Fast online learning and detection of natural landmarks for autonomous aerial robots
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessRestricted access - publisher's policy
European Commisision's projectCHIST-ERA II - European Coordinated Research on Long-term Challenges in Information and Communication Sciences and Technologies - II (EC-FP7-287654)
ARCAS - Aerial Robotics Cooperative Assembly System (EC-FP7-287617)
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.
CitationVillamizar , 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.
- VIS - Visió Artificial i Sistemes Intel.ligents - Ponències/Comunicacions de congressos 
- Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial - Ponències/Comunicacions de congressos 
- IRI - Institut de Robòtica i Informàtica Industrial, CSIC-UPC - Ponències/Comunicacions de congressos 
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