Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
tutorial

Computer Vision for Autonomous UAV Flight Safety: An Overview and a Vision-based Safe Landing Pipeline Example

Published: 08 October 2021 Publication History
  • Get Citation Alerts
  • Abstract

    Recent years have seen an unprecedented spread of Unmanned Aerial Vehicles (UAVs, or “drones”), which are highly useful for both civilian and military applications. Flight safety is a crucial issue in UAV navigation, having to ensure accurate compliance with recently legislated rules and regulations. The emerging use of autonomous drones and UAV swarms raises additional issues, making it necessary to transfuse safety- and regulations-awareness to relevant algorithms and architectures. Computer vision plays a pivotal role in such autonomous functionalities. Although the main aspects of autonomous UAV technologies (e.g., path planning, navigation control, landing control, mapping and localization, target detection/tracking) are already mature and well-covered, ensuring safe flying in the vicinity of crowds, avoidance of passing over persons, or guaranteed emergency landing capabilities in case of malfunctions, are generally treated as an afterthought when designing autonomous UAV platforms for unstructured environments. This fact is reflected in the fragmentary coverage of the above issues in current literature. This overview attempts to remedy this situation, from the point of view of computer vision. It examines the field from multiple aspects, including regulations across the world and relevant current technologies. Finally, since very few attempts have been made so far towards a complete UAV safety flight and landing pipeline, an example computer vision-based UAV flight safety pipeline is introduced, taking into account all issues present in current autonomous drones. The content is relevant to any kind of autonomous drone flight (e.g., for movie/TV production, news-gathering, search and rescue, surveillance, inspection, mapping, wildlife monitoring, crowd monitoring/management), making this a topic of broad interest.

    Supplementary Material

    kakaletsis (kakaletsis.zip)
    Supplemental movie, appendix, image and software files for, Computer Vision for Autonomous UAV Flight Safety: An Overview and a Vision-based Safe Landing Pipeline Example

