The ability of intelligent unmanned platforms to achieve autonomous navigation and
positioning in a large-scale environment has become increasingly demanding, in which …

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

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-visual-inertial odometry via smoothing and
map**, LVI-SAM, that achieves real-time state estimation and map-building with high …

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 …

This letter presents a computationally efficient and robust LiDAR-inertial odometry
framework. We fuse LiDAR feature points with IMU data using a tightly-coupled iterated …

Once an academic venture, autonomous driving has received unparalleled corporate
funding in the last decade. Still, operating conditions of current autonomous cars are mostly …

A growing interest in autonomous driving (AD) and intelligent vehicles (IVs) is fueled by their
promise for enhanced safety, efficiency, and economic benefits. While previous surveys …

In this letter, we propose a robust, real-time tightly-coupled multi-sensor fusion framework,
which fuses measurements from LiDAR, inertial sensor, and visual camera to achieve robust …

Field robotics in perceptually-challenging environments require fast and accurate state
estimation, but modern LiDAR sensors quickly overwhelm current odometry algorithms. To …