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Fast online learning and detection of natural landmarks for autonomous aerial robots

AutorVillamizar, Michael; Sanfeliu, Alberto; Moreno-Noguer, Francesc
Fecha de publicación2014
EditorInstitute of Electrical and Electronics Engineers
CitaciónIEEE International Conference on Robotics and Automation: 4996-5003 (2014)
ResumenWe 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.
DescripciónPresentado al ICRA 2014 celebrado en Hong Kong del 31 de mayo al 7 de junio.
Versión del editorhttp://dx.doi.org/10.1109/ICRA.2014.6907591
Identificadoresissn: 1050-4729
Aparece en las colecciones: (IRII) Artículos
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