Aerial Imagery Based Position and Heading Estimation: For maritime autonomous vessels in GNSS-challenged environments - Bergman, Algot; Lindell, Teodor (My University, 2023)
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.
Keywords: Extended Kalman filter; LiDAR; aerial imagery; particle swarm optimization; computer vision; point cloud; localization; cross-correlation