Keypoint detection and description through deep learning in unstructured environmentsKeypoint detection and description through deep learning in unstructured environments Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2025-02-202023enFeature 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.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.http://creativecommons.org/licenses/by/4.0/Robotics125Petrakis_et_al_Robotics_12(5)_2023.pdfChania [Greece]Library of TUC2025-02-20application/pdf14.3 MBfree Petrakis Georgios Πετρακης Γεωργιος Partsinevelos Panagiotis Παρτσινεβελος Παναγιωτης MDPI Feature extraction Unstructured environments Visual SLAM Deep learning Autonomous navigation