    References

    [1]
    UK Goverment. 2016. The air navigation order. Retrieved from http://www.legislation.gov.uk/uksi/2016/765/made.
    [2]
    Portugal’s National Regulatory Authority: Communications Sector. 2016. ANACOM - Regulamento n. 1093/2016, de 14 de dezembro. Retrieved from https://www.anacom.pt/render.jsp?contentId=1401209.
    [3]
    ESRI. 2017. ArcMap: How Slope works. Retrieved from http://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/how-slope-works.htm.
    [4]
    Spain Goverment. 2020. Drones-Trabajos areos-Compaas o empresas-AESA-Agencia Estatal deSeguridad Area - Ministerio de Fomento. Retrieved from http://www.seguridadaerea.gob.es/langcastellano/cias empresas/trabajos/rpas/default.aspx.
    [5]
    Goverment of Federal Republic of Germany, 2017. Verordnung zur Regelung des Betriebs von unbemannten Fluggerten. 17 (Apr. 2017), 6831. Retrieved from http://www.bgbl.de/xaver/bgbl/start.xav?startbk=BundesanzeigerBGBl&jumpTo=bgbl117s0683.pdf.
    [6]
    Federation Francaise de Drone (FFD)Nouveau guide DGAC, Activits Particulires, v1.2 (10 Janvier 2017). (Mar. 2017). Retrieved from /guide-dgac-activites-particulieres-v1-2/.
    [7]
    R. Acuna, D. Zhang, and V. Willert. 2018. Vision-based UAV landing on a moving platform in GPS denied environments using motion prediction. In Proceedings of the Latin American Robotic Symposium, Brazilian Symposium on Robotics (SBR) and Workshop on Robotics in Education (WRE).
    [8]
    M. Agrawal, K. Konolige, and M. R. Blas. 2008. Censure: Center surround extremas for realtime feature detection and matching. In Proceedings of the European Conference on Computer Vision, (ECCV). Springer.
    [9]
    W. G. Aguilar, V. P. Casaliglla, and J. L. Pólit. 2017. Obstacle avoidance for low-cost UAVs. In Proceedings of the IEEE International Conference on Semantic Computing (ICSC).
    [10]
    T. Ahonen, A. Hadid, and M. Pietikainen. 2006. Face description with local binary patterns: Application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell.12 (2006), 2037–2041.
    [11]
    S. Ahrens, D. Levine, G. Andrews, and J. P. How. 2009. Vision-based guidance and control of a hovering vehicle in unknown, GPS-denied environments. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
    [12]
    Y. Amit and D. Geman. 1999. A computational model for visual selection. Neural Comput. 11, 7 (1999), 1691–1715.
    [13]
    A. Anand, H. S. Koppula, T. Joachims, and A. Saxena. 2013. Contextually guided semantic labeling and search for three-dimensional point clouds. Int. J. Robot. Res. 32, 1 (2013), 19–34.
    [14]
    M. Aydin and E. Kugu. 2016. Finding smoothness area on the topographic maps for the unmanned aerial vehicle’s landing site estimation. In Proceedings of the IEEE International Conference on Digital Information and Communication Technology and Its Applications (DICTAP).
    [15]
    M. Barekatain, M. Martí, H. F. Shih, S. Murray, K. Nakayama, Y. Matsuo, and H. Prendinger. 2017. Okutama-action: An aerial view video dataset for concurrent human action detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
    [16]
    F. Bernuy and J. Ruiz del Solar. 2015. Semantic mapping of large-scale outdoor scenes for autonomous off-road driving. In Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops. IEEE.
    [17]
    K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, and D. Krishnan. 2017. Unsupervised pixel-level domain adaptation with Generative Adversarial Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
    [18]
    J. Byrne, M. Cosgrove, and R. Mehra. 2006. Stereo based obstacle detection for an unmanned air vehicle. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
    [19]
    D. Cavaliere, V. Loia, A. Saggese, S. Senatore, and M. Vento. 2017. Semantically enhanced UAVs to increase the aerial scene understanding. IEEE Trans. Syst., Man, Cybern.: Syst. 49, 3 (2017), 555–567.
    [20]
    Y.-C. Chang, H.-T. Chen, J.-H. Chuang, and I.-C. Liao. 2018. Pedestrian detection in aerial images using vanishing point transformation and deep learning. In Proceedings of the IEEE International Conference on Image Processing (ICIP).
    [21]
    L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam. 2018. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European Conference on Computer Vision (ECCV).
    [22]
    S. Chen, L. Han, X. Liu, Z. He, and X. Yang. 2020. Subspace distribution adaptation frameworks for domain adaptation. IEEE Trans. Neural Netw. Learn. Syst. 31, 12 (2020), 5204–5218.
    [23]
    A. B. Curtis. 2008. Path Planning for Unmanned Air and Ground vehicles in urban environments. Theses and Dissertations. 1317, Brigham Young University.
    [24]
    N. Dalal and B. Triggs. 2005. Histograms of oriented gradients for human detection. In Proceedings of the IEEE International Conference on Computer Vision & Pattern Recognition (CVPR).
    [25]
    R. de Nijs, S. Ramos, G. Roig, X. Boix, L. Van Gool, and K. Kühnlenz. 2012. On-line semantic perception using uncertainty. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
    [26]
    X. Deng and Q. Zeng. 2013. Research on laser-assisted odometry of indoor UAV with monocular vision. In Proceedings of the IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems.
    [27]
