TY  - JOUR
EP  - 4543
KW  - Antennas; Computer vision; Extraction; Landing; Navigation; Photogrammetry; Trajectories
KW  -  A* algorithm; Autonomous landing; Classification algorithm; Features detections; Features extraction; Landing trajectory; Localisation; Location awareness; Source localization; Static environment
KW  -  Feature extraction
VL  - 12
JF  - IEEE Access
N2  - In recent years, the research has focused firmly on Autonomous Aerial Vehicles (AAVs) owing to their vast array of potential applications, to aid those applications this study presents a technical approach for source localization and landing trajectory identification for Autonomous Aerial Vehicle (AAV) landing, leveraging computer vision and photogrammetry techniques. The proposed method aims to achieve accurate and robust localization of the landing target area and precise determination of the AAV's landing trajectory. The source localization module utilizes a computer vision system equipped with onboard cameras and advanced image processing algorithms. The system captures images of the target area and performs feature extraction and matching to estimate the position of the landing target. Additionally, the Aâ?? algorithm serves as a pivotal tool in deriving an optimized trajectory by harnessing the relative positions of the Autonomous Aerial Vehicle (AAV) and the designated landing target. © 2013 IEEE.
AV  - none
A1  - Subramanian, J.A.
A1  - Asirvadam, V.S.
A1  - Zulkifli, S.A.B.M.
A1  - Singh, N.S.S.
A1  - Shanthi, N.
A1  - Lagisetty, R.K.
A1  - Kadir, K.A.
Y1  - 2024///
SN  - 21693536
TI  - Integrating Computer Vision and Photogrammetry for Autonomous Aerial Vehicle Landing in Static Environment
ID  - scholars20243
SP  - 4532
N1  - cited By 2
PB  - Institute of Electrical and Electronics Engineers Inc.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181557999&doi=10.1109%2fACCESS.2024.3349419&partnerID=40&md5=f79b033bdd0369a9693ecf3496a07e3b
ER  -