%0 Journal Article
%4 urlib.net/www/2022/01.25.16.29
%2 urlib.net/www/2022/01.25.16.29.18
%@doi 10.3390/rs14020361
%@issn 2072-4292
%T Automatic Position Estimation Based on Lidar × Lidar Data for Autonomous Aerial Navigation in the Amazon Forest Region
%D 2022
%8 Jan.
%9 journal article
%A Salles, Roberto Neves,
%A Campos Velho, Haroldo Fraga de,
%A Shiguemori, Elcio Hideiti,
%@affiliation Instituto de Estudos Avançados (IEAv)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto de Estudos Avançados (IEAv)
%@electronicmailaddress salles@ieav.cta.br
%@electronicmailaddress haroldo.camposvelho@inpe.br
%@electronicmailaddress elcio@ieav.cta.br
%B Remote Sensing
%V 14
%N 2
%P e361
%K Amazon region, LiDAR, Normalized cross-correlation (NCC), Template matching, Terrain-referenced navigation (TRN).
%X 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.
%@language en
%3 remotesensing-14-00361-v2.pdf