    V. R. Desaraju, N. Michael, M. Humenberger, R. Brockers, S. Weiss, and L. H. Matthies. 2014. Vision-based landing site evaluation and trajectory generation toward rooftop landing. In Robotics: Science and Systems. Citeseer.
    [28]
    DJI. 2021. Protecting the skies in the drone era, elevating safety. (2021). https://terra-1-g.djicdn.com/851d20f7b9f64838a34cd02351370894/Flysafe/190521_US-Letter_Policy-White-Paper_web.pdf.
    [29]
    P. Dollár, R. Appel, S. Belongie, and P. Perona. 2014. Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36, 8 (2014), 1532–1545.
    [30]
    P. Dollár, R. Appel, and W. Kienzle. 2012. Crosstalk cascades for frame-rate pedestrian detection. In Proceedings of the European Conference on Computer Vision (ECCV). Springer.
    [31]
    P. Dollár, S. Belongie, and P. Perona. 2010. The fastest pedestrian detector in the west. In Proceedings of the British Machine Vision Conference. BMVA Press.
    [32]
    P. Dollár, Z. Tu, P. Perona, and S. Belongie. 2009. Integral channel features. (2009). http://dx.doi.org/10.5244/C.23.91
    [33]
    Y. Dong, C. Fu, and E. Kayacan. 2016. RRT-based 3D path planning for formation landing of quadrotor UAVs. In Proceedings of the IEEE International Conference on Control, Automation, Robotics and Vision (ICARCV).
    [34]
    S. Dotenco, F. Gallwitz, and E. Angelopoulou. 2014. Autonomous approach and landing for a low-cost quadrotor using monocular cameras. In Proceedings of the European Conference on Computer Vision (ECCV). Springer.
    [35]
    D. H. Douglas and T. K. Peucker. 1973. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartogr.: Int. J. Geog. Inf. Geovis. 10, 2 (1973), 112–122.
    [36]
    B. Douillard, D. Fox, F. Ramos, et al. 2008. Laser and vision based outdoor object mapping. In Robotics: Science and Systems, Vol. 8. MIT Press.
    [37]
    F. Duchoň, A. Babinec, M. Kajan, P. Beňo, M. Florek, T. Fico, and L. Jurišica. 2014. Path planning with modified a star algorithm for a mobile robot. Procedia Eng. 96 (2014), 59–69.
    [38]
    R. O. Duda and P. E. Hart. 1973. Pattern Classification and Scene Analysis. Wiley. 72007008
    [39]
    P. F. Felzenszwalb, D. A. McAllester, D. Ramanan, et al. 2008. A discriminatively trained, multiscale, deformable part model. In Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
    [40]
    S. Friedman, H. Pasula, and D. Fox. 2007. Voronoi random fields: Extracting topological structure of indoor environments via place labeling. In Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI).
    [41]
    A.-J. Gallego, J. Calvo-Zaragoza, and R. B. Fisher. 2020. Incremental unsupervised domain-adversarial training of neural networks. IEEE Trans. Neural Netw. Learn. Syst. (2020). arXiv 2001.04129
    [42]
    M. Garg, A. Kumar, and P. B. Sujit. 2015. Terrain-based landing site selection and path planning for fixed-wing UAVs. In Proceedings of the IEEE International Conference on Unmanned Aircraft Systems (ICUAS).
    [43]
    R. Girshick. 2015. Fast R-CNN. In Proceedings of the IEEE International Conference on Computer Vision (ICCV).
    [44]
    R. Girshick, J. Donahue, T. Darrell, and J. Malik. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
    [45]
    A. S. Glassner. 1989. An Introduction to Ray Tracing. Elsevier.
    [46]
    Z. Gosiewski, J. Ciesluk, and L. Ambroziak. 2011. Vision-based obstacle avoidance for unmanned aerial vehicles. In Proceedings of the IEEE International Congress on Image and Signal Processing, (ICISP).
    [47]
    D. Guan, X. Luo, Y. Cao, J. Yang, Y. Cao, G. Vosselman, and M. Ying Yang. 2019. Unsupervised domain adaptation for multispectral pedestrian detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
    [48]
    X. Guo, D. Dean, S. Denman, C. Fookes, and S. Sridharan. 2011. Evaluating automatic road detection across a large aerial imagery collection. In Proceedings of the IEEE International Conference on Digital Image Computing: Techniques and Applications, (DICTA).
    [49]
    X. Guo, S. Denman, C. Fookes, L. Mejias, and S. Sridharan. 2014. Automatic UAV forced landing site detection using machine learning. In Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA).
    [50]
    X. Guo, S. Denman, C. Fookes, and S. Sridharan. 2016. A robust UAV landing site detection system using mid-level discriminative patches. In Proceedings of the IEEE International Conference on Pattern Recognition (ICPR).
    [51]
    H. He and B. Upcroft. 2013. Nonparametric semantic segmentation for 3D street scenes. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
    [52]
    K. He, X. Zhang, S. Ren, and J. Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (CVPR).
    [53]
    L. Heng, L. Meier, P. Tanskanen, F. Fraundorfer, and M. Pollefeys. 2011. Autonomous obstacle avoidance and maneuvering on a vision-guided MAV using on-board processing. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
    [54]
    T. Hinzmann, T. Stastny, C. Cadena, R. Siegwart, and I. Gilitschenski. 2018. Free LSD: Prior-free visual landing site detection for autonomous planes. IEEE Robot. Autom. Lett. 3, 3 (2018), 2545–2552.
    [55]
    H. Hirschmüller, P. R. Innocent, and J. Garibaldi. 2002. Real-time correlation-based stereo vision with reduced border errors. Int. J. Comput. Vis. 47, 1–3 (2002), 229–246.
    [56]
