A novel region-based expansion rate obstacle detection method for MAVs using a fisheye camera

Samira Badrloo1,2, Masood Varshosaz2, Saied Pirasteh1,3, Jonathan Li3
1Department of Surveying and Geoinformatics, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
2Department of Photogrammetry and Remote Sensing, K.N. Toosi University of Technology, Tehran 19697, Iran
3Geospatial Sensing and Data Intelligence Lab, Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada

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