Simultaneous localization and mapping (SLAM) techniques are widely researched, since
they allow the simultaneous creation of a map and the sensors' pose estimation in an …

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 introduce DROID-SLAM, a new deep learning based SLAM system. DROID-SLAM
consists of recurrent iterative updates of camera pose and pixelwise depth through a Dense …

Neural implicit representations have recently demonstrated compelling results on dense
Simultaneous Localization And Mapping (SLAM) but suffer from the accumulation of errors …

This article presents ORB-SLAM3, the first system able to perform visual, visual-inertial and
multimap SLAM with monocular, stereo and RGB-D cameras, using pin-hole and fisheye …

Autonomous, agile quadrotor flight raises fundamental challenges for robotics research in
terms of perception, planning, learning, and control. A versatile and standardized platform is …

The increasing world population makes it necessary to fight challenges such as climate
change and to realize production efficiently and quickly. However, the minimum cost …

In this tutorial, we provide principled methods to quantitatively evaluate the quality of an
estimated trajectory from visual (-inertial) odometry (VO/VIO), which is the foundation of …

In this review, we provide an overview of emerging trends and challenges in the field of
intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of …