Abstract Simultaneous Localization and Mapping (SLAM) is a key problem in the field of
Artificial Intelligence and mobile robotics that addresses the problem of localization and …

Although Global Navigation Satellite Systems (GNSSs) generally provide adequate
accuracy for outdoor localization, this is not the case for indoor environments, due to signal …

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

Simultaneous localization and mapping (SLAM) in dynamic environments is an important
problem in robotics navigation, yet it is less studied. In this paper, we present a novel …

The important problem of Simultaneous Localization and Mapping (SLAM) in dynamic
environments is less studied than the counterpart problem in static settings. In this paper, we …

Most of the current visual Simultaneous Localization and Mapping (SLAM) algorithms are
designed based on the assumption of a static environment, and their robustness and …

The problem of quantifying robot localization safety in the presence of undetected sensor
faults is critical when preparing for future applications where robots may interact with …

SLAM (Simultaneous Localization and Mapping) is a fundamental problem when an
autonomous mobile robot explores an unknown environment by constructing/updating the …

In this paper, an adaptive unscented Kalman filter (AUKF) algorithm is applied to
simultaneous localization and mapping (SLAM), based on adaptation of a scaling …

Visual odometry and visual simultaneous localization and mapping aid in tracking the
position of a camera and mapping the surroundings using images. It is an important part of …