    A. Hornung, K. M. Wurm, M. Bennewitz, C. Stachniss, and W. Burgard. 2013. OctoMap: An efficient probabilistic 3D mapping framework based on octrees. Auton. Robots 34, 3 (2013), 189–206.
    [57]
    J. Hosang, M. Omran, R. Benenson, and B. Schiele. 2015. Taking a deeper look at pedestrians. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
    [58]
    S. Hrabar, G. S. Sukhatme, P. Corke, K. Usher, and J. Roberts. 2005. Combined optic-flow and stereo-based navigation of urban canyons for a UAV. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems.
    [59]
    J. Hu, A. Razdan, J. C. Femiani, M. Cui, and P. Wonka. 2007. Road network extraction and intersection detection from aerial images by tracking road footprints. IEEE Trans. Geosci. Rem. Sens. 45, 12 (2007), 4144–4157.
    [60]
    A. E. Johnson, A. R. Klumpp, J. B. Collier, and A. A. Wolf. 2002. LiDAR-based hazard avoidance for safe landing on Mars. J. Guid., Contr., Dynam. 25, 6 (2002), 1091–1099.
    [61]
    E. Kakaletsis and N. Nikolaidis. 2019. Potential UAV landing sites detection through Digital Elevation Models analysis. In Proceedings of the European Signal Processing Conference (EUSIPCO), Satellite Workshop: Signal Processing, Computer Vision and Deep Learning for Autonomous Systems. IEEE.
    [62]
    E. Kakaletsis, M. Tzelepi, P. I. Kaplanoglou, C. Symeonidis, N. Nikolaidis, A. Tefas, and I. Pitas. 2019. Semantic map annotation through UAV video analysis using deep learning models in ROS. In Proceedings of the International Conference on Multimedia Modeling (MMM). Springer.
    [63]
    R. Kala. 2013. Rapidly exploring random graphs: Motion planning of multiple mobile robots. Adv. Robot. 27, 14 (2013), 1113–1122.
    [64]
    Q. Kang, S. Yao, M. C. Zhou, K. Zhang, and A. Abusorrah. 2020. Effective visual domain adaptation via generative adversarial distribution matching. IEEE Trans. Neural Netw. Learn. Syst. (2020).
    [65]
    I. Karakostas, I. Mademlis, N. Nikolaidis, and I. Pitas. 2020. Shot type constraints in UAV cinematography for autonomous target tracking. Inf. Sci. 506 (2020), 273–294.
    [66]
    J. Kennedy and R. Eberhart. 1995. Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks (ICNN).
    [67]
    A. Krizhevsky, I. Sutskever, and G. E. Hinton. 2012. Imagenet classification with deep Convolutional Neural Networks. In Proceedings of the International Conference on Advances in Neural Information Processing Systems (NIPS).
    [68]
    W. Lan, J. Dang, Y. Wang, and S. Wang. 2018. Pedestrian detection based on YOLO network model. In Proceedings of the IEEE International Conference on Mechatronics and Automation (ICMA).
    [69]
    S. Lazebnik, C. Schmid, and J. Ponce. 2006. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
    [70]
    B. Le Saux and M. Sanfourche. 2013. Rapid semantic mapping: Learn environment classifiers on the fly. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems.
    [71]
    J.-O. Lee, K.-H. Lee, S.-H. Park, S.-G. Im, and J. Park. 2011. Obstacle avoidance for small UAVs using monocular vision. Aircr. Eng. Aerosp. Technol. 83, 6 (2011), 397–406.
    [72]
    T. Liu and T. Stathaki. 2017. Enhanced pedestrian detection using deep learning based semantic image segmentation. In Proceedings of the IEEE International Conference on Digital Signal Processing (DSP).
    [73]
    W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg. 2016. SSD: Single shot multibox detector. In Proceedings of the European Conference on Computer Vision (ECCV). Springer.
    [74]
    J. Long, E. Shelhamer, and T. Darrell. 2015. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
    [75]
    David G. Lowe. 2004. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 2 (2004), 91–110.
    [76]
    B. D. Lucas and T. Kanade. 1981. An iterative image registration technique with an application to stereo vision. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI).
    [77]
    J.-Q. Ma. 2009. Content-based image retrieval with HSV color space and texture features. In Proceedings of the IEEE International Conference on Web Information Systems and Mining.
    [78]
    I. Mademlis, V. Mygdalis, N. Nikolaidis, M. Montagnuolo, F. Negro, A. Messina, and I. Pitas. 2019. High-level multiple-UAV cinematography tools for covering outdoor events. IEEE Trans. Broadcast. 65, 3 (2019), 627–635.
    [79]
    I. Mademlis, V. Mygdalis, N. Nikolaidis, and I. Pitas. 2018. Challenges in autonomous UAV cinematography: An overview. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME).
    [80]
    I. Mademlis, N. Nikolaidis, A. Tefas, I. Pitas, T. Wagner, and A. Messina. 2018. Autonomous unmanned aerial vehicles filming in dynamic unstructured outdoor environments. IEEE Sig. Process. Mag. 36, 1 (2018), 147–153.
    [81]
    I. Mademlis, N. Nikolaidis, A. Tefas, I. Pitas, T. Wagner, and A. Messina. 2019. Autonomous UAV cinematography: a tutorial and a formalized shot type taxonomy. Comput. Surv. 52, 5 (2019), 105.
    [82]
    I. Mademlis, P. Nousi, C. Le Barz, T. Gonçalves, and I. Pitas. 2019. Communications for autonomous unmanned aerial vehicle fleets in outdoor cinematography applications. In Proceedings of the EURASIP European Signal Processing Conference (EUSIPCO) Satellite Workshop: Signal Processing, Computer Vision and Deep Learning for Autonomous Systems.
    [83]
