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 mapping solution in
different driving environment. In this context, Simultaneous Localization and Mapping …

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
mapping, LVI-SAM, that achieves real-time state estimation and map-building with high …

Methods for 3D lane detection have been recently proposed to address the issue of
inaccurate lane layouts in many autonomous driving scenarios (uphill/downhill, bump, etc.) …

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

We present a novel tightly-coupled LiDAR-inertial odometry and mapping scheme for both
solid-state and mechanical LiDARs. As frontend, a feature-based lightweight LiDAR …

The rapid development of autonomous driving and mobile mapping calls for off-the-shelf
LiDAR SLAM solutions that are adaptive to LiDARs of different specifications on various …

In this paper, we present BEVerse, a unified framework for 3D perception and prediction
based on multi-camera systems. Unlike existing studies focusing on the improvement of …

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 …