Promising SLAM Methods for Automated Guided Vehicles and Autonomous Mobile Robots

authored by: Li Li, Kunal Kalavadia, Lothar Schulze
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.
Organisation(s): Institute of Production Systems and Logistics
External Organisation(s): Ostwestfalen-Lippe University of Applied Sciences
Type: Conference article
Journal: Procedia Computer Science
Volume: 232
Pages: 2867-2874
No. of pages: 8
Publication date: 2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: General Computer Science
Electronic version(s): https://doi.org/10.1016/j.procs.2024.02.103 (Access: Open)

BibTeX

@article{889275ed5f6a4d38b5ce92e0250c5d47,
title = "Promising SLAM Methods for Automated Guided Vehicles and Autonomous Mobile Robots",
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.",
keywords = "Automated Guided Vehicle, Autonomous Mobile Robot, Robot Operating System, Simultaneous Localization and Mapping",
author = "Li Li and Kunal Kalavadia and Lothar Schulze",
year = "2024",
doi = "10.1016/j.procs.2024.02.103",
language = "English",
volume = "232",
pages = "2867--2874",
journal = "Procedia Computer Science",
issn = "1877-0509",
publisher = "Elsevier BV",
note = "5th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2023 ; Conference date: 22-11-2023 Through 24-11-2023",
}