Due to the limitations of LiDAR, such as its high cost, short service life and massive volume,
visual sensors with their lightweight and low cost are attracting more and more attention and …

Inertial sensors are widely utilized in smartphones, drones, vehicles, and wearable devices,
playing a crucial role in enabling ubiquitous and reliable localization. Inertial sensor-based …

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

In the last decade, numerous supervised deep learning approaches have been proposed for
visual–inertial odometry (VIO) and depth map estimation, which require large amounts of …

Neural implicit representations have emerged as a promising solution for providing dense
geometry in Simultaneous Localization and Mapping (SLAM). However, existing methods in …

Visible-infrared person re-identification (ReID) aims to recognize a same person of interest
across a network of RGB and IR cameras. Some deep learning (DL) models have directly …

Limited navigation capabilities of many current robots and UAVs restricts their applications
in GPS denied areas. Large aircraft with complex navigation systems rely on a variety of …

The insufficient number of annotated thermal infrared (TIR) image datasets not only hinders
TIR image-based deep learning networks to have comparable performances to that of RGB …

Abstract Visual Simultaneous Localization and Mapping (VSLAM) technology has become a
key technology in autonomous driving and robot navigation. Relying on camera sensors …

Thermal SLAM outperforms visual SLAM under conditions of nighttime, low visibility (eg, fog,
smoke, and dust), and direct glare. However, the unique non-uniformity correction for …