    I. Mademlis, A. Torres-González, J. Capitán, R. Cunha, B. Guerreiro, A. Messina, F. Negro, C. Le Barz, T. Gonçalves, A. Tefas, and I. Pitas. 2019. A multiple-UAV software architecture for autonomous media production. In Proceedings of the EURASIP European Signal Processing Conference (EUSIPCO) Satellite Workshop: Signal Processing, Computer Vision and Deep Learning for Autonomous Systems.
    [84]
    D. Magree, J. G. Mooney, and E. N. Johnson. 2013. Monocular visual mapping for obstacle avoidance on UAVs. In Proceedings of the IEEE International Conference on Unmanned Aircraft Systems (ICUAS).
    [85]
    A. Marcu, D. Costea, V. Licaret, M. Pîrvu, E. Slusanschi, and M. Leordeanu. 2018. SafeUAV: Learning to estimate depth and safe landing areas for UAVs from synthetic data. In Proceedings of the European Conference on Computer Vision (ECCV).
    [86]
    D. Maturana, S. Arora, and S. Scherer. 2017. Looking forward: A semantic mapping system for scouting with micro-aerial vehicles. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
    [87]
    D. Maturana and S. Scherer. 2015. 3D convolutional neural networks for landing zone detection from LiDAR. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
    [88]
    D. F. Maune. 2007. Digital Elevation Model Technologies and Applications: The DEM Users Manual. ASPRS Publications.
    [89]
    A. Mcfadyen, L. Mejias, P. Corke, and C. Pradalier. 2013. Aircraft collision avoidance using spherical visual predictive control and single point features. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems.
    [90]
    L. Mejias and D. Fitzgerald. 2013. A multi-layered approach for site detection in UAS emergency landing scenarios using geometry-based image segmentation. In Proceedings of the IEEE International Conference on Unmanned Aircraft Systems (ICUAS).
    [91]
    C. L. Miller and R. A. Laflamme. 1958. The Digital Terrain Model: Theory & Application. MIT Photogrammetry Laboratory.
    [92]
    N. Mitsou, R. de Nijs, D. Lenz, J. Frimberger, D. Wollherr, K. Kühnlenz, and C. Tzafestas. 2012. Online semantic mapping of urban environments. In Proceedings of the International Conference on Spatial Cognition (ICSC). Springer.
    [93]
    M. Mittal, R. Mohan, W. Burgard, and A. Valada. 2019. Vision-based autonomous UAV navigation and landing for urban search and rescue. arXiv preprint arXiv:1906.01304 (2019).
    [94]
    R. Mur-Artal, J. M. M. Montiel, and J. D. Tardos. 2015. ORB-SLAM: A versatile and accurate monocular SLAM system. IEEE Trans. Robot. 31, 5 (2015), 1147–1163.
    [95]
    F. Nex and F. Remondino. 2014. UAV for 3D mapping applications: A review. Appl. Geomat. 6, 1 (2014), 1–15.
    [96]
    P. Nousi, I. Mademlis, I. Karakostas, A. Tefas, and I. Pitas. 2019. Embedded UAV real-time visual object detection and tracking. In Proceedings of the IEEE International Conference on Real-time Computing and Robotics (RCAR).
    [97]
    A. Nurhadiyatna and S. Lončarić. 2017. Semantic image segmentation for pedestrian detection. In Proceedings of the IEEE International Symposium on Image and Signal Processing and Analysis.
    [98]
    H. Oleynikova, Z. Taylor, M. Fehr, R. Siegwart, and J. Nieto. 2017. Voxblox: Incremental 3D Euclidean signed distance fields for on-board MAV planning. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
    [99]
    M. A. Olivares-Mendez, L. Mejias, P. Campoy, and I. Mellado-Bataller. 2012. Quadcopter see and avoid using a fuzzy controller. In Uncertainty Modeling in Knowledge Engineering and Decision Making. World Scientific, 1239–1244.
    [100]
    F. Pan, I. Shin, F. Rameau, S. Lee, and I. S. Kweon. 2020. Unsupervised intra-domain adaptation for semantic segmentation through self-supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
    [101]
    D. Pangercic, B. Pitzer, M. Tenorth, and M. Beetz. 2012. Semantic object maps for robotic housework-representation, acquisition and use. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
    [102]
    C. Papageorgiou and T. Poggio. 2000. A trainable system for object detection. Int. J. Comput. Vis. 38, 1 (2000), 15–33.
    [103]
    C. Papaioannidis, I. Mademlis, and I. Pitas. 2021. Autonomous UAV safety by visual human crowd detection using multi-task deep neural networks. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
    [104]
    G. Papandreou, T. Zhu, L.-C. Chen, S. Gidaris, J. Tompson, and K. Murphy. 2018. PersonLab: Person pose estimation and instance segmentation with a bottom-up, part-based, geometric embedding model. In Proceedings of the European Conference on Computer Vision (ECCV).
    [105]
    F. Patrona, I. Mademlis, A. Tefas, and I. Pitas. 2019. Computational UAV cinematography for intelligent shooting based on semantic visual analysis. In Proceedings of the IEEE International Conference on Image Processing (ICIP).
    [106]
    R. Paul, R. Triebel, D. Rus, and P. Newman. 2012. Semantic categorization of outdoor scenes with uncertainty estimates using multi-class Gaussian process classification. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems.
    [107]
    R. Polastro, F. Corrêa, F. Cozman, and J. Okamoto. 2010. Semantic mapping with a probabilistic description logic. In Proceedings of the Brazilian Symposium on Artificial Intelligence. Springer.
    [108]
