The state-of-the-art decision and planning approaches for autonomous vehicles have
moved away from manually designed systems, instead focusing on the utilisation of large …

This article surveys recent progress and discusses future opportunities for simultaneous
localization and map** (SLAM) in extreme underground environments. SLAM in …

This article presents FAST-LIO2: a fast, robust, and versatile LiDAR-inertial odometry
framework. Building on a highly efficient tightly coupled iterated Kalman filter, FAST-LIO2 …

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

Place recognition is a key module in robotic navigation. The existing line of studies mostly
focuses on visual place recognition to recognize previously visited places solely based on …

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

We provide an open-source C++ library for real-time metric-semantic visual-inertial
Simultaneous Localization And Map** (SLAM). The library goes beyond existing visual …

Humans are able to form a complex mental model of the environment they move in. This
mental model captures geometric and semantic aspects of the scene, describes the …

Accurate state estimation is a fundamental problem for autonomous robots. To achieve
locally accurate and globally drift-free state estimation, multiple sensors with complementary …