Abstract
During last decade the scientific research on Unmanned Aerial Vehicless (UAVs) increased spectacularly and led to the design of multiple types of aerial platforms. The major challenge today is the development of autonomously operating aerial agents capable of completing missions independently of human interaction. To this extent, visual sensing techniques have been integrated in the control pipeline of the UAVs in order to enhance their navigation and guidance skills. The aim of this article is to present a comprehensive literature review on vision based applications for UAVs focusing mainly on current developments and trends. These applications are sorted in different categories according to the research topics among various research groups. More specifically vision based position-attitude control, pose estimation and mapping, obstacle detection as well as target tracking are the identified components towards autonomous agents. Aerial platforms could reach greater level of autonomy by integrating all these technologies onboard. Additionally, throughout this article the concept of fusion multiple sensors is highlighted, while an overview on the challenges addressed and future trends in autonomous agent development will be also provided.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
U.S Department of Transportation: Federal Aviation Administration. https://www.faa.gov/uas/faqs/
U.K Ministry of Defence: Unmanned Aircraft Systems: Terminology, Definitions and Classification
U.S. Department of Defense: Standard practice for system safety. MIL-STD-882D (2000)
Huang, H.-M.: Autonomy levels for unmanned systems (ALFUS) framework, volume I: Terminology, Version 2.0 (2008)
Valavanis, K.P.: Advances in Unmanned Aerial Vehicles: State of the Art and the Road to Autonomy, vol. 33. Springer Science & Business Media (2008)
YAMAHA: RMAX. http://rmax.yamaha-motor.com.au/features/
Ascending Technologies: AscTec NEO. http://www.asctec.de/en/uav-uas-drones-rpas-roav/asctec-firefly/
ShadowAir: Super Bat ShadowAir. http://www.shadowair.com
Association Unmanned Aerial Vehicle Systems: Civil and Commercial UAS Applications. https://www.uavs.org/commercial
Mejias, L., Correa, J.F., Mondragón, I., Campoy, P.: Colibri: A vision-guided uav for surveillance and visual inspection (2007)
Araar, O., Aouf, N.: A new hybrid approach for the visual servoing of vtol uavs from unknown geometries. In: IEEE 22nd Mediterranean Conference of Control and Automation (MED), pp. 1425–1432. IEEE (2014)
Carrillo, L.R.G., López, A.E.D., Lozano, R., Pégard, C.: Combining stereo vision and inertial navigation system for a quad-rotor uav. J. Intelli. Robot. Syst. 65(1-4), 373–387 (2012)
Max Botix: XL-MaxSonar-EZ4 Ultrasonic Sensor. http://www.maxbotix.com
SkyBotix AG: VI sensor. http://www.skybotix.com/
TeraRanger: TeraRanger Rotating Lidar. http://www.teraranger.com/products/teraranger-lidar/
Matrix Vision: mvBlueFOX3 Camera. https://www.matrix-vision.com/USB3-vision-camera-mvbluefox3.html
Szeliski, R.: Computer Vision: Algorithms and Applications. Springer Science & Business Media (2010)
Kendoul, F.: Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems. J. Field Robot. 29(2), 315–378 (2012)
Hutchinson, S., Hager, G.D., Corke, P.I.: A tutorial on visual servo control. IEEE Trans. Robot. Autom. 12(5), 651–670 (1996)
Corke, P.: Robotics, Vision and Control: Fundamental Algorithms in MATLAB, vol. 73. Springer Science & Business Media (2011)
Asl, H.J., Oriolo, G., Bolandi, H.: An adaptive scheme for image-based visual servoing of an underactuated uav. IEEE Trans. Robot. Autom. 29(1) (2014)
Ozawa, R., Chaumette, F.: Dynamic visual servoing with image moments for a quadrotor using a virtual spring approach. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 5670–5676. IEEE (2011)
Araar, O., Aouf, N.: Visual servoing of a quadrotor uav for autonomous power lines inspection. In: 22nd Mediterranean Conference of Control and Automation (MED), pp. 1418–1424. IEEE (2014)
