Target Localization for Autonomous Landing Site Detection: A Review and Preliminary Result with Static Image Photogrammetry

被引:3
作者
Subramanian, Jayasurya Arasur [1 ]
Asirvadam, Vijanth Sagayan [1 ]
Zulkifli, Saiful Azrin B. M. [1 ]
Singh, Narinderjit Singh Sawaran [2 ]
Shanthi, N. [3 ]
Lagisetty, Ravi Kumar [4 ]
机构
[1] Univ Teknol PETRONAS, Elect & Elect Engn, Seri Iskandar 32610, Malaysia
[2] INTI Int Univ, Fac Data Sci & Informat Technol, Nilai 71800, Malaysia
[3] Kongu Engn Coll, Dept Comp Sci & Engn, Erode 638052, India
[4] Indian Space Res Org, Bangalore 560094, India
关键词
feature detection; autonomous landing; unmanned aerial vehicle; computer vision; source localization; photogrammetry; HAZARD DETECTION; VISION; UAV; AVOIDANCE; VEHICLE; CRATER;
D O I
10.3390/drones7080509
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
The advancement of autonomous technology in Unmanned Aerial Vehicles (UAVs) has piloted a new era in aviation. While UAVs were initially utilized only for the military, rescue, and disaster response, they are now being utilized for domestic and civilian purposes as well. In order to deal with its expanded applications and to increase autonomy, the ability for UAVs to perform autonomous landing will be a crucial component. Autonomous landing capability is greatly dependent on computer vision, which offers several advantages such as low cost, self-sufficiency, strong anti-interference capability, and accurate localization when combined with an Inertial Navigation System (INS). Another significant benefit of this technology is its compatibility with LiDAR technology, Digital Elevation Models (DEM), and the ability to seamlessly integrate these components. The landing area for UAVs can vary, ranging from static to dynamic or complex, depending on their environment. By comprehending these characteristics and the behavior of UAVs, this paper serves as a valuable reference for autonomous landing guided by computer vision and provides promising preliminary results with static image photogrammetry.
引用
收藏
页数:23
相关论文
共 94 条
[1]   OFF-NADIR PHOTOGRAMMETRY FOR AIRBORNE SAR MOTION COMPENSATION: A FIRST STEP [J].
Ahmed, Usman Iqbal ;
Rabus, Bernhard ;
Kubanski, Mike .
2021 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM IGARSS, 2021, :8519-8522
[2]   Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces [J].
Alcantarilla, Pablo F. ;
Nuevo, Jesus ;
Bartoli, Adrien .
PROCEEDINGS OF THE BRITISH MACHINE VISION CONFERENCE 2013, 2013,
[3]   Vision Based Autonomous Landing of Multirotor UAV on Moving Platform [J].
Araar, Oualid ;
Aouf, Nabil ;
Vitanov, Ivan .
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 2017, 85 (02) :369-384
[4]   A System for Autonomous Landing of a UAV on a Moving Vehicle [J].
Battiato, Sebastiano ;
Cantelli, Luciano ;
D'Urso, Fabio ;
Farinella, Giovanni Maria ;
Guarnera, Luca ;
Guastella, Dario ;
Melita, Carmelo Donato ;
Muscato, Giovanni ;
Ortis, Alessandro ;
Ragusa, Francesco ;
Santoro, Corrado .
IMAGE ANALYSIS AND PROCESSING,(ICIAP 2017), PT I, 2017, 10484 :129-139
[5]   SURF: Speeded up robust features [J].
Bay, Herbert ;
Tuytelaars, Tinne ;
Van Gool, Luc .
COMPUTER VISION - ECCV 2006 , PT 1, PROCEEDINGS, 2006, 3951 :404-417
[6]  
Benini A, 2016, IEEE INT CONF ROBOT, P3463, DOI 10.1109/ICRA.2016.7487525
[7]   Peering into lunar permanently shadowed regions with deep learning [J].
Bickel, V. T. ;
Moseley, B. ;
Lopez-Francos, I ;
Shirley, M. .
NATURE COMMUNICATIONS, 2021, 12 (01)
[8]  
Bitoun Jonas, 2020, 2020 IEEE Region 10 Conference (TENCON), P685, DOI 10.1109/TENCON50793.2020.9293911
[9]  
Brady T, 2007, AEROSP CONF PROC, P3073
[10]  
Brady T, 2009, AEROSP CONF PROC, P561