    G. Priestnall, J. Jaafar, and A. Duncan. 2000. Extracting urban features from LiDAR digital surface models. Comput., Environ. Urban Syst. 24, 2 (2000), 65–78.
    [109]
    A. Pronobis and P. Jensfelt. 2012. Large-scale semantic mapping and reasoning with heterogeneous modalities. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
    [110]
    M. Quigley, B. Gerkey, K. Conley, J. Faust, T. Foote, J. Leibs, E. Berger, R. Wheeler, and A. Ng. 2009. ROS: an open-source robot operating system. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) Workshop on Open Source Robotics.
    [111]
    R. Joseph and F. Ali2018. YOLOv3: an incremental improvement. ArXiv abs/1804.02767 (2018).
    [112]
    J. Redmon, S. Divvala, R. Girshick, and A. Farhadi. 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
    [113]
    E. Remolina and B. Kuipers. 2004. Towards a general theory of topological maps. Artif. Intell. 152, 1 (2004), 47–104.
    [114]
    S. Ren, K. He, R. Girshick, and J. Sun. 2015. Faster R-CNN: Towards real-time object detection with region proposal networks. In Proceedings of the International Conference on Advances in Neural Information Processing Systems (NIPS).
    [115]
    V. Roberge, M. Tarbouchi, and G. Labonté. 2012. Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Trans. Industr. Inform. 9, 1 (2012), 132–141.
    [116]
    Scott D. Roth. 1982. Ray casting for modeling solids. Comput. Graph. Image Process. 18, 2 (1982), 109–144.
    [117]
    Z. Saishang, C. Yifu, Z. Min, X. Zhong, and L. Can. 2015. MSURF: A new image matching algorithm which combines homography and SURF algorithm. In Proceedings of the IEEE International Conference on Geoinformatics.
    [118]
    M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen. 2018. MobileNetV2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
    [119]
    A. Satpathy, X. Jiang, and H.-L. Eng. 2014. LBP-based edge-texture features for object recognition. IEEE Trans. Image Process. 23, 5 (2014), 1953–1964.
    [120]
    S. Scherer, L. Chamberlain, and S. Singh. 2012. Autonomous landing at unprepared sites by a full-scale helicopter. Robot. Auton. Syst.ems 60, 12 (2012), 1545–1562.
    [121]
    S. Sengupta, E. Greveson, A. Shahrokni, and P. H. S. Torr. 2013. Urban 3D semantic modelling using stereo vision. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
    [122]
    S. Sengupta, P. Sturgess, L. Ladickỳ, and P. H. S. Torr. 2012. Automatic dense visual semantic mapping from street-level imagery. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems.
    [123]
    P. Serra, R. Cunha, T. Hamel, D. Cabecinhas, and C. Silvestre. 2016. Landing of a quadrotor on a moving target using dynamic image-based visual servo control. IEEE Trans. Robot. 32, 6 (2016), 1524–1535.
    [124]
    S. Shah, D. Dey, C. Lovett, A. Kapoor, and W. Burgard. 2017. AirSim: High-fidelity visual and physical simulation for autonomous vehicles. In Proceedings of the Field and Service Robotics Conference.
    [125]
    H. Shakhatreh, A. H. Sawalmeh, A. Al-Fuqaha, Z. Dou, E. Almaita, I. Khalil, N. S. Othman, A. Khreishah, and M. Guizani. 2019. Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges. IEEE Access 7 (2019), 48572–48634.
    [126]
    L. Shams and J. Spoelstra. 1996. Learning Gabor-based features for face detection. In Proceedings of the World Congress in Neural Networks. International Neural Network Society.
    [127]
    J. Shotton, J. Winn, C. Rother, and A. Criminisi. 2006. TextonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In Proceedings of the European Conference on Computer Vision (ECCV). Springer.
    [128]
    A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang, and R. Webb. 2017. Learning from simulated and unsupervised images through adversarial training. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
    [129]
    K. Simonyan and A. Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).
    [130]
    G. Singh and J. Košecká. 2012. Acquiring semantics induced topology in urban environments. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
    [131]
    Skydio. 2018. Skydio R1. Retrieved from https://robots.ieee.org/robots/skydior1/.
    [132]
    M. V. Srinivasan, S. Thurrowgood, and D. Soccol. 2006. An optical system for guidance of terrain following in UAVs. In Proceedings of the IEEE International Conference on Video and Signal=based Surveillance.
    [133]
    C. Stöcker, R. Bennett, F. Nex, M. Gerke, and J. Zevenbergen. 2017. Review of the current state of UAV regulations. Rem. Sens. 9 (2017), 459.
    [134]
    C. Symeonidis, I. Mademlis, N. Nikolaidis, and I. Pitas. 2019. Improving neural non-maximum suppression for object detection by exploiting interest-point detectors. In Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP).
    [135]
    X. Tan and W. Triggs. 2010. Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19, 6 (2010), 1635–1650.
    [136]
    Y.-J. Tsai, C.-S. Lee, C.-L. Lin, and C.-H. Huang. 2015. Development of flight path planning for multirotor aerial vehicles. Aerospace 2, 2 (2015), 171–188.
    [137]
    M. Tzelepi and A. Tefas. 2017. Human crowd detection for drone flight safety using Convolutional Neural Networks. In Proceedings of the EURASIP European Signal Processing Conference (EUSIPCO). IEEE.
    [138]
