@InProceedings{SallesCampShig:2022:AcSeUA,
               author = "Salles, Roberto N. and Campos Velho, Haroldo Fraga de and 
                         Shiguemori, Elcio Hideiti",
          affiliation = "{Instituto de Estudos Avan{\c{c}}ados (IEAv)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto de Estudos 
                         Avan{\c{c}}ados (IEAv)}",
                title = "Active Sensors for UAV Autonomous Navigation on Amazon Region",
            booktitle = "Anais...",
                 year = "2022",
         organization = "International Conference on Electrical, Computer, Communications 
                         and Mechatronics Engineering (ICECCME)",
             keywords = "aerial drone, autonomous navigation, template, matching, LiDAR 
                         data, InSAR images.",
             abstract = "This work is an additional exploration inspired by the results of 
                         an earlier study of the geo-localization problem over a densely 
                         forested region of the Brazilian Amazon forest. Light Detection 
                         and Ranging (LiDAR) data was post-processed from 3D cloud point 
                         format to 2D elevation images and template matching was used with 
                         normalized cross-correlation. Within a constrained search area it 
                         was possible to geo-localize the 2D patches of surface images on 
                         Interferometric Synthetic Aperture Radar (InSAR) elevation data. 
                         The transect 3D cloud point was transformed into a 12.5m 
                         resolution 2D surface image with the circular binning procedure, a 
                         resolution compatible with the Advanced Land Observation Satellite 
                         (ALOS) elevation maps used as reference. This application of 
                         template matching achieved 36m root mean square error, or about 4 
                         pixels of error, over the LiDAR transect route. Position 
                         estimation is essential for autonomous navigation of aerial 
                         vehicles, and experiments with LiDAR data show potential for 
                         localization over densely forested regions, where Computer Vision 
                         methods using optical camera data may fail to acquire 
                         distinguishable features.",
  conference-location = "Maldives",
      conference-year = "16-18 Nov. 2022",
           targetfile = "ICECCME2022_Proceedings.pdf",
        urlaccessdate = "12 maio 2024"
}