Autonomous driving vehicles require both a precise localization and mapping solution in
different driving environment. In this context, Simultaneous Localization and Mapping …

In the past two decades, robotics in construction (RiC) has become an interdisciplinary
research field that integrates a large number of urgent technologies (eg, additive …

We propose a framework for tightly-coupled lidar inertial odometry via smoothing and
mapping, LIO-SAM, that achieves highly accurate, real-time mobile robot trajectory …

We propose a framework for tightly-coupled lidar-visual-inertial odometry via smoothing and
mapping, LVI-SAM, that achieves real-time state estimation and map-building with high …

Simultaneous Localization and Mapping (SLAM) has wide robotic applications such as
autonomous driving and unmanned aerial vehicles. Both computational efficiency and …

Distributed as an open‐source library since 2013, real‐time appearance‐based mapping
(RTAB‐Map) started as an appearance‐based loop closure detection approach with …

We propose a lightweight and ground-optimized lidar odometry and mapping method, LeGO-
LOAM, for realtime six degree-of-freedom pose estimation with ground vehicles. LeGO …

Reliable and accurate localization and mapping are key components of most autonomous
systems. Besides geometric information about the mapped environment, the semantics …

Multi-beam LiDAR sensors are increasingly used in robotics, particularly with autonomous
cars for localization and perception tasks, both relying on the ability to build a precise map of …

Autonomous navigation requires both a precise and robust mapping and localization
solution. In this context, Simultaneous Localization and Mapping (SLAM) is a very well …