    M. Tzelepi and A. Tefas. 2019. Graph-embedded convolutional neural networks in human crowd detection for drone flight safety. IEEE Trans. Emerg. Topics Comput. Intell. 5, 2 (2019).
    [139]
    J. R. R. Uijlings, K. E. A. Van De Sande, T. Gevers, and A. W. M. Smeulders. 2013. Selective search for object recognition. Int. J. Comput. Vis. 104, 2 (2013), 154–171.
    [140]
    R. Vogel, M. Achab, S. Clémençon, and C. Tillier. 2020. Weighted empirical risk minimization: Sample selection bias correction based on importance sampling. arXiv preprint arXiv:2002.05145 (2020).
    [141]
    X. Wang, T. X. Han, and S. Yan. 2009. A HOG-LBP human detector with partial occlusion handling. In Proceedings of the IEEE International Conference on Computer Vision (ICCV).
    [142]
    Z. Wang, M. Yu, Y. Wei, R. Feris, J. Xiong, W.-M. Hwu, T. S. Huang, and H. Shi. 2020. Differential treatment for stuff and things: A simple unsupervised domain adaptation method for semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
    [143]
    Y. Watanabe, P. Fabiani, and G. L. Besnerais. 2010. Towards a UAV visual air-to-ground target tracking in an urban environment. In Proceedings of the International Congress of the Aeronautical Sciences (ICAS).
    [144]
    Y. Watanabe, C. Lesire, A. Piquereau, P. Fabiani, M. Sanfourche, and G. Le Besnerais. 2010. The ONERA ReSSAC unmanned autonomous helicopter: Visual air-to-ground target tracking in an urban environment. In Proceedings of the American Helicopter Society 66th Annual Forum (AHS’10).
    [145]
    R. Weibel and M. Heller. 1993. Digital Terrain Modelling. Oxford University Press.
    [146]
    D. F. Wolf and G. S. Sukhatme. 2008. Semantic mapping using mobile robots. IEEE Trans. Robot. 24, 2 (2008), 245–258.
    [147]
    W. Xian, M. Qinwei, M. Shaopeng, and W. Hongtao. 2011. A marker locating method based on gray centroid algorithm and its application to displacement and strain measurement. In Proceedings of the IEEE International Conference on Intelligent Computation Technology and Automation, (ICICTA).
    [148]
    P. Xu. 2015. Information Fusion for Scene Understanding. Ph.D. Dissertation. Universite Technologie de Compie.
    [149]
    S. Yan, D. Xu, B. Zhang, H.-J. Zhang, Q. Yang, and S. Lin. 2007. Graph embedding and extensions: A general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29, 1 (2007), 40–51.
    [150]
    J. Yang, W. An, S. Wang, X. Zhu, C. Yan, and J. Huang. 2020. Label-driven reconstruction for domain adaptation in semantic segmentation. In Proceedings of the European Conference on Computer Vision (ECCV). Springer.
    [151]
    J. Yang, H. Zou, Y. Zhou, and L. Xie. 2021. Robust adversarial discriminative domain adaptation for real-world cross-domain visual recognition. Neurocomputing 433 (2021), 28–36.
    [152]
    L. Yang, J. Qi, J. Xiao, and X. Yong. 2014. A literature review of UAV 3D path planning. In Proceedings of the World Congress on Intelligent Control and Automation (WCICA).
    [153]
    C. Yu, J. Wang, C. Peng, C. Gao, G. Yu, and N. Sang. 2018. BiSeNet: Bilateral segmentation network for real-time semantic segmentation. In Proceedings of the European Conference on Computer Vision (ECCV).
    [154]
    F. Yu and V. Koltun. 2015. Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015).
    [155]
    C. Yuan, F. Recktenwald, and H. A. Mallot. 2009. Visual steering of UAV in unknown environments. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
    [156]
    Y. Yuan, X. Chen, and J. Wang. 2019. Object-contextual representations for semantic segmentation. arXiv preprint arXiv:1909.11065 (2019).
    [157]
    H. Zender, O. M. Mozos, P. Jensfelt, G.-J. M. Kruijff, and W. Burgard. 2008. Conceptual spatial representations for indoor mobile robots. Robot. Auton. Syst. 56, 6 (2008), 493–502.
    [158]
    L. Zhang. 2005. Automatic Digital Surface Model (DSM) Generation from Linear Array Images. Institute of Geodesy and Photogrammetry.
    [159]
    L. Zhang, L. Lin, X. Liang, and K. He. 2016. Is faster R-CNN doing well for pedestrian detection? In Proceedings of the European Conference on Computer Vision (ECCV). Springer.
    [160]
    S. Zhang, R. Benenson, M. Omran, J. Hosang, and B. Schiele. 2016. How far are we from solving pedestrian detection? In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
    [161]
    Y. Zhang and B. D. Davison. 2021. Domain adaptation for object recognition using subspace sampling demons. Multimedia Tools Applic. 80 (2021), 23255–23274. https://doi.org/10.1007/s11042-020-09336-0
    [162]
    H. Zhao, X. Qi, X. Shen, J. Shi, and J. Jia. 2018. ICNet for real-time semantic segmentation on high-resolution images. In Proceedings of the European Conference on Computer Vision (ECCV).
    [163]
    H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia. 2017. Pyramid scene parsing network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
    [164]
    S. Zhao, X. Yue, S. Zhang, B. Li, H. Zhao, B. Wu, R. Krishna, J. E. Gonzalez, A. L. Sangiovanni-Vincentelli, S. A. Seshia et al. 2020. A review of single-source deep unsupervised visual domain adaptation. IEEE Trans. Neural Netw. Learn. Syst. (2020), 1–21.
    [165]
    B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba. 2017. Scene parsing through ADE20K dataset. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 1. 5122–5130.

