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 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 …

We present ESLAM, an efficient implicit neural representation method for Simultaneous
Localization and Mapping (SLAM). ESLAM reads RGB-D frames with unknown camera …

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead
of capturing images at a fixed rate, they asynchronously measure per-pixel brightness …

We propose D3VO as a novel framework for monocular visual odometry that exploits deep
networks on three levels--deep depth, pose and uncertainty estimation. We first propose a …

We introduce TransformerFusion, a transformer-based 3D scene reconstruction approach.
From an input monocular RGB video, the video frames are processed by a transformer …

Abstract We propose Deep Patch Visual Odometry (DPVO), a new deep learning system for
monocular Visual Odometry (VO). DPVO uses a novel recurrent network architecture …

We present DM-VIO, a monocular visual-inertial odometry system based on two novel
techniques called delayed marginalization and pose graph bundle adjustment. DM-VIO …

Deep learning based localization and mapping has recently attracted significant attention.
Instead of creating hand-designed algorithms through exploitation of physical models or …