{"title":"AI-based approaches for improving autonomous mobile robot localization in indoor environments: A comprehensive review","authors":"Shoude Wang , Nur Syazreen Ahmad","doi":"10.1016/j.jestch.2025.101977","DOIUrl":null,"url":null,"abstract":"<div><div>The adoption of indoor autonomous mobile robot (AMR) has surged significantly, driven by their ability to integrate diverse sensors, maintain low operating costs, facilitate easy deployment, and exhibit superior mobility. Nonetheless, navigating complex indoor environments presents substantial challenges that can impede AMR performance and diminish overall system efficiency. To overcome these obstacles, researchers have concentrated on developing autonomous localization techniques that empower AMR to navigate and execute tasks effectively within intricate settings. Recent advancements in artificial intelligence (AI) applications have profoundly influenced this field, enhancing the control and decision-making capabilities of AMR. This paper offers a comprehensive review of AI-based strategies aimed at improving localization of indoor AMR, including aerial vehicles. We systematically categorize and critically analyze existing research on Simultaneous Localization and Mapping (SLAM)-based methods, odometry-based approaches, and multi-sensor fusion techniques, elucidating the principles and implementations of various AI methodologies. Additionally, we discuss real-time performance challenges associated with AI-based approaches and delineate the distinctions between AI-enhanced localization methods and traditional localization techniques, highlighting the necessity and advantages of adopting AI-based solutions. By clarifying these methodologies, our goal is to enhance their comprehension and promote their widespread adoption within the field. Finally, we discuss emerging research directions and ongoing challenges, providing guidance for future investigations and advancements in the domain of indoor AMR.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"63 ","pages":"Article 101977"},"PeriodicalIF":5.1000,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Science and Technology-An International Journal-Jestech","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2215098625000321","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
The adoption of indoor autonomous mobile robot (AMR) has surged significantly, driven by their ability to integrate diverse sensors, maintain low operating costs, facilitate easy deployment, and exhibit superior mobility. Nonetheless, navigating complex indoor environments presents substantial challenges that can impede AMR performance and diminish overall system efficiency. To overcome these obstacles, researchers have concentrated on developing autonomous localization techniques that empower AMR to navigate and execute tasks effectively within intricate settings. Recent advancements in artificial intelligence (AI) applications have profoundly influenced this field, enhancing the control and decision-making capabilities of AMR. This paper offers a comprehensive review of AI-based strategies aimed at improving localization of indoor AMR, including aerial vehicles. We systematically categorize and critically analyze existing research on Simultaneous Localization and Mapping (SLAM)-based methods, odometry-based approaches, and multi-sensor fusion techniques, elucidating the principles and implementations of various AI methodologies. Additionally, we discuss real-time performance challenges associated with AI-based approaches and delineate the distinctions between AI-enhanced localization methods and traditional localization techniques, highlighting the necessity and advantages of adopting AI-based solutions. By clarifying these methodologies, our goal is to enhance their comprehension and promote their widespread adoption within the field. Finally, we discuss emerging research directions and ongoing challenges, providing guidance for future investigations and advancements in the domain of indoor AMR.
期刊介绍:
Engineering Science and Technology, an International Journal (JESTECH) (formerly Technology), a peer-reviewed quarterly engineering journal, publishes both theoretical and experimental high quality papers of permanent interest, not previously published in journals, in the field of engineering and applied science which aims to promote the theory and practice of technology and engineering. In addition to peer-reviewed original research papers, the Editorial Board welcomes original research reports, state-of-the-art reviews and communications in the broadly defined field of engineering science and technology.
The scope of JESTECH includes a wide spectrum of subjects including:
-Electrical/Electronics and Computer Engineering (Biomedical Engineering and Instrumentation; Coding, Cryptography, and Information Protection; Communications, Networks, Mobile Computing and Distributed Systems; Compilers and Operating Systems; Computer Architecture, Parallel Processing, and Dependability; Computer Vision and Robotics; Control Theory; Electromagnetic Waves, Microwave Techniques and Antennas; Embedded Systems; Integrated Circuits, VLSI Design, Testing, and CAD; Microelectromechanical Systems; Microelectronics, and Electronic Devices and Circuits; Power, Energy and Energy Conversion Systems; Signal, Image, and Speech Processing)
-Mechanical and Civil Engineering (Automotive Technologies; Biomechanics; Construction Materials; Design and Manufacturing; Dynamics and Control; Energy Generation, Utilization, Conversion, and Storage; Fluid Mechanics and Hydraulics; Heat and Mass Transfer; Micro-Nano Sciences; Renewable and Sustainable Energy Technologies; Robotics and Mechatronics; Solid Mechanics and Structure; Thermal Sciences)
-Metallurgical and Materials Engineering (Advanced Materials Science; Biomaterials; Ceramic and Inorgnanic Materials; Electronic-Magnetic Materials; Energy and Environment; Materials Characterizastion; Metallurgy; Polymers and Nanocomposites)