@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 = "21 maio 2024"
}