Recent researches on robotics have shown significant improvement, spanning from
algorithms, mechanics to hardware architectures. Robotics, including manipulators, legged …

Simultaneous localization and map** (SLAM) is an active research topic in machine
vision and robotics. It has various applications in many different fields such as mobile robots …

Simultaneous Localization and Map** (SLAM) is a critical task for autonomous navigation.
However, due to the computational complexity of SLAM algorithms, it is very difficult to …

In the rapidly evolving field of autonomous cars, advanced deep learning systems have
ushered in a new era of innovation, enabling the integration of unique features into vehicles …

A key real-time task in autonomous systems is simultaneous localization and map**
(SLAM). Although prior work has proposed hardware accelerators to process SLAM in real …

Visual Simultaneous Localization and Map** (vSLAM) is the method of employing an
optical sensor to map the robot's observable surroundings while also identifying the robot's …

Simultaneous localization and map** (SLAM) becomes a solved problem in terms of the
algorithms. In order to use them in autonomous mobile robots, researches are focusing on …

Perception of the indoor environment is the basis of mobile robot localization, navigation,
and path planning, and it is particularly important to construct semantic maps in real time …

Visual Odometry (VO) systems are widely used to determine the position and orientation of a
robot or camera in an unknown environment. They are deployed on resource-constrained …

Recently, augmented reality technologies are debuting to the mainstream markets.
Simultaneous Localization and Map** (SLAM), which serves as the core to drive …