Azinheira, J.R., Rives, P.: Image-based visual servoing for vanishing features and ground lines tracking: Application to a uav automatic landing. Int. J. Optomechatron. 2(3), 275–295 (2008)
Sa, I., Hrabar, S., Corke, P.: Inspection of pole-like structures using a vision-controlled vtol uav and shared autonomy. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4819–4826. IEEE (2014)
Mills, S.J., Ford, J.J., Mejías, L.: Vision based control for fixed wing uavs inspecting locally linear infrastructure using skid-to-turn maneuvers. J. Intell. Robot. Syst. 61(1–4), 29–42 (2011)
Peliti, P., Rosa, L., Oriolo, G., Vendittelli, M.: Vision-based loitering over a target for a fixed-wing uav. In: Proceedings of the 10th International IFAC Symposium on Robot Control (2012)
Guenard, N., Hamel, T., Mahony, R.: A practical visual servo control for an unmanned aerial vehicle. IEEE Trans. Robot. 24(2), 331–340 (2008)
Metni, N., Hamel, T.: A uav for bridge inspection: Visual servoing control law with orientation limits. Autom. Construct. 17(1), 3–10 (2007)
Hamel, T., Mahony, R.: Image based visual servo control for a class of aerial robotic systems. Automatica 43(11), 1975–1983 (2007)
Chriette, A.: An analysis of the zero-dynamics for visual servo control of a ducted fan uav. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 2515–2520. IEEE (2006)
Le Bras, F., Mahony, R., Hamel, T., Binetti, P.: Adaptive filtering and image based visual servo control of a ducted fan flying robot. In: 45th IEEE Conference on Decision and Control, pp. 1751–1757. IEEE (2006)
Kim, S., Choi, S., Lee, H., Kim, H.J.: Vision-based collaborative lifting using quadrotor uavs. In: 14th International Conference on Control, Automation and Systems (ICCAS), pp. 1169–1174. IEEE (2014)
Barajas, M., Dávalos-Viveros, J.P., Garcia-Lumbreras, S., Gordillo, J.L.: Visual servoing of uav using cuboid model with simultaneous tracking of multiple planar faces. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 596–601. IEEE (2013)
Fahimi, F., Thakur, K.: An alternative closed-loop vision-based control approach for unmanned aircraft systems with application to a quadrotor. In: International Conference on Unmanned Aircraft Systems (ICUAS), pp. 353–358. IEEE (2013)
Lee, D., Ryan, T., Kim, H.J.: Autonomous landing of a vtol uav on a moving platform using image-based visual servoing. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 971–976. IEEE (2012)
Huh, S., Shim, D.H.: A vision-based automatic landing method for fixed-wing uavs. J. Intell. Robot. Syst. 57(1–4), 217–231 (2010)
Salazar, S., Romero, H., Gomez, J., Lozano, R.: Real-time stereo visual servoing control of an uav having eight-rotors. In: 6th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), pp. 1–11. IEEE (2009)
Dib, A., Zaidi, N., Siguerdidjane, H.: Robust control and visual servoing of an uav. In: 17th IFAC World Congress 2008, pp. CD–ROM (2008)
Kendoul, F., Fantoni, I., Nonami, K.: Optic flow-based vision system for autonomous 3d localization and control of small aerial vehicles. Robot. Autonom. Syst. 57(6), 591–602 (2009)
Eberli, D., Scaramuzza, D., Weiss, S., Siegwart, R.: Vision based position control for mavs using one single circular landmark. J. Intell. Robot. Syst. 61(1–4), 495–512 (2011)
Kendoul, F., Fantoni, I., Lozano, R.: Adaptive vision-based controller for small rotorcraft uavs control and guidance. In: Proceedings of the 17th IFAC world congress, pp. 6–11 (2008)
Lange, S., Sunderhauf, N., Protzel, P.: A vision based onboard approach for landing and position control of an autonomous multirotor uav in gps-denied environments. In: International Conference on Advanced Robotics, 2009. ICAR 2009, pp. 1–6. IEEE (2009)
Alkowatly, M.T., Becerra, V.M., Holderbaum, W.: Bioinspired autonomous visual vertical control of a quadrotor unmanned aerial vehicle. J. Guid. Control Dyn., 1–14 (2014)
Ghadiok, V., Goldin, J., Ren, W.: On the design and development of attitude stabilization, vision-based navigation, and aerial gripping for a low-cost quadrotor. Autonom. Robots 33(1–2), 41–68 (2012)
