Localization and Mapping Algorithm for the Indoor Mobile Robot Based on LIDAR

Xiang, Yong; Yang, Xiuqing; Yang, Weijie; Miao, Weng

doi:10.1088/1757-899x/831/1/012021

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…In recent years, many improvements have been made based on these SLAM methods. Xiang Y. et al [ 55 ] designed a LIDAR-based localization and mapping method for indoor mobile robots. The method makes improvements in distribution and limiting the number of resamplings based on the original RBPF-SLAM algorithm.…”

Section: Overview Of Single Sensor Sensing Technologiesmentioning

confidence: 99%

A Review of Sensing Technologies for Indoor Autonomous Mobile Robots

Liu,

Wang,

Xie

et al. 2024

Sensors

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As a fundamental issue in robotics academia and industry, indoor autonomous mobile robots (AMRs) have been extensively studied. For AMRs, it is crucial to obtain information about their working environment and themselves, which can be realized through sensors and the extraction of corresponding information from the measurements of these sensors. The application of sensing technologies can enable mobile robots to perform localization, mapping, target or obstacle recognition, and motion tasks, etc. This paper reviews sensing technologies for autonomous mobile robots in indoor scenes. The benefits and potential problems of using a single sensor in application are analyzed and compared, and the basic principles and popular algorithms used in processing these sensor data are introduced. In addition, some mainstream technologies of multi-sensor fusion are introduced. Finally, this paper discusses the future development trends in the sensing technology for autonomous mobile robots in indoor scenes, as well as the challenges in the practical application environments.

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Section: Overview Of Single Sensor Sensing Technologiesmentioning

confidence: 99%

A Review of Sensing Technologies for Indoor Autonomous Mobile Robots

Liu,

Wang,

Xie

et al. 2024

Sensors

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“…Ali and Payton (2021), on the other hand, explored task-based continuous authentication models by using real-world wrist-worn sensor data. To realize autonomous localization and navigation of indoor robots, Xiang et al (2020) designed an indoor mobile robot positioning and mapping system. Mobile technology has also been used to address the challenges of language proficiency in language teaching (Chen & Chih-Cheng, 2018;Fang et al, 2021;Tragant et al, 2022).…”

Section: Mobile-assisted Tased-based Learningmentioning

confidence: 99%

Systematic Review of Mobile-Assisted Task-Based Learning Based on WoS (2013-2022)

Jia

Harji

2023

InSITE Conference

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Aim/Purpose. The aim of this paper is to present a systematic review of studies conducted on mobile-assisted task-based learning between 2013 to 2022. The primary objectives of the review are to elicit educational and learning contexts, research areas and foci, research trends, methodologies, data collection techniques, mobile technology used, learning outcomes, and issues in mobile-assisted task-based learning. Background. Conventional task-based learning has evolved over the last decade with numerous researchers incorporating various mobile technologies and devices to enhance the task-based approach. This paper presents a systematic review of task-based learning, specifically with the adoption of the mobile-assisted method in delivering tasks. Methodology. To provide viable discussions and maintain the accuracy of the review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol items were adhered to. The papers for review were sourced from ISI Web of Science within the last decade from 2013 to 2022. The outcome of screening based on the inclusive and exclusive criteria as well as from the backward search resulted in 16 papers out of 276 papers that were eventually reviewed and analyzed. Contribution. This study is of significant value, especially during the post-pandemic period, as mobile task-based learning provides for flexibility and portability, and to date, no systematic reviews have been conducted specifically on mobile-assisted task-based learning in the last decade. This review looked at the study characteristics, educational and learning contexts, research areas and research foci, research trends, methodologies, data collection techniques, mobile technology used, learning outcomes, and issues in the papers on mobile-assisted task-based learning. In addition to attributing to the existing body of knowledge, this paper provides insights for researchers, educators, as well as mobile technology developers for consideration. Findings. Major findings are that all papers are on language learning, with English as a foreign language being the most frequently researched area, and most of the studies are conducted in higher education contexts. Informal learning has become a critical learning setting, and language proficiency and participant perceptions are the two major foci. In addition, no studies have been found to combine mobile learning and social media platforms together to promote language acquisition. Recommendations for Practitioners. This systematic review provides practitioners with an overview of mobile technologies that have been found to be effective in the task-based approach for different educational levels and language skills. These effective mobile technologies could be considered for adoption in their respective contexts. Recommendations for Researchers. For researchers, the time frame for a systematic review could be expanded to increase the number of papers for review. In addition, to ensure the comprehensiveness of findings, big data analysis software could be employed to analyze the research foci, research trends, and research shifts. Additional databases could be added for more extensive review. Impact on Society. The research findings of this paper can provide benefits for researchers, educators, and mobile technology developers for them to collaboratively promote the development of mobile-assisted task-based learning. Future Research. Future researchers could conduct in-depth empirical studies on mobile seamless learning, research with tasks that are designed based on learners’ needs analysis and explore cognitive load as well as learning anxiety which are not extensively researched.

