4 accepted papers
Implicit representations for LiDAR-based Simultaneous Localization and Mapping (SLAM) offer significant advantages in storage efficiency and expressive power over traditional explicit maps. However, a critical limitation for implicit SLAM is their deterministic nature, which prevents the quantificat…
This paper presents a universal LiDAR point cloud global localization framework based on multi-sector overlapping loss to address the localization challenges caused by heterogeneous LiDAR point clouds with varying resolutions, scanning formats, and field of view differences. The proposed method firs
One-shot global localization is crucial in many robotic applications, providing significant advantages during initialization and relocalization processes. However, LiDAR-based one-shot global localization methods encounter challenges, including local feature matching errors, sensitivity to dynamic o
This paper presents a novel one-shot global localization algorithm based on semantic-guided triangle descriptors to address initialization and global localization challenges in GNSSdenied environments. By encoding semantic geometric information into triangle descriptors, the proposed approach achiev