Visual Slam-Based Mapping and Localization for Aerial Images
by Onur Eker 1 
, Hakan Cevikalp 2,* and Hasan Saribas 3
1 Havelsan, Ankara, Turkey
2 Eskisehir Osmangazi University, Machine Learning and Computer Vision Laboratory, Eskisehir, Turkey
3 Eskisehir Technical University, Faculty of Aeronautics and Astronautics, Eskisehir, Turkey
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 1, Issue 1, Page # 01-09, 2022; DOI: 10.55708/js0101001
Keywords: SLAM, Mapping, Object detection, Orthomosaic, Aerial imaging
Received: 27 December 2022, Revised: 03 February 2022, Accepted: 06 February 2022, Published Online: 24 February 2022
AMA Style
Eker O, Cevikalp H, Saribas H., Visual Slam-Based Mapping and Localization for Aerial Images, Journal of Engineering Research and Sciences. 2022;1(1):1-9. DOI: 10.55708/js0101001
Chicago/Turabian Style
O. Eker, H. Cevikalp, H. Saribas “Visual Slam-Based Mapping and Localization for Aerial Images”, Journal of Engineering Research and Sciences, vol. 1, no. 1, pp. 1-9 (2022). DOI: 10.55708/js0101001
IEEE Style
O. Eker, H. Cevikalp, H. Saribas “Visual Slam-Based Mapping and Localization for Aerial Images”, Journal of Engineering Research and Sciences, vol. 1, no. 1, pp. 1-9 (2022). DOI: 10.55708/js0101001
Fast and accurate observation of an area in disaster scenarios such as earthquake, flood and avalanche is crucial for first aid teams. Digital surface models, orthomosaics and object detection algorithms can play an important role for rapid decision making and response in such scenarios. In recent years, Unmanned Aerial Vehicles (UAVs) have become increasingly popular because of their ability to perform different tasks at lower costs. A real-time orthomosaic generated by using UAVs can be helpful for various tasks where both speed and efficiency are required. An orthomosaic provides an overview of the area to be observed, and helps the operator to find the regions of interest. Then, object detection algorithms help to identify the desired objects in those regions. In this study, a monocular SLAM based system, which combines the camera and GPS data of the UAV, has been developed for mapping the observed environment in real-time. A deep learning based state-of-the-art object detection method is adapted to the system in order to detect objects in real time and acquire their global positions. The performance of the developed method is evaluated in both single and multiple UAVs scenarios.
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