Aerial Imagery Based Position and Heading Estimation: For maritime autonomous vessels in GNSS-challenged environments
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
One of the most important parts of an autonomous vehicle is the estimation of its
position and orientation, which often comes from a GNSS sensor. However, the performance
is degraded when the GNSS signals are obstructed. Nowadays, a popular
implementation for estimation with accurate precision is to fuse the information
from IMU, GNSS and LiDARs. The method of how a position and orientation are
calculated from the LiDAR measurements can be done in different ways. This thesis
presents a method of how position and orientation can be effectively estimated by
matching LiDAR measurements to an aerial image. The method consists of projecting
the LiDAR measurements onto the aerial image, filtering the measurements and
the image, and then calculating the cross-correlation. The orientation is estimated
via stochastic optimization which also finds the maximum correlation to update the
position. This is fused in an EKF with information from IMU and a GNSS sensor to
get a more precise estimation. The conclusion of the thesis is that the method works
well in estimating the position and heading in a GNSS-challenged environment.
Beskrivning
Ämne/nyckelord
Extended Kalman filter, LiDAR, aerial imagery, particle swarm optimization, computer vision, point cloud, localization, cross-correlation