Fucen, Z., Haiqing, S., Hong, W.: The object recognition and adaptive threshold selection in the vision system for landing an unmanned aerial vehicle. In: International Conference on Information and Automation (ICIA), pp. 117–122. IEEE (2009)
Zhao, Y., Pei, H.: An improved vision-based algorithm for unmanned aerial vehicles autonomous landing. Phys. Proced. 33, 935–941 (2012)
Artieda, J., Sebastian, J.M., Campoy, P., Correa, J.F., Mondragón, I.F., Martínez, C., Olivares, M.: Visual 3-d slam from uavs. J. Intell. Robot. Syst. 55(4–5), 299–321 (2009)
Faessler, M., Fontana, F., Forster, C., Mueggler, E., Pizzoli, M., Scaramuzza, D.: Autonomous, vision-based flight and live dense 3d mapping with a quadrotor micro aerial vehicle. J. Field Robot. (2015)
Fraundorfer, F., Heng, L., Honegger, D., Lee, G.H., Meier, L., Tanskanen, P., Pollefeys, M.: Vision-based autonomous mapping and exploration using a quadrotor mav. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4557–4564. IEEE (2012)
Schmid, K., Lutz, P., Tomić, T., Mair, E., Hirschmüller, H.: Autonomous vision-based micro air vehicle for indoor and outdoor navigation. J. Field Robot. 31(4), 537–570 (2014)
Harmat, A., Trentini, M., Sharf, I.: Multi-camera tracking and mapping for unmanned aerial vehicles in unstructured environments. J. Intell. Robot. Syst., 1–27 (2014)
Leishman, R.C., McLain, T.W., Beard, R.W.: Relative navigation approach for vision-based aerial gps-denied navigation. J. Intell. Robot. Syst. 74(1–2), 97–111 (2014)
Forster, C., Pizzoli, M., Scaramuzza, D.: Svo: Fast semi-direct monocular visual odometry. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 15–22. IEEE (2014)
Forster, C., Faessler, M., Fontana, F., Werlberger, M., Scaramuzza, D.: Continuous on-board monocular-vision-based elevation mapping applied to autonomous landing of micro aerial vehicles. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 111–118. IEEE (2015)
Lynen, S., Achtelik, M.W., Weiss, S., Chli, M., Siegwart, R.: A robust and modular multi-sensor fusion approach applied to mav navigation. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3923–3929. IEEE (2013)
Pizzoli, M., Forster, C., Scaramuzza, D.: Remode: Probabilistic, monocular dense reconstruction in real time. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 2609–2616. IEEE (2014)
Fu, C., Olivares-Mendez, M.A., Suarez-Fernandez, R., Campoy, P.: Monocular visual-inertial slam-based collision avoidance strategy for fail-safe uav using fuzzy logic controllers. J. Intell. Robot. Syst. 73(1–4), 513–533 (2014)
Wang, T., Wang, C., Liang, J., Zhang, Y.: Rao-blackwellized visual slam for small uavs with vehicle model partition. Indus. Robot: Int. J. 41(3), 266–274 (2014)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Magree, D., Johnson, E.N.: Combined laser and vision-aided inertial navigation for an indoor unmanned aerial vehicle. In: American Control Conference (ACC), pp. 1900–1905. IEEE (2014)
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)
Mahalanobis, P.C.: On the generalised distance in statistics 2(1), 49–55 (1936)
Huh, S., Shim, D.H., Kim, J.: Integrated navigation system using camera and gimbaled laser scanner for indoor and outdoor autonomous flight of uavs. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3158–3163. IEEE (2013)
Wang, C.L., Wang, T.M., Liang, J.H., Zhang, Y.C., Zhou, Y.: Bearing-only visual slam for small unmanned aerial vehicles in gps-denied environments. Int. J. Autom. Comput. 10(5), 387–396 (2013)
Nemra, A., Aouf, N.: Robust cooperative uav visual slam. In: IEEE 9th International Conference on Cybernetic Intelligent Systems (CIS), pp. 1–6. IEEE (2010)
Min, J., Jeong, Y., Kweon, I.S.: Robust visual lock-on and simultaneous localization for an unmanned aerial vehicle. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 93–100. IEEE (2010)
Jama, M., Schinstock, D.: Parallel tracking and mapping for controlling vtol airframe. J. Control Sci. Eng. 2011, 26 (2011)
