A novel region-based expansion rate obstacle detection method for MAVs using a fisheye camera
International Journal of Applied Earth Observation and Geoinformation - Tập 108 - Trang 102739 - 2022
Tài liệu tham khảo
Aguilar, 2017, Obstacle avoidance based-visual navigation for micro aerial vehicles, Electronics, 6, 10, 10.3390/electronics6010010
Al-Kaff, 2017, Obstacle detection and avoidance system based on monocular camera and size expansion algorithm for AVs, Sensors, 17, 1061, 10.3390/s17051061
Asmussen, 2015, Semi-automatic segmentation of petrographic thin section images using a “seeded-region growing algorithm” with an application to characterize wheatheredsubarkose sandstone, Comput. Geosci., 83, 89, 10.1016/j.cageo.2015.05.001
Badrloo, 2017, Monocular vision based obstacle detection. Earth Obs, Geomatics Eng., 1, 122
Barry, 2018, High-speed autonomous obstacle avoidance with pushbroom stereo, J. Field Rob., 35, 52, 10.1002/rob.21741
Bi, 2019, A lightweight autonomous MAV for indoor search and rescue, Asian J. Control, 21, 1732, 10.1002/asjc.2162
Chataigner, F., Cavestany, P., Soler, M., Rizzo, C., Gonzalez, J.-P., Bosch, C., Gibert, J., Torrente, A., Gomez, R., Serrano, D., 2020. ARSI: An aerial robot for sewer inspection. In: Grau, A., Morel, Y., Puig-Pey, A., Cecchi, F. (Eds.), Advances in robotics research: From lab to market. Springer Tracts in Advanced Robotics, vol. 132, pp. 249–274. https://doi.org/10.1007/978-3-030-22327-4_12.
Chen, 2020, Show, match and segment: Joint weakly supervised learning of semantic matching and object co-segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 43, 3632, 10.1109/TPAMI.2020.2985395
Cho, 2019, Vision-based obstacle avoidance strategies for MAVs using optical flows in 3-D textured environments, Sensor, 19, 2523, 10.3390/s19112523
Choi, 2019, Analysis of fish-eye lens camera self-calibration, Sensors (Basel), 19, 1218, 10.3390/s19051218
De Croon, 2018, Learning what is above and what is below: Horizon approach to monocular obstacle detection, Arxiv prepr., 08007
De Croon, 2021, Enhancing optical-flow-based control by learning visual appearance cues for flying robots, Nature Mach. Intell., 3, 33, 10.1038/s42256-020-00279-7
Díaz-vilariño, 2016, Indoor navigation from point clouds: 3D modelling and obstacle detection, Int. Arch. Photogramm. Remote Sens. Spatial Inform. Sci., 41, 275, 10.5194/isprs-archives-XLI-B4-275-2016
Escobar‐Alvarez, 2018, R-advance: Rapid adaptive prediction for vision-based autonomous navigation, control, and evasion, J. Field Rob., 35, 91, 10.1002/rob.21744
Figorito, 2014, Semi-automatic detection of linear archaeological traces from orthorectified aerial images, Int. J. Appl. Earth Obs. Geoinf., 26, 458
Gao, 2020, Autonomous aerial robot using dual-fisheye cameras, J. Field Rob., 37, 497, 10.1002/rob.21946
Gharani, 2017, Context-aware obstacle detection for navigation by visually impaired, Image Vis. Comput., 64, 103, 10.1016/j.imavis.2017.06.002
Giannì, 2017, Obstacle detection system involving fusion of multiple sensor technologies, Int. Arch. Photogram. Remote Sens. Spat. Inform. Sci., 42, 127, 10.5194/isprs-archives-XLII-2-W6-127-2017
Häne, 2017, 3D visual perception for self-driving cars using a multi-camera system: Dalibration, mapping, localization, and obstacle detection, Image Vis. Comput., 68, 14, 10.1016/j.imavis.2017.07.003
Ho, 2018, Optical-flow based self-supervised learning of obstacle appearance applied to MAV landing, Rob. Auton. Syst., 100, 78, 10.1016/j.robot.2017.10.004
Hong, 2021, TPR-TNR plot for confusion matrix, Commun. Stat. Appl. Meth., 28, 161
Huang, 2015, An indoor obstacle detection system using depth information and region growth, Sensors, 15, 27116, 10.3390/s151027116
Huh, 2008, A stereo vision-based obstacle detection system in vehicles, Opt. Lasers Eng., 46, 168, 10.1016/j.optlaseng.2007.08.002
Huh, 2015, Vision-based sense-and-avoid framework for unmanned aerial vehicles, IEEE Trans. Aerosp. Electron. Syst., 51, 3427, 10.1109/TAES.2015.140252
Jarron, 2019, Modelling wide-angle lens cameras for metrology and mapping applications, ISPRS Ann. Photogram. Remote Sens. Spat. Inform. Sci., 4, 79, 10.5194/isprs-annals-IV-2-W7-79-2019
Ji, 2020, Panoramic SLAM from a multiple fisheye camera rig, ISPRS J. Photogramm. Remote Sens., 159, 169, 10.1016/j.isprsjprs.2019.11.014
Jung, 2007, Stereo vision-based forward obstacle detection, Int. J. Automot. Technol., 8, 493
Kim, 2015, Rear obstacle detection system with fisheye stereo camera using HCT, Expert Syst. Appl., 42, 6295, 10.1016/j.eswa.2015.04.035
