Enhanced Unmanned Aerial Vehicle Localization in Dynamic Environments Using Monocular Simultaneous Localization and Mapping and Object Tracking

Youssef El Gaouti (), Fouad Khenfri, Mehdi Mcharek and Cherif Larouci
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Youssef El Gaouti: École Supérieure des Techniques Aéronautiques et de Construction Automobile, ESTACA’Lab—Paris-Saclay, F-78180 Montigny-le-Bretonneux, France
Fouad Khenfri: École Supérieure des Techniques Aéronautiques et de Construction Automobile, ESTACA’Lab—Laval, F-53000 Laval, France
Mehdi Mcharek: École Supérieure des Techniques Aéronautiques et de Construction Automobile, ESTACA’Lab—Paris-Saclay, F-78180 Montigny-le-Bretonneux, France
Cherif Larouci: École Supérieure des Techniques Aéronautiques et de Construction Automobile, ESTACA’Lab—Paris-Saclay, F-78180 Montigny-le-Bretonneux, France

Mathematics, 2024, vol. 12, issue 11, 1-17

Abstract: This work proposes an innovative approach to enhance the localization of unmanned aerial vehicles (UAVs) in dynamic environments. The methodology integrates a sophisticated object-tracking algorithm to augment the established simultaneous localization and mapping (ORB-SLAM) framework, utilizing only a monocular camera setup. Moving objects are detected by harnessing the power of YOLOv4, and a specialized Kalman filter is employed for tracking. The algorithm is integrated into the ORB-SLAM framework to improve UAV pose estimation by correcting the impact of moving elements and effectively removing features connected to dynamic elements from the ORB-SLAM process. Finally, the results obtained are recorded using the TUM RGB-D dataset. The results demonstrate that the proposed algorithm can effectively enhance the accuracy of pose estimation and exhibits high accuracy and robustness in real dynamic scenes.

Keywords: computer vision; UAV localization; object tracking; dynamic environment (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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