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

Abstract Simultaneous Localization and Mapping (SLAM) have been widely studied over the
last years for autonomous vehicles. SLAM achieves its purpose by constructing a map of the …

In this paper, we propose a novel LiDAR-Inertial-Visual sensor fusion framework termed R 3
LIVE, which takes advantage of measurement of LiDAR, inertial, and visual sensors to …

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 …

We propose Super Odometry, a high-precision multi-modal sensor fusion framework,
providing a simple but effective way to fuse multiple sensors such as LiDAR, camera, and …

To achieve accurate and robust pose estimation in Simultaneous Localization and Mapping
(SLAM) task, multisensor fusion is proven to be an effective solution and thus provides great …

We present an efficient multi-sensor odometry system for mobile platforms that jointly
optimizes visual, lidar, and inertial information within a single integrated factor graph. This …

We investigate a novel way to integrate visual SLAM and lidar SLAM. Instead of enhancing
visual odometry via lidar depths or using visual odometry as the motion initial guess of lidar …

Deep learning has had remarkable success in robotic perception, but its data-centric nature
suffers when it comes to generalizing to ever-changing environments. By contrast, physics …

With the wide penetration of smart robots in multifarious fields, the simultaneous localization
and mapping (SLAM) technique in robotics has attracted growing attention in the community …