Abstract Knowledge about the own pose is key for all mobile robot applications. Thus pose
estimation is part of the core functionalities of mobile robots. Over the last two decades …

Global registration is a fundamental task that estimates the relative pose between two
viewpoints of 3D point clouds. However, there are two issues that degrade the performance …

Point set registration is the technology used to estimate the spatial transformation between
two LiDAR scans, which is challenging in the presence of outlier correspondences and …

Numerous researchers have conducted studies to achieve fast and robust ground-optimized
LiDAR odometry methods for terrestrial mobile platforms. In particular, ground-optimized …

Radar sensors are emerging as solutions for perceiving surroundings and estimating ego-
motion in extreme weather conditions. Unfortunately, radar measurements are noisy and …

This study introduces a novel framework, G3Reg, for fast and robust global registration of
LiDAR point clouds. In contrast to conventional complex keypoints and descriptors, we …

Mobile robots navigating in outdoor environments frequently encounter the issue of
undesired traces left by dynamic objects and manifested as obstacles on map, impeding …

Given a building floorplan, humans can localize themselves by matching the observation of
the environment with the floorplan using geometric, semantic, and topological clues …

This paper presents Segregator, a global point cloud registration framework that exploits
both semantic information and geometric distribution to efficiently build up outlier-robust …

Localization has been a challenging task for autonomous navigation. A loop detection
algorithm must overcome environmental changes for the place recognition and re …