5 accepted papers
Cross-modal localization has drawn increasing attention in recent years, while the visual relocalization in prior LiDAR maps is less studied. Related methods usually suffer from inconsistency between the 2D texture and 3D geometry, neglecting the intensity features in the LiDAR point cloud. In this…
3D moving object segmentation (MOS) is vital for autonomous systems, providing essential information for downstream tasks like mapping and localization. However, current MOS methods face challenges due to the limitation of existing datasets, which are sparse in moving objects and limited in scene di…
Visual localization for mobile robots and intelligent vehicles in prior LiDAR maps can achieve high accuracy and low cost. However, algorithms for finding the cross-modal correspondences between images and LiDAR map points are not yet stable. In this paper, we propose a monocular visual localization…
Point cloud registration (PCR) is a key problem for robotics, autonomous driving, and other applications. Constructing generalizable 3D descriptors and determining whether a 3D descriptor is in the overlapping area are challenging tasks in PCR. Despite the fast evolution of learning-based 3D descrip
Point cloud registration is a key problem for robotics, computer vision, and other applications. Previous global registration algorithms are sensitive to noises or partial occlusion, while local registration algorithms are highly dependent on initial angles. To solve these problems, we propose Centr