Basheer Al-Tawil, Thorsten Hempel, Ahmed A. Abdelrahman, A. Al-Hamadi
{"title":"机器人视觉 SLAM 综述:演变、特性和未来应用","authors":"Basheer Al-Tawil, Thorsten Hempel, Ahmed A. Abdelrahman, A. Al-Hamadi","doi":"10.3389/frobt.2024.1347985","DOIUrl":null,"url":null,"abstract":"Visual simultaneous localization and mapping (V-SLAM) plays a crucial role in the field of robotic systems, especially for interactive and collaborative mobile robots. The growing reliance on robotics has increased complexity in task execution in real-world applications. Consequently, several types of V-SLAM methods have been revealed to facilitate and streamline the functions of robots. This work aims to showcase the latest V-SLAM methodologies, offering clear selection criteria for researchers and developers to choose the right approach for their robotic applications. It chronologically presents the evolution of SLAM methods, highlighting key principles and providing comparative analyses between them. The paper focuses on the integration of the robotic ecosystem with a robot operating system (ROS) as Middleware, explores essential V-SLAM benchmark datasets, and presents demonstrative figures for each method’s workflow.","PeriodicalId":504612,"journal":{"name":"Frontiers in Robotics and AI","volume":"2 4","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A review of visual SLAM for robotics: evolution, properties, and future applications\",\"authors\":\"Basheer Al-Tawil, Thorsten Hempel, Ahmed A. Abdelrahman, A. Al-Hamadi\",\"doi\":\"10.3389/frobt.2024.1347985\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Visual simultaneous localization and mapping (V-SLAM) plays a crucial role in the field of robotic systems, especially for interactive and collaborative mobile robots. The growing reliance on robotics has increased complexity in task execution in real-world applications. Consequently, several types of V-SLAM methods have been revealed to facilitate and streamline the functions of robots. This work aims to showcase the latest V-SLAM methodologies, offering clear selection criteria for researchers and developers to choose the right approach for their robotic applications. It chronologically presents the evolution of SLAM methods, highlighting key principles and providing comparative analyses between them. The paper focuses on the integration of the robotic ecosystem with a robot operating system (ROS) as Middleware, explores essential V-SLAM benchmark datasets, and presents demonstrative figures for each method’s workflow.\",\"PeriodicalId\":504612,\"journal\":{\"name\":\"Frontiers in Robotics and AI\",\"volume\":\"2 4\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Robotics and AI\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/frobt.2024.1347985\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Robotics and AI","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/frobt.2024.1347985","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
视觉同步定位与映射(V-SLAM)在机器人系统领域,尤其是交互式协作移动机器人领域发挥着至关重要的作用。在现实世界的应用中,对机器人技术的依赖与日俱增,增加了任务执行的复杂性。因此,已经出现了多种 V-SLAM 方法来促进和简化机器人的功能。这项工作旨在展示最新的 V-SLAM 方法,为研究人员和开发人员提供明确的选择标准,以便为他们的机器人应用选择正确的方法。它按时间顺序介绍了 SLAM 方法的演变,突出了关键原则,并提供了它们之间的比较分析。论文重点介绍了作为中间件的机器人操作系统(ROS)与机器人生态系统的整合,探讨了重要的 V-SLAM 基准数据集,并展示了每种方法工作流程的演示图。
A review of visual SLAM for robotics: evolution, properties, and future applications
Visual simultaneous localization and mapping (V-SLAM) plays a crucial role in the field of robotic systems, especially for interactive and collaborative mobile robots. The growing reliance on robotics has increased complexity in task execution in real-world applications. Consequently, several types of V-SLAM methods have been revealed to facilitate and streamline the functions of robots. This work aims to showcase the latest V-SLAM methodologies, offering clear selection criteria for researchers and developers to choose the right approach for their robotic applications. It chronologically presents the evolution of SLAM methods, highlighting key principles and providing comparative analyses between them. The paper focuses on the integration of the robotic ecosystem with a robot operating system (ROS) as Middleware, explores essential V-SLAM benchmark datasets, and presents demonstrative figures for each method’s workflow.