Simultaneous localization and mapping (SLAM) technology is essential for robots to
navigate unfamiliar environments. It utilizes the sensors the robot carries to answer the …

The development of autonomous vehicles has prompted an interest in exploring various
techniques in navigation. One such technique is simultaneous localization and mapping …

Most existing dynamic simultaneous localization and mapping (SLAM) methods integrating
neural networks require high computational support and predefined categories of dynamic …

Current simultaneous localization and mapping (SLAM) algorithms perform well in static
environments but easily fail in dynamic environments. Recent works introduce deep learning …

Dynamic Object-aware SLAM (DOS) exploits object-level information to enable robust
motion estimation in dynamic environments. Existing methods mainly focus on identifying …

The dynamic factors in the environment will lead to the decline of camera localization
accuracy due to the violation of the static environment assumption of SLAM algorithm …

Multi-modal unsupervised domain adaptation (MM-UDA) for 3D semantic segmentation is a
practical solution to embed semantic understanding in autonomous systems without …

Simultaneous localization and mapping (SLAM) has been widely used in augmented reality
(AR), virtual reality (VR), robotics, and autonomous vehicles as the theoretical basis for …

Estimating human motion from video is an active research area due to its many potential
applications. Most state-of-the-art methods predict human shape and posture estimates for …

Simultaneous localization and mapping (SLAM) is crucial for the progression of autonomous
systems, including autonomous driving, augmented reality (AR), and robotics. Traditionally …