@misc{12813,
  abstract     = {{Autonomous Mobile Robots, as the advanced version of Automated Guided Vehicles have received a lot of interest and recognition in recent years. Simultaneous Localization and Mapping (SLAM) techniques enable the vehicles to independently navigate and map their surroundings so that they can drive autonomously in changing and uncharted areas. Due to the increasing importance and contributive development of SLAMs for automated guided vehicles and autonomous mobile robots, this study seeks to provide an in-depth analysis of well-known SLAM techniques developed and applied during the previous ten years. Well-known SLAM algorithms considered in this paper include GMapping, Cartographer, LIO-SAM, and so on. They are mainly examined and compared from the viewpoints of basic principles, sensor requirements, computing complexity, and performance. The aim of this paper is to offer insights into various SLAM approaches to researchers, practitioners, and developers in the field of automated guided vehicles and autonomous mobile robots, facilitating the selection of suitable SLAM methods for specific applications and fostering innovation in autonomous navigation and mapping.}},
  author       = {{Li, Li and Schulze, Lothar and Kalavadia, Kunal Satish}},
  booktitle    = {{5th International Conference on Industry 4.0 and Smart Manufacturing (ISM)}},
  editor       = {{Longo, F. and Shen, W. and Padovano, A.}},
  issn         = {{1877-0509}},
  keywords     = {{Automated Guided Vehicle, Autonomous Mobile Robot, Simultaneous Localization and Mapping, Robot Operating System}},
  location     = {{Lisbon, PORTUGAL}},
  pages        = {{2867--2874}},
  publisher    = {{Elsevier BV}},
  title        = {{{Promising SLAM Methods for Automated Guided Vehicles and Autonomous Mobile Robots}}},
  doi          = {{10.1016/j.procs.2024.02.103}},
  volume       = {{232}},
  year         = {{2024}},
}