{"title":"增强现实 SLAM 算法综述","authors":"","doi":"10.1016/j.displa.2024.102806","DOIUrl":null,"url":null,"abstract":"<div><p>Augmented Reality (AR) has gained significant attention in recent years as a technology that enhances the user’s perception and interaction with the real world by overlaying virtual objects. Simultaneous Localization and Mapping (SLAM) algorithm plays a crucial role in enabling AR applications by allowing the device to understand its position and orientation in the real world while mapping the environment. This paper first summarizes AR products and SLAM algorithms in recent years, and presents a comprehensive overview of SLAM algorithms including feature-based method, direct method, and deep learning-based method, highlighting their advantages and limitations. Then provides an in-depth exploration of classical SLAM algorithms for AR, with a focus on visual SLAM and visual-inertial SLAM. Lastly, sensor configuration, datasets, and performance evaluation for AR SLAM are also discussed. The review concludes with a summary of the current state of SLAM algorithms for AR and provides insights into future directions for research and development in this field. Overall, this review serves as a valuable resource for researchers and engineers who are interested in understanding the advancements and challenges in SLAM algorithms for AR.</p></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Review on SLAM algorithms for Augmented Reality\",\"authors\":\"\",\"doi\":\"10.1016/j.displa.2024.102806\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Augmented Reality (AR) has gained significant attention in recent years as a technology that enhances the user’s perception and interaction with the real world by overlaying virtual objects. Simultaneous Localization and Mapping (SLAM) algorithm plays a crucial role in enabling AR applications by allowing the device to understand its position and orientation in the real world while mapping the environment. This paper first summarizes AR products and SLAM algorithms in recent years, and presents a comprehensive overview of SLAM algorithms including feature-based method, direct method, and deep learning-based method, highlighting their advantages and limitations. Then provides an in-depth exploration of classical SLAM algorithms for AR, with a focus on visual SLAM and visual-inertial SLAM. Lastly, sensor configuration, datasets, and performance evaluation for AR SLAM are also discussed. The review concludes with a summary of the current state of SLAM algorithms for AR and provides insights into future directions for research and development in this field. Overall, this review serves as a valuable resource for researchers and engineers who are interested in understanding the advancements and challenges in SLAM algorithms for AR.</p></div>\",\"PeriodicalId\":50570,\"journal\":{\"name\":\"Displays\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Displays\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0141938224001707\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Displays","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0141938224001707","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
摘要
增强现实(AR)技术通过叠加虚拟对象来增强用户对现实世界的感知和互动,近年来受到了广泛关注。同时定位和映射(SLAM)算法允许设备在映射环境的同时了解自己在现实世界中的位置和方向,在实现 AR 应用方面发挥着至关重要的作用。本文首先总结了近年来的 AR 产品和 SLAM 算法,并全面介绍了 SLAM 算法,包括基于特征的方法、直接方法和基于深度学习的方法,强调了它们的优势和局限性。然后深入探讨了 AR 的经典 SLAM 算法,重点介绍了视觉 SLAM 和视觉-惯性 SLAM。最后,还讨论了 AR SLAM 的传感器配置、数据集和性能评估。综述最后总结了 AR SLAM 算法的现状,并对该领域未来的研发方向提出了见解。总之,对于有兴趣了解 AR SLAM 算法的进展和挑战的研究人员和工程师来说,本综述是一份宝贵的资料。
Augmented Reality (AR) has gained significant attention in recent years as a technology that enhances the user’s perception and interaction with the real world by overlaying virtual objects. Simultaneous Localization and Mapping (SLAM) algorithm plays a crucial role in enabling AR applications by allowing the device to understand its position and orientation in the real world while mapping the environment. This paper first summarizes AR products and SLAM algorithms in recent years, and presents a comprehensive overview of SLAM algorithms including feature-based method, direct method, and deep learning-based method, highlighting their advantages and limitations. Then provides an in-depth exploration of classical SLAM algorithms for AR, with a focus on visual SLAM and visual-inertial SLAM. Lastly, sensor configuration, datasets, and performance evaluation for AR SLAM are also discussed. The review concludes with a summary of the current state of SLAM algorithms for AR and provides insights into future directions for research and development in this field. Overall, this review serves as a valuable resource for researchers and engineers who are interested in understanding the advancements and challenges in SLAM algorithms for AR.
期刊介绍:
Displays is the international journal covering the research and development of display technology, its effective presentation and perception of information, and applications and systems including display-human interface.
Technical papers on practical developments in Displays technology provide an effective channel to promote greater understanding and cross-fertilization across the diverse disciplines of the Displays community. Original research papers solving ergonomics issues at the display-human interface advance effective presentation of information. Tutorial papers covering fundamentals intended for display technologies and human factor engineers new to the field will also occasionally featured.