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

The recent fast development in computer vision and mobile sensor technology such as
mobile LiDAR and RGB-D cameras is pushing the boundary of the technology to suit the …

To solve the SLAM (simultaneous localization and map building) of mobile robots, a multi-
robot SLAM algorithm based on particle filter for communication in unknown environment is …

An integrated approach to development of unmanned ground vehicle: design, analysis,
implementation and suggestions Page 1 Journal of the Faculty of Engineering and …

We propose an approach for solving Visual Teach and Repeat tasks for routes that consist of
discrete directions along path networks using deep learning. Visual paths are specified by a …

Graph-based simultaneous localization and mapping (G-SLAM) is an intuitive SLAM
implementation where graphs are used to represent poses, landmarks and sensor …

Cartographer is used in LiDAR simultaneous localisation and mapping (SLAM), and it has a
stable mapping results and utilises loop closure detection to eliminate accumulated errors …

To solve these problems of low accuracy of the proposed distribution, large number of
particles required and particle degradation of traditional Rao-Blackwillised particle filter …

Simultaneous localization and mapping (SLAM) is a very important application employed in
many realistic scenarios, where a mobile robot builds a map of the environment while also …

As autonomous driving is rapidly becoming the next major challenge in the automotive
industry, the problem of Simultaneous Localization And Mapping (SLAM) has never been …