Törnqvist, D., Schön, T.B., Karlsson, R., Gustafsson, F.: Particle filter slam with high dimensional vehicle model. J. Intell. Robot. Syst 55(4–5), 249–266 (2009)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey vision conference, vol. 15, p. 50. Manchester (1988)
Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B., et al.: Fastslam: A factored solution to the simultaneous localization and mapping problem. In: AAAI/IAAI, pp. 593–598 (2002)
Bryson, M., Sukkarieh, S.: Building a robust implementation of bearing-only inertial slam for a uav. J. Field Robot. 24(1–2), 113–143 (2007)
Kim, J., Sukkarieh, S.: Real-time implementation of airborne inertial-slam. Robot. Autonom. Syst. 55(1), 62–71 (2007)
Liming Luke Chen, D.R.M., Dr Matthias Steinbauer, P., Mossel, A., Leichtfried, M., Kaltenriner, C., Kaufmann, H.: Smartcopter: Enabling autonomous flight in indoor environments with a smartphone as on-board processing unit. Int. J. Pervas. Comput. Commun. 10(1), 92–114 (2014)
Yang, J., Dani, A., Chung, S.J., Hutchinson, S.: Inertial-aided vision-based localization and mapping in a riverine environment with reflection measurements. In: AIAA Guidance, Navigation, and Control Conference. Boston (2013)
Zhang, R., Liu, H.H.: Vision-based relative altitude estimation of small unmanned aerial vehicles in target localization. In: American Control Conference (ACC), 2011, pp. 4622–4627. IEEE (2011)
Nourani-Vatani, N., Pradalier, C.: Scene change detection for vision-based topological mapping and localization. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3792–3797. IEEE (2010)
Canny, J.: A computational approach to edge detection. Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986)
Caballero, F., Merino, L., Ferruz, J., Ollero, A.: Unmanned aerial vehicle localization based on monocular vision and online mosaicking. J. Intell. Robot. Syst. 55(4–5), 323–343 (2009)
Lee, S.J., Kim, J.H.: Development of a quadrocoptor robot with vision and ultrasonic sensors for distance sensing and mapping. In: Robot Intelligence Technology and Applications 2012, pp. 477–484. Springer (2013)
Engel, J., Sturm, J., Cremers, D.: Scale-aware navigation of a low-cost quadrocopter with a monocular camera. Robot. Autonom. Syst. 62(11), 1646–1656 (2014)
Chowdhary, G., Johnson, E.N., Magree, D., Wu, A., Shein, A.: Gps-denied indoor and outdoor monocular vision aided navigation and control of unmanned aircraft. J. Field Robot. 30(3), 415–438 (2013)
Zhang, X., Xian, B., Zhao, B., Zhang, Y.: Autonomous flight control of a nano quadrotor helicopter in a gps-denied environment using on-board vision. IEEE Trans. Ind. Electron. 62 (10), 6392–6403 (2015)
Harmat, A., Trentini, M., Sharf, I.: Multi-camera tracking and mapping for unmanned aerial vehicles in unstructured environments. J. Intell. Robot. Syst. 78(2), 291–317 (2015)
Bloesch, M., Omari, S., Hutter, M., Siegwart, R.: Robust visual inertial odometry using a direct ekf-based approach. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015, pp. 298–304. IEEE (2015)
Shen, S., Mulgaonkar, Y., Michael, N., Kumar, V.: Vision-based state estimation for autonomous rotorcraft mavs in complex environments. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 1758–1764. IEEE (2013)
Troiani, C., Martinelli, A., Laugier, C., Scaramuzza, D.: Low computational-complexity algorithms for vision-aided inertial navigation of micro aerial vehicles. Robot. Autonom. Syst. 69, 80–97 (2015)
Loianno, G., Watterson, M., Kumar, V.: Visual inertial odometry for quadrotors on se (3). In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 1544–1551. IEEE (2016)
Loianno, G., Thomas, J., Kumar, V.: Cooperative localization and mapping of mavs using rgb-d sensors. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 4021–4028. IEEE (2015)
Piasco, N., Marzat, J., Sanfourche, M.: Collaborative localization and formation flying using distributed stereo-vision. In: IEEE International Conference on Robotics and Automation. Stockholm (2016)
