Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 2867-2874 |
| Seitenumfang | 8 |
| Fachzeitschrift | Procedia Computer Science |
| Jahrgang | 232 |
| Frühes Online-Datum | 20 März 2024 |
| Publikationsstatus | Veröffentlicht - 2024 |
| Veranstaltung | 5th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2023 - Lisbon, Portugal Dauer: 22 Nov. 2023 → 24 Nov. 2023 |
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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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in: Procedia Computer Science, Jahrgang 232, 2024, S. 2867-2874.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Promising SLAM Methods for Automated Guided Vehicles and Autonomous Mobile Robots
AU - Li, Li
AU - Kalavadia, Kunal
AU - Schulze, Lothar
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Automated Guided Vehicle
KW - Autonomous Mobile Robot
KW - Robot Operating System
KW - Simultaneous Localization and Mapping
UR - http://www.scopus.com/inward/record.url?scp=85189788789&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2024.02.103
DO - 10.1016/j.procs.2024.02.103
M3 - Conference article
AN - SCOPUS:85189788789
VL - 232
SP - 2867
EP - 2874
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 5th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2023
Y2 - 22 November 2023 through 24 November 2023
ER -