@InCollection{BragaCampShig:2020:LiNoPa,
author = "Braga, Jos{\'e} Renato Garcia and Campos Velho, Haroldo Fraga de
and Shiguemori, Elcio H.",
title = "Lidar and non-extensive particle filter for UAV autonomous
navigation",
booktitle = "Computational intelligence in emerging technologies for
engineering applications",
publisher = "Springer",
year = "2020",
editor = "Santiago, O. L. and Corona, C. C. and Silva Neto, A. J. and
Verdegay, J. L.",
pages = "227--238",
keywords = "Unmanned aerial vehicle (UAV) · Autonomous navigation · LiDAR data
· Non-extensive particle filter · Data fusion.",
abstract = "Unmanned aerial vehicle (UAV) is a technology employed for several
applications nowadays. One important UAV research topic is the
autonomous navigation (AN). The standard procedure for AN is to
fuse the signals from an inertial navigation system (INS) and from
a global navigation satellite system (GNSS). Our approach to
perform the autonomous navigation uses a computer vision system,
instead of GNSS signal, associated to the visual odometry. The two
techniques applied to estimate the UAV position are combined by a
non-extensive particle filter. However, the development of a
computer vision system for estimating the UAV position in a
situation of flight over water-covered areas and flight in low
light conditions is a challenge. Our approach uses images from an
active sensor called light detection and ranging (LiDAR) to allow
the flight in such conditions.",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto de Estudos
Avan{\c{c}}ados (IEAv)}",
isbn = "978-3-030-34409-2",
language = "en",
targetfile = "braga_lidar.pdf",
urlaccessdate = "11 maio 2024"
}