AI-based approaches for improving autonomous mobile robot localization in indoor environments: A comprehensive review
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 chall...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Engineering Science and Technology, an International Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098625000321 |
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| author | Shoude Wang Nur Syazreen Ahmad |
| author_facet | Shoude Wang Nur Syazreen Ahmad |
| author_sort | Shoude Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-94abe3e31a634abd891af910294cc270 |
| institution | Kabale University |
| issn | 2215-0986 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering Science and Technology, an International Journal |
| spelling | doaj-art-94abe3e31a634abd891af910294cc2702025-02-12T05:31:11ZengElsevierEngineering Science and Technology, an International Journal2215-09862025-03-0163101977AI-based approaches for improving autonomous mobile robot localization in indoor environments: A comprehensive reviewShoude Wang0Nur Syazreen Ahmad1School of Sergeancy, Weifang University of Science and Technology, 1299 Jinguang Street, Shouguang, China; School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang 14300, MalaysiaSchool of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang 14300, Malaysia; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2215098625000321Simultaneous localization and mapping (SLAM)Autonomous mobile robots (AMR)Unmanned aerial vehicle (UAV)Artificial intelligence (AI)AI-based localizationIndoor localization |
| spellingShingle | Shoude Wang Nur Syazreen Ahmad AI-based approaches for improving autonomous mobile robot localization in indoor environments: A comprehensive review Engineering Science and Technology, an International Journal Simultaneous localization and mapping (SLAM) Autonomous mobile robots (AMR) Unmanned aerial vehicle (UAV) Artificial intelligence (AI) AI-based localization Indoor localization |
| title | AI-based approaches for improving autonomous mobile robot localization in indoor environments: A comprehensive review |
| title_full | AI-based approaches for improving autonomous mobile robot localization in indoor environments: A comprehensive review |
| title_fullStr | AI-based approaches for improving autonomous mobile robot localization in indoor environments: A comprehensive review |
| title_full_unstemmed | AI-based approaches for improving autonomous mobile robot localization in indoor environments: A comprehensive review |
| title_short | AI-based approaches for improving autonomous mobile robot localization in indoor environments: A comprehensive review |
| title_sort | ai based approaches for improving autonomous mobile robot localization in indoor environments a comprehensive review |
| topic | Simultaneous localization and mapping (SLAM) Autonomous mobile robots (AMR) Unmanned aerial vehicle (UAV) Artificial intelligence (AI) AI-based localization Indoor localization |
| url | http://www.sciencedirect.com/science/article/pii/S2215098625000321 |
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