    Cited By

    View all
    • (2024)Deploying a Computer Vision Model Based on YOLOv8 Suitable for Drones in the Tuna Fishing and Aquaculture IndustryJournal of Marine Science and Engineering10.3390/jmse1205082812:5(828)Online publication date: 16-May-2024
    • (2024)Design of a UAV Trajectory Prediction System Based on Multi-Flight ModesDrones10.3390/drones80602558:6(255)Online publication date: 10-Jun-2024
    • (2024)UAV-Assisted IoT Applications, QoS Requirements and Challenges with Future Research DirectionsACM Computing Surveys10.1145/365728756:10(1-35)Online publication date: 10-Apr-2024
    • Show More Cited By

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Computing Surveys
    ACM Computing Surveys  Volume 54, Issue 9
    December 2022
    800 pages
    ISSN:0360-0300
    EISSN:1557-7341
    DOI:10.1145/3485140
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 08 October 2021
    Accepted: 01 June 2021
    Revised: 01 May 2021
    Received: 01 June 2020
    Published in CSUR Volume 54, Issue 9

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. UAV flight Safety
    2. crowd/person detection
    3. landing site detection
    4. path planning
    5. obstacle avoidance
    6. semantic mapping

    Qualifiers

    • Tutorial
    • Refereed

    Funding Sources

    • European Union’s Horizon 2020 research and innovation programme

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)371
    • Downloads (Last 6 weeks)28

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Deploying a Computer Vision Model Based on YOLOv8 Suitable for Drones in the Tuna Fishing and Aquaculture IndustryJournal of Marine Science and Engineering10.3390/jmse1205082812:5(828)Online publication date: 16-May-2024
    • (2024)Design of a UAV Trajectory Prediction System Based on Multi-Flight ModesDrones10.3390/drones80602558:6(255)Online publication date: 10-Jun-2024
    • (2024)UAV-Assisted IoT Applications, QoS Requirements and Challenges with Future Research DirectionsACM Computing Surveys10.1145/365728756:10(1-35)Online publication date: 10-Apr-2024
    • (2024)UAV Trajectory Prediction Based on Flight State RecognitionIEEE Transactions on Aerospace and Electronic Systems10.1109/TAES.2023.330385460:3(2629-2641)Online publication date: Jun-2024
    • (2024)Design of UAV Flight State Recognition System for Multisensor Data FusionIEEE Sensors Journal10.1109/JSEN.2024.339488324:13(21386-21394)Online publication date: 1-Jul-2024
    • (2024)A Gyro-Based Tracking Assistant for Drones With Uncooled Infrared CameraIEEE Sensors Journal10.1109/JSEN.2023.333562124:1(645-659)Online publication date: 1-Jan-2024
    • (2024)On Automatic Person-in-Water Detection for Marine Search and Rescue OperationsIEEE Access10.1109/ACCESS.2024.338664012(52428-52438)Online publication date: 2024
    • (2024)A Survey of Deep Learning Techniques and Computer Vision in Robotic and Drone with ApplicationsBIO Web of Conferences10.1051/bioconf/2024970000897(00008)Online publication date: 5-Apr-2024
    • (2024)Progress in artificial intelligence-based visual servoing of autonomous unmanned aerial vehicles (UAVs)International Journal of Thermofluids10.1016/j.ijft.2024.10059021(100590)Online publication date: Feb-2024
    • (2024)A fast, lightweight deep learning vision pipeline for autonomous UAV landing support with added robustnessEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.107864131:COnline publication date: 1-May-2024
    • Show More Cited By

    View Options

    Get Access

    Login options

    Full Access

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media