Hierarchical 3D LiDAR Localization Improves Robot Positioning: A new AI system helps the robot regain its sense of location in dynamic, ever-changing environments.
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| Title: | Hierarchical 3D LiDAR Localization Improves Robot Positioning: A new AI system helps the robot regain its sense of location in dynamic, ever-changing environments. |
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| Source: | Motion Design. 4/1/2026, p16-17. 2p. |
| Subjects: | LIDAR, Localization problems (Robotics), Deep learning, Mobile robots |
| Abstract: | This article focuses on a hierarchical localization system developed by researchers at Universidad Miguel Hernández de Elche (UMH) in Spain to improve mobile robot positioning in large, dynamic environments. The system, called MCL-DLF (Monte Carlo Localization – Deep Local Feature), combines coarse global localization using 3D LiDAR point clouds with fine localization based on deep learning-extracted local features, enabling robots to estimate their precise position and orientation even after displacement or power loss. Validated over several months in both indoor and outdoor settings, MCL-DLF demonstrates higher accuracy and robustness to environmental changes compared to conventional methods. This advancement supports safer and more reliable autonomous navigation critical for applications such as service robotics, logistics, and environmental monitoring. [Extracted from the article] |
| Copyright of Motion Design is the property of SAE Media and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 193401091 AccessLevel: 6 PubType: Periodical PubTypeId: serialPeriodical PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Hierarchical 3D LiDAR Localization Improves Robot Positioning: A new AI system helps the robot regain its sense of location in dynamic, ever-changing environments. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Motion+Design%22">Motion Design</searchLink>. 4/1/2026, p16-17. 2p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22LIDAR%22">LIDAR</searchLink><br /><searchLink fieldCode="DE" term="%22Localization+problems+%28Robotics%29%22">Localization problems (Robotics)</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+robots%22">Mobile robots</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This article focuses on a hierarchical localization system developed by researchers at Universidad Miguel Hernández de Elche (UMH) in Spain to improve mobile robot positioning in large, dynamic environments. The system, called MCL-DLF (Monte Carlo Localization – Deep Local Feature), combines coarse global localization using 3D LiDAR point clouds with fine localization based on deep learning-extracted local features, enabling robots to estimate their precise position and orientation even after displacement or power loss. Validated over several months in both indoor and outdoor settings, MCL-DLF demonstrates higher accuracy and robustness to environmental changes compared to conventional methods. This advancement supports safer and more reliable autonomous navigation critical for applications such as service robotics, logistics, and environmental monitoring. [Extracted from the article] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Motion Design is the property of SAE Media and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193401091 |
| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 2 StartPage: 16 Subjects: – SubjectFull: LIDAR Type: general – SubjectFull: Localization problems (Robotics) Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Mobile robots Type: general Titles: – TitleFull: Hierarchical 3D LiDAR Localization Improves Robot Positioning: A new AI system helps the robot regain its sense of location in dynamic, ever-changing environments. Type: main BibRelationships: IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: 4/1/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 26420929 Titles: – TitleFull: Motion Design Type: main |
| ResultId | 1 |
