Review: issues and challenges of simultaneous localization and mapping (SLAM) technology in autonomous robot.

Abstract

To aid in robot navigation and environment analysis, visual SLAM systems process visual data. Things like AMRs (autonomous mobile robots) and AGVs (autonomous guided vehicles) have been gaining popularity in recent years. These robots depend significantly on simultaneous localization and mapping (SLAM) technology to keep the factory floor free of accidents. vSLAM employs a technique for estimating the precise positioning and orientation of a sensor relative to its environment as well as the navigation of the region around it. SLAM algorithms can be used in various applications, including self-driving vehicles, mobile robots, drones, etc. Visual SLAM does not refer to a particular set of methods or software. This paper proposes to review some of the issues and challenges facing SLAM technology in autonomous robot applications and draw a conclusion.