Simultaneous Localization and Mapping (SLAM) for warehouse applications

Abstract

The ravaging impacts of the COVID-19 pandemic on global supply chains and its exposures of the vulnerabilities of the This research paper describes the development and implementation of a practical SLAM model for warehouse application. SLAM technology allows autonomous systems to map unknown environments while estimating their own position. The model combines hardware (mobile robot platform with sensors) and software (SLAM algorithms and real-time data processing) components. The project involved reviewing SLAM algorithms, assembling the hardware, developing software modules, and testing the model's performance. The experiments showed that the model successfully mapped unknown environments and accurately estimated its position in real-time. The project has practical implications for robotics, autonomous vehicles, and augmented reality. Overall, this research contributes to the advancement of SLAM technology and provides insights for further exploration in the field.