3 accepted papers
This letter presents a novel method for geographical localization by registering satellite maps with LiDAR point clouds. This method includes a Transformer-based 2D-3D matching network called D-GLSNet that directly matches the LiDAR point clouds and satellite images through end-to-end learning. With
In this letter, we propose a probabilistic continuous-time visual-inertial odometry (VIO) for rolling shutter cameras. The continuous-time trajectory formulation naturally facilitates the fusion of asynchronized high-frequency IMU data and motion-distorted rolling shutter images. To prevent intracta
This paper proposes an online large-scale dense mapping method for UAVs with a height of 150–250 meters. We first fuse the GPS with the visual odometry to estimate the scaled poses and sparse points. In order to use the depth of sparse points for depth map, we propose Sparse Confidence Cascade View-…