Details
| Original language | English |
|---|---|
| Pages (from-to) | 2867-2874 |
| Number of pages | 8 |
| Journal | Procedia Computer Science |
| Volume | 232 |
| Early online date | 20 Mar 2024 |
| Publication status | Published - 2024 |
| Event | 5th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2023 - Lisbon, Portugal Duration: 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.
Keywords
- Automated Guided Vehicle, Autonomous Mobile Robot, Robot Operating System, Simultaneous Localization and Mapping
ASJC Scopus subject areas
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Procedia Computer Science, Vol. 232, 2024, p. 2867-2874.
Research output: Contribution to journal › Conference article › Research › 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 -