itself. A camera is installed on the vehicle to detect the gates. Using vehicle localization data from a navigation solution, camera alignment and global gate positions are estimated simultaneously. The presented algorithm calculates the desired target waypoints to fly through the gates. Furthermore, the paper presents a mission execution plan that instructs the vehicle to search for a gate, to fly through it after successful detection, and to search for a proceeding one. All algorithms are designed to run onboard the vehicle so that no interaction with the ground control station is necessary, making the vehicle completely autonomous. To develop and optimize algorithms, and to prove the correctness and accuracy of vision-based gate detection under real operational conditions, gate positions are searched in images taken from manual helicopter flights. Afterwards, the integration of visual sensing and mission control is proven. The paper presents results from full autonomous flight where the helicopter searches and flies through a gate without operator actions.
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Adolf, F., Andert, F., Lorenz, S., Goormann, L., Dittrich, J.: An unmanned helicopter for autonomous flights in urban terrain. In: Kröger, T., Wahl, F.M. (eds.) Advances in Robotics Research, pp. 275–285. Springer, Berlin (2009)
Andert, F., Goormann, L.: A fast and small 3-D obstacle model for autonomous applications. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2883–2889 (2008)
Arun, K.S., Huang, T.S., Blostein, S.D.: Least-squares fitting of two 3-d point sets. IEEE Trans. Pattern Anal. Mach. Intell. 9(5), 698–700 (1987)
Bernatz, A., Thielecke, F.: Navigation of a low flying vtol aircraft with the help of a downwards pointing camera. In: AIAA Guidance, Navigation and Control Conference (2004)
Brown, D.C.: Close-range camera calibration. Photogramm. Eng. 8, 855–866 (1971)
Caballero, F., Merino, L., Ferruz, J., Ollero, A.: Vision-based odometry and slam for medium and high altitude flying uavs. J. Intell. Robot. Syst. 54(1–3), 137–161 (2009)
Corke, P., Lobo, J., Dias, J.: An introduction to inertial and visual sensing. Int. J. Rob. Res. 26(6), 519–535 (2007)
Dissanayake, M.W.M.G., Newman, P., Clark, S., Durrant-Whyte, H.F., Csorba, M.: A solution to the simultaneous localization and mapping (slam) problem. IEEE Trans. Robot. Autom. 17(3), 229–241 (2001)
Dittrich, J.S., Bernatz, A., Thielecke, F.: Intelligent systems research using a small autonomous rotorcraft testbed. In: 2nd AIAA Unmanned Unlimited Conference, Workshop and Exhibit (2003)
Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Graphics and Image Processing 24(6), 381–395 (1981)
Hrabar, S.: 3D path planning and stereo-based obstacle avoidance for rotorcraft uavs. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 807–814 (2008)
Lorusso, A., Eggert, D.W., Fisher, R.B.: Comparison of four algorithms for estimating 3-D rigid transformations. In: British Machine Vision Conference, pp. 237–246 (1995)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: International Joint Conference on Artificial Intelligence, pp. 674–679 (1981)
Ludington, B., Johnson, E., Vachtsevanos, G.: Vision based navigation and target tracking for unmanned aerial vehicles. In: Valavanis, K.P. (ed.) Advances in Unmanned Aerial Vehicles, pp. 245–266. Springer, Dordrecht (2007)
Nüchter, A., Lingemann, K., Hertzberg, J., Surmann, H.: 6D slam with approximate data association. In: 12th International Conference on Advanced Robotics, pp. 242–249 (2005)
Scherer, S., Singh, S., Chamberlain, L., Saripalli, S.: Flying fast and low among obstacles. In: IEEE International Conference on Robotics and Automation, pp. 2023–2029 (2007)
Sharp, C.S., Shakernia, O., Sastry, S.S.: A vision system for landing an unmanned aerial vehicle. In: IEEE International Conference on Robotics and Automation, pp. 2793–2798 (2002)
Shim, D.H., Chung, H., Sastry, S.S.: Conflict-free navigation in unknown urban environments. IEEE Robot. Autom. Mag. 13(3), 27–33 (2006)
Strelow, D., Singh, S.: Optimal motion estimation from visual and inertial data. In: IEEE Workshop on Applications of Computer Vision, pp. 314–319 (2002)
Thrun, S.: Robotic mapping: A survey. In: Lakemeyer, G., Nebel, B. (eds.) Exploring Artificial Intelligence in the New Millenium. Morgan Kaufmann, San Francisco (2002)
Watanabe, Y., Calisey, A.J., Johnson, E.N.: Vision-based obstacle avoidance for uavs. In: AIAA Guidance, Navigation and Control Conference and Exhibit (2007)
Watanabe, Y., Fabiani, P., Mouyon, P.: Research perspectives in uav visual target tracking in uncertain environments. In: Workshop on Visual Guidance Systems for Small Autonomous Aerial Vehicles, IEEE IROS Conference (2008)
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Andert, F., Adolf, FM., Goormann, L. et al. Autonomous Vision-Based Helicopter Flights Through Obstacle Gates. J Intell Robot Syst 57, 259–280 (2010). https://doi.org/10.1007/s10846-009-9357-3
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DOI: https://doi.org/10.1007/s10846-009-9357-3