Nieuwenhuisen, M., Droeschel, D., Beul, M., Behnke, S.: Obstacle detection and navigation planning for autonomous micro aerial vehicles. In: International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1040–1047. IEEE (2014)
Schmid, K., Tomic, T., Ruess, F., Hirschmuller, H., Suppa, M.: Stereo vision based indoor/outdoor navigation for flying robots. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3955–3962. IEEE (2013)
Heng, L., Meier, L., Tanskanen, P., Fraundorfer, F., Pollefeys, M.: Autonomous obstacle avoidance and maneuvering on a vision-guided mav using on-board processing. In: IEEE international conference on Robotics and automation (ICRA), pp. 2472–2477. IEEE (2011)
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)
Xiaoyi, D., Qinhua, Z.: Research on laser-assisted odometry of indoor uav with monocular vision. In: 3rd Annual International Conference on Cyber Technology in Automation, Control and Intelligent Systems (CYBER), pp. 165–169. IEEE (2013)
Gosiewski, Z., Ciesluk, J., Ambroziak, L.: Vision-based obstacle avoidance for unmanned aerial vehicles. In: 4th International Congress on Image and Signal Processing (CISP), vol. 4, pp. 2020–2025. IEEE (2011)
Yuan, C., Recktenwald, F., Mallot, H.A.: Visual steering of uav in unknown environments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3906–3911. IEEE (2009)
Shah, S.I.A., Johnson, E.N.: 3d obstacle detection using a single camera. In: AIAA guidance, navigation, and control conference (AIAA), vol. 5678 (2009)
Lee, J.O., Lee, K.H., Park, S.H., Im, S.G., Park, J.: Obstacle avoidance for small uavs using monocular vision. Aircraft Eng. Aeros. Technol. 83(6), 397–406 (2011)
Watanabe, Y., Fabiani, P., Le Besnerais, G.: Towards a uav visual air-to-ground target tracking in an urban environment
Watanabe, Y., Lesire, C., Piquereau, A., Fabiani, P., Sanfourche, M., Le Besnerais, G.: The onera ressac unmanned autonomous helicopter: Visual air-to-ground target tracking in an urban environment. In: American Helicopter Society 66th Annual Forum (AHS 2010) (2010)
Jian, L., Xiao-min, L.: Vision-based navigation and obstacle detection for uav. In: International Conference on Electronics, Communications and Control (ICECC), pp. 1771–1774. IEEE (2011)
Byrne, J., Cosgrove, M., Mehra, R.: Stereo based obstacle detection for an unmanned air vehicle. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 2830–2835. IEEE (2006)
Yadav, V., Wang, X., Balakrishnan, S.: Neural network approach for obstacle avoidance in 3-d environments for uavs. In: American Control Conference, pp. 6–pp. IEEE (2006)
Srinivasan, M.V., Thurrowgood, S., Soccol, D.: An optical system for guidance of terrain following in uavs. In: International Conference on Video and Signal Based Surveillance (AVSS), pp. 51–51. IEEE (2006)
Hrabar, S., Sukhatme, G., Corke, P., Usher, K., Roberts, J.: Combined optic-flow and stereo-based navigation of urban canyons for a uav. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3309–3316. IEEE (2005)
Olivares-Mendez, M.A., Mejias, L., Campoy, P., Mellado-Bataller, I.: Quadcopter see and avoid using a fuzzy controller. In: Proceedings of the 10th International FLINS Conference on Uncertainty Modeling in Knowledge Engineering and Decision Making (FLINS 2012). World Scientific (2012)
Mohammed, A.D., Morris, T.: An improved camshift algorithm for object detection and extraction
Ahrens, S., Levine, D., Andrews, G., How, J.P.: Vision-based guidance and control of a hovering vehicle in unknown, gps-denied environments. In: International Conference on Robotics and Automation (ICRA), pp. 2643–2648. IEEE (2009)
Shi, J., Tomasi, C.: Good features to track. In: Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 593–600. IEEE (1994)
Lucas, B.D., Kanade, T., et al.: An iterative image registration technique with an application to stereo vision. In: IJCAI, vol. 81, pp. 674–679 (1981)
Mcfadyen, A., Mejias, L., Corke, P., Pradalier, C.: Aircraft collision avoidance using spherical visual predictive control and single point features. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 50–56. IEEE (2013)
