%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2016/09.08.17.44
%2 sid.inpe.br/sibgrapi/2016/09.08.17.44.44
%T Automatic Landmark Selection for UAV Autonomous Navigation
%D 2016
%A Melo, Allan dos Santos,
%A Silva Filho, Paulo,
%A Shiguemori, Elcio Hideiti,
%@affiliation Paulista University - UNIP
%@affiliation Advanced Studies Institute - IEAv
%@affiliation Advanced Studies Institute - IEAv
%E Aliaga, Daniel G.,
%E Davis, Larry S.,
%E Farias, Ricardo C.,
%E Fernandes, Leandro A. F.,
%E Gibson, Stuart J.,
%E Giraldi, Gilson A.,
%E Gois, João Paulo,
%E Maciel, Anderson,
%E Menotti, David,
%E Miranda, Paulo A. V.,
%E Musse, Soraia,
%E Namikawa, Laercio,
%E Pamplona, Mauricio,
%E Papa, João Paulo,
%E Santos, Jefersson dos,
%E Schwartz, William Robson,
%E Thomaz, Carlos E.,
%B Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)
%C São José dos Campos, SP, Brazil
%8 4-7 Oct. 2016
%I Sociedade Brasileira de Computação
%J Porto Alegre
%S Proceedings
%K Landmark, self-adaptative cluster, ORB.
%X Landmark recognition has been showing promissing results for UAVs autonomous navigation by image. Although, the selection of landmarks has a significant influence in the results, more efficient methods to select them are necessary. The work aims to develop an algorithm that selects automatically landmarks through keypoints obtained with ORB. The algorithm is based on a modified X-means approach.
%@language en
%3 Automatic Landmark Selection for UAV Autonomous Navigation.pdf