Integração de sensor LiDAR 2D para mapeamento e localização de veículo AGV.

Abstract

AMRs di er from AGVs due to their autonomous navigation capabilities and adaptability to environments without xed tracks. Autonomous navigation requires precise perception, localization, and mapping systems. In the context of LiDAR-based SLAM, the Iterative Closest Point (ICP) stands out as one of the primary algorithms used for re ning localization and point cloud alignment. This study proposes a SLAM and ICP-based solution for navigation exclusively utilizing a 2D LiDAR sensor, without additional sensor support, under computational constraints. The solution was experimentally implemented on an AGV vehicle in a laboratory, enabling its reclassi cation from an AGV to an AMR. The main contributions include: a comparative study of ICP methods (classical, Gauss-Newton pointto- point, and point-to-plane) using real data; incremental implementation of ICP, allowing continuous alignment of multiple scans; adaptation of the system for real-time operation; and the development of an interactive interface to visualize the trajectory and generated map. The proposed ICP technique demonstrated superior performance and was experimentally validated, highlighting its potential for extension to other real-time SLAM applications involving vehicles.