Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

We review the use of factor graphs for the modeling and solving of large-scale inference
problems in robotics. Factor graphs are a family of probabilistic graphical models, other …

Here, we present a general framework for combining visual odometry and lidar odometry in
a fundamental and first principle method. The method shows improvements in performance …

This paper presents a tightly-coupled multi-sensor fusion algorithm termed LiDAR-inertial-
camera fusion (LIC-Fusion), which efficiently fuses IMU measurements, sparse visual …

Higher level functionality in autonomous driving depends strongly on a precise motion
estimate of the vehicle. Powerful algorithms have been developed. However, their great …

We present a data processing pipeline to online estimate ego‐motion and build a map of the
traversed environment, leveraging data from a 3D laser scanner, a camera, and an inertial …

Simultaneous localization and mapping (SLAM) is a widely researched topic in the field of
robotics, augmented/virtual reality and more dominantly in self-driving cars. SLAM is a …

LiDAR-based Simultaneous Localization and Mapping (LiDAR-SLAM) uses the LiDAR
sensor to localize itself by observing environmental features and incrementally build the …

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 …

In this paper, we present a novel multi-sensor fusion framework for tightly coupled
monocular visual-LiDAR odometry and mapping. Compared to previous visual-LiDAR fusion …