Kim, Y., Jung, W., Bang, H.: Visual target tracking and relative navigation for unmanned aerial vehicles in a gps-denied environment. Int. J. Aeronaut. Space Sci. 15(3), 258–266 (2014)
Price, A., Pyke, J., Ashiri, D., Cornall, T.: Real time object detection for an unmanned aerial vehicle using an fpga based vision system. In: International Conference on Robotics and Automation (ICRA), pp. 2854–2859. IEEE (2006)
Jeon, B., Baek, K., Kim, C., Bang, H.: Mode changing tracker for ground target tracking on aerial images from unmanned aerial vehicles (iccas 2013). In: 13th International Conference on Control, Automation and Systems (ICCAS), pp. 1849–1853. IEEE (2013)
Rodriguez, J., Castiblanco, C., Mondragon, I., Colorado, J.: Low-cost quadrotor applied for visual detection of landmine-like objects. In: International Conference on Unmanned Aircraft Systems (ICUAS), pp. 83–88. IEEE (2014)
Gu, A., Xu, J.: Vision based ground marker fast detection for small robotic uav. In: 5th IEEE International Conference on Software Engineering and Service Science (ICSESS), pp. 975–978. IEEE (2014)
Zou, J.T., Tseng, Y.C.: Visual track system applied in quadrotor aerial robot. In: 2012 Third International Conference on Digital Manufacturing and Automation (ICDMA), pp. 1025–1028. IEEE (2012)
Teuliere, C., Eck, L., Marchand, E.: Chasing a moving target from a flying uav. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4929–4934. IEEE (2011)
Zhou, J.: Ekf based object detect and tracking for uav by using visual-attention-model. In: International Conference on Progress in Informatics and Computing (PIC), pp. 168–172. IEEE (2014)
Watanabe, Y., Fabiani, P., Le Besnerais, G.: Simultaneous visual target tracking and navigation in a gps-denied environment. In: International Conference on Advanced Robotics (ICAR), pp. 1–6. IEEE (2009)
Saif, A.S., Prabuwono, A.S., Mahayuddin, Z.R.: Real time vision based object detection from uav aerial images: A conceptual framework. In: Intelligent Robotics Systems: Inspiring the NEXT, pp. 265–274. Springer (2013)
Gaszczak, A., Breckon, T.P., Han, J.: Real-time people and vehicle detection from uav imagery. In: IS&T/SPIE Electronic Imaging, pp. 78,780B–78,780B. International Society for Optics and Photonics (2011)
Li, Z., Ding, J.: Ground moving target tracking control system design for uav surveillance. In: IEEE International Conference on Automation and Logistics, pp. 1458–1463. IEEE (2007)
Maier, J., Humenberger, M.: Movement detection based on dense optical flow for unmanned aerial vehicles. Int. J. Adv. Robot. Syst. 10, 1–11 (2013)
Tarhan, M., Altuġ, E.: A catadioptric and pan-tilt-zoom camera pair object tracking system for uavs. J. Intell. Robot. Syst. 61(1–4), 119–134 (2011)
Majidi, B., Bab-Hadiashar, A.: Aerial tracking of elongated objects in rural environments. Mach. Vis. Appl. 20(1), 23–34 (2009)
Liu, X., Lin, Z., Acton, S.T.: A grid-based bayesian approach to robust visual tracking. Digit. Signal Process. 22(1), 54–65 (2012)
Candamo, J., Kasturi, R., Goldgof, D.: Using color profiles for street detection in low-altitude uav video. In: SPIE Defense, Security, and Sensing, pp. 73,070O–73,070O. International Society for Optics and Photonics (2009)
Pestana, J., Sanchez-Lopez, J.L., Saripalli, S., Campoy, P.: Computer vision based general object following for gps-denied multirotor unmanned vehicles. In: American Control Conference (ACC), pp. 1886–1891. IEEE (2014)
Qadir, A., Semke, W., Neubert, J.: Vision based neuro-fuzzy controller for a two axes gimbal system with small uav. J. Intell. Robot. Syst. 74(3–4), 1029–1047 (2014)
Mondragon, I.F., Campoy, P., Correa, J.F., Mejias, L.: Visual model feature tracking for uav control. In: IEEE International Symposium on Intelligent Signal Processing (WISP), pp. 1–6. IEEE (2007)
Zhao, S., Hu, Z., Yin, M., Ang, K.Z., Liu, P., Wang, F., Dong, X., Lin, F., Chen, B.M., Lee, T.H.: A robust real-time vision system for autonomous cargo transfer by an unmanned helicopter. IEEE Trans. Ind. Electron. 62(2) (2015)
