@Article{SallesCampShig:2022:AuPoEs,
               author = "Salles, Roberto Neves 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 = "Automatic Position Estimation Based on Lidar × Lidar Data for 
                         Autonomous Aerial Navigation in the Amazon Forest Region",
              journal = "Remote Sensing",
                 year = "2022",
               volume = "14",
               number = "2",
                pages = "e361",
                month = "Jan.",
             keywords = "Amazon region, LiDAR, Normalized cross-correlation (NCC), Template 
                         matching, Terrain-referenced navigation (TRN).",
             abstract = "In this paper we post-process and evaluate the position estimation 
                         of pairs of template windows and geo-referenced images generated 
                         from LiDAR cloud point data using the Normalized Cross-Correlation 
                         (NCC) method. We created intensity, surface and terrain pairs of 
                         images for use with template matching, with 5 m pixel spacing, 
                         through binning. We evaluated square and circular binning 
                         approaches, without filtering the original data. Template matching 
                         achieved approximately 7 m root mean square error (RMSE) on 
                         intensity and surface templates on the respective georeferenced 
                         images, while on terrain templates it had many mismatches due to 
                         insufficient terrain features over the assumed flight transect. 
                         Analysis of NCC showed the possibility of rejecting bad matches of 
                         intensity and surface templates, but terrain templates required an 
                         additional criteria of flatness for rejection. The combined NCC of 
                         intensity, surface and terrain proved stable for rejection of bad 
                         matches and had the lowest RMSE. Filtering outliers from surface 
                         images changed very little the accuracy of the matches, but 
                         greatly improved correlation values, indicating that the forest 
                         canopy might have the best features for geo-localization with 
                         template matching. Position estimation is essential for autonomous 
                         navigation of aerial vehicles and the these experiments with LiDAR 
                         data show potential for localization over densely forested regions 
                         where methods using optical camera data may fail to acquire 
                         distinguishable features.",
                  doi = "10.3390/rs14020361",
                  url = "http://dx.doi.org/10.3390/rs14020361",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-14-00361-v2.pdf",
        urlaccessdate = "03 maio 2024"
}