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Ιδρυματικό Αποθετήριο [SANDBOX]
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/C917C95B-4206-45E2-9FA1-1D793EEEDEC8 | - |
| Αναγνωριστικό | https://doi.org/10.3390/robotics12050137 | - |
| Αναγνωριστικό | https://www.mdpi.com/2218-6581/12/5/137 | - |
| Γλώσσα | en | - |
| Μέγεθος | 28 pages | en |
| Τίτλος | Keypoint detection and description through deep learning in unstructured environments | en |
| Δημιουργός | Petrakis Georgios | en |
| Δημιουργός | Πετρακης Γεωργιος | el |
| Δημιουργός | Partsinevelos Panagiotis | en |
| Δημιουργός | Παρτσινεβελος Παναγιωτης | el |
| Εκδότης | MDPI | en |
| Περιγραφή | The implementation of the doctoral thesis was co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning» in the context of the Act “Enhancing Human Resources Research Potential by undertaking a Doctoral Research” Sub-action 2: IKY Scholarship Programme for PhD candidates in the Greek Universities. | en |
| Περίληψη | Feature extraction plays a crucial role in computer vision and autonomous navigation, offering valuable information for real-time localization and scene understanding. However, although multiple studies investigate keypoint detection and description algorithms in urban and indoor environments, far fewer studies concentrate in unstructured environments. In this study, a multi-task deep learning architecture is developed for keypoint detection and description, focused on poor-featured unstructured and planetary scenes with low or changing illumination. The proposed architecture was trained and evaluated using a training and benchmark dataset with earthy and planetary scenes. Moreover, the trained model was integrated in a visual SLAM (Simultaneous Localization and Maping) system as a feature extraction module, and tested in two feature-poor unstructured areas. Regarding the results, the proposed architecture provides a mAP (mean Average Precision) in a level of 0.95 in terms of keypoint description, outperforming well-known handcrafted algorithms while the proposed SLAM achieved two times lower RMSE error in a poor-featured area with low illumination, compared with ORB-SLAM2. To the best of the authors’ knowledge, this is the first study that investigates the potential of keypoint detection and description through deep learning in unstructured and planetary environments. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2025-02-20 | - |
| Ημερομηνία Δημοσίευσης | 2023 | - |
| Θεματική Κατηγορία | Feature extraction | en |
| Θεματική Κατηγορία | Unstructured environments | en |
| Θεματική Κατηγορία | Visual SLAM | en |
| Θεματική Κατηγορία | Deep learning | en |
| Θεματική Κατηγορία | Autonomous navigation | en |
| Βιβλιογραφική Αναφορά | G. Petrakis and P. Partsinevelos, “Keypoint detection and description through deep learning in unstructured environments,” Robotics, vol. 12, no. 5, Sep. 2023, doi: 10.3390/robotics12050137. | en |
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