2026-04-17T07:54:54Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/247112026-02-02T08:54:39Zcom_2072_1033col_2072_452950

Fast online learning and detection of natural landmarks for autonomous aerial robots Villamizar Vergel, Michael Alejandro Sanfeliu Cortés, Alberto Moreno-Noguer, Francesc Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial Institut de Robòtica i Informàtica Industrial Universitat Politècnica de Catalunya. VIS - Visió Artificial i Sistemes Intel·ligents Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Reconeixement de formes Pattern recognition systems Computer vision aerospace robotics computer vision pattern recognition object detection online learning autonomous robots Reconeixement de formes (Informàtica) Visió per ordinador We present a method for efficiently detecting natural landmarks that can handle scenes with highly repetitive patterns and targets progressively changing its appearance. At the core of our approach lies a Random Ferns classifier, that models the posterior probabilities of different views of the target using multiple and independent Ferns, each containing features at particular positions of the target. A Shannon entropy measure is used to pick the most informative locations of these features. This minimizes the number of Ferns while maximizing its discriminative power, allowing thus, for robust detections at low computational costs. In addition, after offline initialization, the new incoming detections are used to update the posterior probabilities on the fly, and adapt to changing appearances that can occur due to the presence of shadows or occluding objects. All these virtues, make the proposed detector appropriate for UAV navigation. Besides the synthetic experiments that will demonstrate the theoretical benefits of our formulation, we will show applications for detecting landing areas in regions with highly repetitive patterns, and specific objects under the presence of cast shadows or sudden camera motions. Preprint 2014 Conference report Villamizar , M.; Sanfeliu, A.; Moreno-Noguer, F. Fast online learning and detection of natural landmarks for autonomous aerial robots. A: IEEE International Conference on Robotics and Automation. "2014 IEEE International Conference on Robotics and Automation (ICRA)". Hong Kong: Institute of Electrical and Electronics Engineers (IEEE), 2014, p. 4996-5003. https://hdl.handle.net/2117/24711 10.1109/ICRA.2014.6907591 eng info:eu-repo/grantAgreement/EC/FP7/287654/EU/European Coordinated Research on Long-term Challenges in Information and Communication Sciences and Technologies/CHIST-ERA II info:eu-repo/grantAgreement/EC/FP7/287617/EU/Aerial Robotics Cooperative Assembly System/ARCAS Restricted access - publisher's policy 8 p. application/pdf Institute of Electrical and Electronics Engineers (IEEE)