Scene understanding is an important capability for robots acting in unstructured
environments. While most SLAM approaches provide a geometrical representation of the …

Human-computer interaction requires accurate localization and effective mapping, while
dynamic objects can influence the accuracy of localization and mapping. State-of-the-art …

We propose a novel dense mapping framework for sparse visual SLAM systems which
leverages a compact scene representation. State-of-the-art sparse visual SLAM systems …

Dynamic environments pose a severe challenge to visual SLAM as moving objects
invalidate the assumption of a static background. While recent works employ deep learning …

The availability of real-time semantics greatly improves the core geometric functionality of
SLAM systems, enabling numerous robotic and AR/VR applications. We present a new …

Simultaneous Localization and Mapping (SLAM) has been widely applied in computer
vision and robotics. For the dynamic environments which are very common in the real word …

Simultaneous Localisation and Mapping (SLAM) is one of the fundamental problems in
autonomous mobile robots where a robot needs to reconstruct a previously unseen …

Existing simultaneous localization and mapping (SLAM) algorithms are not robust in
challenging low-texture environments because there are only few salient features. The …

Fiducial markers can encode rich information about the environment and aid Visual SLAM
(VSLAM) approaches in reconstructing maps with practical semantic information. Current …

Simultaneous Localization and Mapping (SLAM) is considered significant for intelligent
mobile robot autonomous pathfinding. Over the past years, many successful SLAM systems …