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“…Ali and Payton (2021), on the other hand, explored task-based continuous authentication models by using real-world wrist-worn sensor data. To realize autonomous localization and navigation of indoor robots, Xiang et al (2020) designed an indoor mobile robot positioning and mapping system. Mobile technology has also been used to address the challenges of language proficiency in language teaching (T. H. Chen & Lin, 2018;Fang et al, 2021;Tragant et al, 2021).…”

Section: Mobile-assisted Task-based Learningmentioning

confidence: 99%

Systematic Review of Mobile-Assisted Task-Based Learning Based on WoS (2013-2022)

Jia

Harji

2022

JITE:Research

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Aim/Purpose: The aim of this paper is to present a systematic review of studies conducted on mobile-assisted task-based learning between 2013 and 2022. The primary objectives of the review are to elicit educational and learning contexts, research areas and foci, research trends, methodologies, data collection techniques, mobile technology used, learning outcomes, and issues in mobile-assisted task-based learning. Background: Conventional task-based learning has evolved over the last decade with numerous researchers incorporating various mobile technologies and devices to enhance the task-based approach. This paper presents a systematic review of task-based learning, specifically with the adoption of the mobile-assisted method in delivering tasks. Methodology: To provide viable discussions and maintain the accuracy of the review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (Liberati et al., 2009) protocol items were adhered to. The papers for review were sourced from ISI Web of Science within the last decade from 2013 to 2022. The outcome of screening based on the inclusive and exclusive criteria as well as from the backward search resulted in 16 papers out of 276 papers that were eventually reviewed and analyzed. Contribution: This study is of significant value, especially during the post-pandemic period, as mobile task-based learning provides for flexibility and portability, and to date, no systematic reviews have been conducted specifically on mobile-assisted task-based learning in the last decade. This review looked at the study characteristics, educational and learning contexts, research areas and research foci, research trends, methodologies, data collection techniques, mobile technology used, learning outcomes, and issues in the papers on mobile-assisted task-based learning. In addition to attributing to the existing body of knowledge, this paper provides insights for researchers, educators, as well as mobile technology developers for consideration. Findings: Major findings are that all papers are on language learning, with English as a foreign language being the most frequently researched area, and most of the studies are conducted in higher education contexts. Informal learning has become a critical learning setting, and language proficiency and participant perceptions are the two major foci. In addition, no studies have been found to combine mobile learning and social media platforms together to promote language acquisition. Recommendations for Practitioners: This systematic review provides practitioners with an overview of mobile technologies that have been found to be effective in the task-based approach for different educational levels and language skills. These effective mobile technologies could be considered for adoption in their respective contexts. Recommendation for Researchers: For researchers, the time frame for a systematic review could be expanded to increase the number of papers for review. In addition, to ensure the comprehensiveness of findings, big data analysis software could be employed to analyze the research foci, research trends, and research shifts. Additional databases could be added for more extensive review. Impact on Society: The research findings of this paper can provide benefits for researchers, educators, and mobile technology developers for them to collaboratively promote the development of mobile-assisted task-based learning. Future Research: Future researchers could conduct in-depth empirical studies on mobile seamless learning, research with tasks that are designed based on learners’ needs analysis and explore cognitive load as well as learning anxiety which are not extensively researched.

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Localization and Mapping Algorithm for the Indoor Mobile Robot Based on LIDAR

Cited by 7 publications

References 10 publications

A Review of Sensing Technologies for Indoor Autonomous Mobile Robots

A Review of Sensing Technologies for Indoor Autonomous Mobile Robots

Systematic Review of Mobile-Assisted Task-Based Learning Based on WoS (2013-2022)

Systematic Review of Mobile-Assisted Task-Based Learning Based on WoS (2013-2022)

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