Cichella, V., Kaminer, I., Dobrokhodov, V., Hovakimyan, N.: Coordinated vision-based tracking for multiple uavs. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015, pp. 656–661. IEEE (2015)
Lin, S., Garratt, M.A., Lambert, A.J.: Monocular vision-based real-time target recognition and tracking for autonomously landing an uav in a cluttered shipboard environment. Autonom Robots, 1–21 (2016)
Fu, C., Duan, R., Kircali, D., Kayacan, E.: Onboard robust visual tracking for uavs using a reliable global-local object model. Sensors 16(9), 1406 (2016)
Tomic, T., Schmid, K., Lutz, P., Domel, A., Kassecker, M., Mair, E., Grixa, I.L., 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)
Wang, T., Wang, C., Liang, J., Chen, Y., Zhang, Y.: Vision-aided inertial navigation for small unmanned aerial vehicles in gps-denied environments. Int. J. Adv. Robot. Syst. (2013)
Zhao, S., Lin, F., Peng, K., Chen, B.M., Lee, T.H.: Homography-based vision-aided inertial navigation of uavs in unknown environments. In: AIAA Guidance, Navigation, and Control Conference (2012)
Cocchioni, F., Mancini, A., Longhi, S.: Autonomous navigation, landing and recharge of a quadrotor using artificial vision. In: International Conference on Unmanned Aircraft Systems (ICUAS), pp. 418–429. IEEE (2014)
Carrillo, L.R.G., Flores Colunga, G., Sanahuja, G., Lozano, R.: Quad rotorcraft switching control: An application for the task of path following. IEEE Trans. Control Syst. Technol. 22(4), 1255–1267 (2014)
Lee, D., Kim, Y., Bang, H.: Vision-aided terrain referenced navigation for unmanned aerial vehicles using ground features. Proc. Inst. Mech. Eng. Part G: J. Aeros. Eng. 228(13), 2399–2413 (2014)
Yol, A., Delabarre, B., Dame, A., Dartois, J.E., Marchand, E.: Vision-based absolute localization for unmanned aerial vehicles. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3429–3434. IEEE (2014)
Tardif, J.P., George, M., Laverne, M., Kelly, A., Stentz, A.: Vision-aided inertial navigation for power line inspection. In: 1st International Conference on Applied Robotics for the Power Industry (CARPI), pp. 1–6 (2010)
Sanahuja, G., Castillo, P.: Embedded laser vision system for indoor aerial autonomous navigation. J. Int. Robot. Syst. 69(1–4), 447–457 (2013)
Tippetts, B.J., Lee, D.J., Fowers, S.G., Archibald, J.K.: Real-time vision sensor for an autonomous hovering micro unmanned aerial vehicle. J. Aeros. Comput. Inf. Commun. 6(10), 570–584 (2009)
Boṡnak, M., Matko, D., BlaŻiċ, S.: Quadrocopter hovering using position-estimation information from inertial sensors and a high-delay video system. J. Intell. Robot. Syst. 67(1), 43–60 (2012)
Frew, E.W., Langelaan, J., Stachura, M.: Adaptive planning horizon based on information velocity for vision-based navigation. In: AIAA Guidance, Navigation and Controls Conference (2007)
Bircher, A., Kamel, M., Alexis, K., Oleynikova, H., Siegwart, R.: Receding horizon next-best-view??? planner for 3d exploration. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 1462–1468. IEEE (2016)
Nuske, S., Choudhury, S., Jain, S., Chambers, A., Yoder, L., Scherer, S., Chamberlain, L., Cover, H., Singh, S.: Autonomous exploration and motion planning for an unmanned aerial vehicle navigating rivers. J. Field Robot. 32(8), 1141–1162 (2015)
Avellar, G.S., Pereira, G.A., Pimenta, L.C., Iscold, P.: Multi-uav routing for area coverage and remote sensing with minimum time. Sensors 15(11), 27,783–27,803 (2015)
Burri, M., Oleynikova, H., Achtelik, M.W., Siegwart, R.: Real-time visual-inertial mapping, re-localization and planning onboard mavs in unknown environments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015, pp. 1872–1878. IEEE (2015)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
About this article
Cite this article
Kanellakis, C., Nikolakopoulos, G. Survey on Computer Vision for UAVs: Current Developments and Trends. J Intell Robot Syst 87, 141–168 (2017). https://doi.org/10.1007/s10846-017-0483-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10846-017-0483-z