Most of the state-of-the-art visual simultaneous localization and mapping (V-SLAM)
algorithms perform well in structured environments. However, the assumption of a static …

Now, most existing dynamic RGB-D SLAM methods are based on deep learning or
mathematical models. Abundant training sample data is necessary for deep learning, and …

The visual SLAM in dynamic environment has been regarded as a fundamental task for
robots. Currently, existing works achieve good performance in only indoor scenes due to the …

Visual simultaneous localization and mapping (vSLAM) is inherently constrained by the
static world assumption, which renders success in the presence of dynamic objects rather …

Vision simultaneous localization and mapping (SLAM) is essential for adapting to new
environments and for localization and is therefore widely used in robotics. However …

Visual Simultaneous Localization and Mapping (SLAM) in dynamic scenes is a prerequisite
for robot-related applications. Most of the existing SLAM algorithms mainly focus on dynamic …

With the continuous development of robotics and computer vision technology, mobile robots
have been widely applied in various fields. In this process, semantic maps for robots have …

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 …

Aiming at the problems of low localization accuracy and poor mapping consistency for
mobile robots in dynamic environments, this paper presents a 3D simultaneous localization …

Simultaneous localization and mapping (SLAM) is considered to be an important way for
some smart devices to perform automatic path planning, and many successful SLAM …