Kucukyildiz, 2017, Design and implementation of a multi sensor based brain computer interface for a robotic wheelchair, J. Intell. Rob. Syst., 87, 247, 10.1007/s10846-017-0477-x
Kumar, 2018, Monocular fisheye camera depth estimation using sparse lidar supervision, 2853
Lee, 2021, Deep learning-based monocular obstacle avoidance for unmanned aerial vehicle navigation in tree plantations, J. Intell. Rob. Syst., 101, 1, 10.1007/s10846-020-01284-z
Lee, 2016, A monocular vision sensor-based obstacle detection algorithm for autonomous robots, Sensors, 16, 311, 10.3390/s16030311
Li, 2019, Construction of obstacle element map based on indoor scene recognition, Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci., XLII-2/W13, 819, 10.5194/isprs-archives-XLII-2-W13-819-2019
Liang, 2021, Spherically optimized RANSAC aided by an IMU for fisheye image matching, Remote Sens., 13, 2017, 10.3390/rs13102017
Lin, 2018, Autonomous aerial navigation using monocular visual-inertial fusion, J. Field Rob., 35, 23, 10.1002/rob.21732
Lowe, 2004, Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vision, 60, 91, 10.1023/B:VISI.0000029664.99615.94
Mancini, 2018, J-MOD 2: joint monocular obstacle detection and depth estimation, IEEE Rob. Autom. Lett., 3, 1490, 10.1109/LRA.2018.2800083
Mashaly, 2016, Efficient sky segmentation approach for small UAV autonomous obstacles avoidance in cluttered environment, 6710
McGuire, 2017, Efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone, IEEE Rob. Autom. Lett., 2, 1070, 10.1109/LRA.2017.2658940
Mori, 2013, First results in detecting and avoiding frontal obstacles from a monocular camera for micro unmanned aerial vehicles, 1750
Mu, 2018, A novel Shi-Tomasi corner detection algorithm based on progressive probabilistic hough transform, 2918
Padhy, 2019, Obstacle avoidance for unmanned aerial vehicles: Using visual features in unknown environments, IEEE Consum. Electron. Mag., 8, 74, 10.1109/MCE.2019.2892280
Pestana, 2019, Overview obstacle maps for obstacle-aware navigation of autonomous drones, J. Field Rob., 36, 734, 10.1002/rob.21863
Qin, 2021, Multiple receptive field network (MRF-Net) for autonomous underwater vehicle fishing net detection using forward-looking sonar images, Sensors, 21, 1933, 10.3390/s21061933
Ricolfe-Viala, 2010, Robust metric calibration of non-linear camera lens distortion, Pattern Recogn., 43, 1688, 10.1016/j.patcog.2009.10.003
Rusiecki, 2012, Robust learning algorithm based on iterative least median of squares, Neural Process. Lett., 36, 145, 10.1007/s11063-012-9227-z
Said, 2016, A study of image processing using morphological opening and closing processes, Int. J. Control Theor. Appl., 9, 15
Scaramuzza, D., Ikeuchi, K., 2014. Omnidirectional Camera. Springer US, New York. https://doi.org/10.1007/978-0-387-31439-6.
Silva, 2020, Monocular trail detection and tracking aided by visual SLAM for small unmanned aerial vehicles, J. Intell. Rob. Syst., 97, 531, 10.1007/s10846-019-01033-x
Singh, 2021, A framework for the generation of obstacle data for the study of obstacle detection by ultrasonic sensors, IEEE Sens. J., 21, 9475, 10.1109/JSEN.2021.3055515
Singh, 2017, Obstacle detection techniques in outdoor environment: Process, study and analysis, Int. J. Image Graph. Signal Process., 9, 35, 10.5815/ijigsp.2017.05.05
Simões, 2020, Audio guide for visually impaired people based on combination of stereo vision and musical tones, Sensors, 20, 151, 10.3390/s20010151
Tijmons, 2017, Obstacle avoidance strategy using onboard stereo vision on a flapping wing MAV, IEEE Trans. Rob., 33, 858, 10.1109/TRO.2017.2683530
Tsai, 2018, Vision-based obstacle detection for mobile robot in outdoor environment, J. Inform. Sci. Eng., 34, 21
Ulrich, I., Nourbakhsh, I., 2000. Appearance-based obstacle detection with monocular color vision. In: AAAI/IAAI, pp. 866–871.
Urban, 2021, Time- and resource-efficient time-to-collision forecasting for indoor pedestrian obstacles avoidance, J. Imag., 7, 61, 10.3390/jimaging7040061
Urban, 2015, Improved wide-angle, fisheye and omnidirectional camera calibration, ISPRS J. Photogramm. Remote Sens., 108, 72, 10.1016/j.isprsjprs.2015.06.005
Yin, 2021, Integrating remote sensing and geospatial big data for urban land use mapping: A review, Int. J. Appl. Earth Obs. Geoinf., 103, 102514
Yu, 2011, ASIFT: An algorithm for fully affine invariant comparison, Image Process. Line, 1, 11, 10.5201/ipol.2011.my-asift
Zahran, 2018, A new velocity meter based on Hall effect sensors for UAV indoor navigation, IEEE Sens. J., 19, 3067, 10.1109/JSEN.2018.2890094
Zeng, 2016, Brain-inspired obstacle detection based on the biological visual pathway, 355