@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}},
}

@inproceedings{559,
  abstract     = {{The navigation technique has been always an important issue for guiding an automated guided vehicle (AGV). With the development of sensor technology, software engineering, andalgorithms, there is a spectrum of different navigation methods for AGVs. Inorder to avoid the additional environmental installation,so as to increase the flexibility of route planning, but to keep the positioning precision, more sensors, such as light detection and ranging (LIDAR), wheel encoders and gyroscope are installed on the vehicles to be automated. Some intelligent algorithms such as simultaneous localization and mapping (SLAM) algorithms and Monte Carlo localization have been developed for the navigation of vehicles, including position and orientation. The interesting question, especially for the AGVmanufacturers, is: which algorithm is more suitable for which kind of applications. The suitabilityof an algorithm for the navigation of AGVs with facilities of Light Detection and Ranging(LIDAR), encoders and gyroscope ismainly determined by four properties. They are the positioning precision, computational costs, execution time and positioning repeatability. This paper intends to investigate the suitability of an algorithm or a navigation method for AGVs with LIDAR, wheel encoders,and gyroscope. The two aspects of positioningaccuracy and repeatability are especially concerned. A general comparison of different navigation methods and algorithms is given. An experimental platform with a basic vehicle, controlling system and sensors is then developed to further evaluate the algorithms. The hardware components and software components are compatible to robot operating system (ROS). This open-source robotics middleware provides services and tools for creating robot applications. As ROS SLAM nodes, open-source SLAM algorithms can be evaluated relatively easily without any rewriting or modification of the algorithms. As a new research field, there is not jet a SLAM algorithm, which is predominant absolutely.}},
  author       = {{Li, Li and Schulze, L.}},
  booktitle    = {{Production Engineering and Management}},
  editor       = {{Villmer, Franz-Josef and Padoano, Elio}},
  isbn         = {{978-3-946856-03-0}},
  keywords     = {{Automatedguided vehicle, Simultaneous localization and mapping, Robot operating system, Light detection and ranging}},
  location     = {{Lemgo}},
  number       = {{1}},
  pages        = {{213--223}},
  title        = {{{Comparison and Evaluation of SLAM Algorithms for AGV Navigation}}},
  year         = {{2018}},
}