LiDAR-based place recognition (LPR) plays a pivotal role in autonomous driving, which
assists Simultaneous Localization and Mapping (SLAM) systems in reducing accumulated …

The capability of a mobile robot to efficiently and safely perform complex missions is limited
by its knowledge of the environment, namely the situation. Advanced reasoning, decision …

Place recognition is an important branch of Simultaneous Localization and Mapping
(SLAM), which finds revisited places, thereby reducing error accumulations. Most …

The Lidar Simultaneous Localization and Mapping (Lidar-SLAM) processes the point cloud
from the Lidar and accomplishes location and mapping. Lidar SLAM is usually divided to …

Accurate and robust localization is one of the key modules of autonomous driving and multi-
sensor robotic systems in complex urban scenes. Compared with camera-based …

Mobile robots should be aware of their situation, comprising the deep understanding of their
surrounding environment along with the estimation of its own state, to successfully make …

Agricultural robots must navigate challenging dynamic and semi-structured environments.
Recently, environmental modelling using LiDAR-based SLAM has shown promise in …

Scene completion refers to obtaining dense scene representation from an incomplete
perception of complex 3D scenes. This helps robots detect multi-scale obstacles and …

A key component of graph-based SLAM systems is the ability to detect loop closures in a
trajectory to reduce the drift accumulated over time from the odometry. Most LiDAR-based …

Indoor wayfinding poses significant challenges, as navigating complex environments can be
difficult and time-consuming. Augmented reality (AR) has emerged as a promising solution …