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

This paper aims to review current approaches for real-time artificial intelligence-based visual
Simultaneous Localization and Mapping (vSLAM) operating in dynamic environments …

In this paper, we present a tightly-coupled visual-inertial object-level multi-instance dynamic
SLAM system. Even in extremely dynamic scenes, it can robustly optimise for the camera …

Creating 3D semantic reconstructions of environments is fundamental to many applications,
especially when related to autonomous agent operation (eg., goal-oriented navigation or …

Simultaneous localization and mapping (SLAM) in slowly varying scenes is important for
long-term robot task completion. Failing to detect scene changes may lead to inaccurate …

Augmented Reality (AR) has increasingly benefited from the use of Simultaneous
Localization and Mapping (SLAM) systems. This technology has enabled developers to …

Maintaining an up-to-date map to reflect recent changes in the scene is very important,
particularly in situations involving repeated traversals by a robot operating in an …

Being able to explore an environment and understand the location and type of all objects
therein is important for indoor robotic platforms that must interact closely with humans …

Recent Semantic SLAM methods combine classical geometry-based estimation with deep
learning-based object detection or semantic segmentation. In this paper we evaluate the …

Simultaneous Localization & Mapping (SLAM) is the process of building a mutual
relationship between localization and mapping of the subject in its surrounding environment …