Academic Journal
DB-LIO: Database-Driven LiDAR–Inertial Odometry for Memory-Bounded Persistent Mapping.
| Τίτλος: | DB-LIO: Database-Driven LiDAR–Inertial Odometry for Memory-Bounded Persistent Mapping. |
|---|---|
| Συγγραφείς: | Kim, Hun-Hee1,2 (AUTHOR), Kang, Ho-Hyun1,2 (AUTHOR), Noh, Dong-Hee1 (AUTHOR), Lee, Hea-Min1,2 (AUTHOR) lee10849@keti.re.kr |
| Πηγή: | Sensors (14248220). May2026, Vol. 26 Issue 10, p3061. 29p. |
| Θεματικοί όροι: | *SLAM (Robotics), *Computer memory management, *Optimization algorithms, *Data management |
| Περίληψη: | This paper proposes DB-LIO (database-driven LiDAR-inertial odometry), a simultaneous localization and mapping (SLAM) system that addresses memory scalability challenges in extended autonomous operation. Existing LiDAR-SLAM systems accumulate keyframe history in memory, leading to O (N) growth and out-of-memory failures during extended operation. To overcome this limitation, DB-LIO introduces three core design elements. First, it proposes a spatially indexed keyframe management scheme that persistently stores keyframes in SQLite with R-Tree spatial indexing, enabling O (log N + k) spatial queries that tightly couple cache eviction with factor-graph optimization requirements—a design that ensures every keyframe potentially involved in the next optimization cycle resides in cache. Second, it presents a four-level memory bounding architecture—SLAM-engine keyframe trimming with transparent on-demand reloading, a DB-level least recently used (LRU) cache with a spatial active window, Scan Context descriptor pool bounding, and iSAM2 sliding window compaction with a sparse global anchor graph—that collectively bounds the dominant memory consumers to O (C) . Third, the DB-based persistent storage enables a localization mode that can reload previously built maps—including full point clouds, six-degree-of-freedom poses, timestamps, and inter-keyframe relationships—and perform pose estimation using the stored map, which is particularly valuable for agricultural robots and other autonomous systems requiring map reuse. Experiments on a custom orchard dataset demonstrate an 81.9% reduction in memory usage compared with that of the in-memory baseline (2888 MB → 524 MB), while preserving equivalent trajectory accuracy (absolute trajectory error (ATE) root mean square error (RMSE) 0.305 ± 0.001 m vs. 0.296 m). Validation on the KITTI odometry benchmark confirms that the proposed localization mode generalizes across different LiDAR types (Livox Mid360, Velodyne HDL-64E) and environments (orchard, urban driving). [ABSTRACT FROM AUTHOR] |
| Βάση Δεδομένων: | Academic Search Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:asx&genre=article&issn=14248220&ISBN=&volume=26&issue=10&date=20260515&spage=3061&pages=3061-3089&title=Sensors (14248220)&atitle=DB-LIO%3A%20Database-Driven%20LiDAR%E2%80%93Inertial%20Odometry%20for%20Memory-Bounded%20Persistent%20Mapping.&aulast=Kim%2C%20Hun-Hee&id=DOI:10.3390/s26103061 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: DB-LIO: Database-Driven LiDAR–Inertial Odometry for Memory-Bounded Persistent Mapping. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kim%2C+Hun-Hee%22">Kim, Hun-Hee</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kang%2C+Ho-Hyun%22">Kang, Ho-Hyun</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Noh%2C+Dong-Hee%22">Noh, Dong-Hee</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lee%2C+Hea-Min%22">Lee, Hea-Min</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> lee10849@keti.re.kr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Sensors+%2814248220%29%22">Sensors (14248220)</searchLink>. May2026, Vol. 26 Issue 10, p3061. 29p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22SLAM+%28Robotics%29%22">SLAM (Robotics)</searchLink><br />*<searchLink fieldCode="DE" term="%22Computer+memory+management%22">Computer memory management</searchLink><br />*<searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+management%22">Data management</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper proposes DB-LIO (database-driven LiDAR-inertial odometry), a simultaneous localization and mapping (SLAM) system that addresses memory scalability challenges in extended autonomous operation. Existing LiDAR-SLAM systems accumulate keyframe history in memory, leading to O (N) growth and out-of-memory failures during extended operation. To overcome this limitation, DB-LIO introduces three core design elements. First, it proposes a spatially indexed keyframe management scheme that persistently stores keyframes in SQLite with R-Tree spatial indexing, enabling O (log N + k) spatial queries that tightly couple cache eviction with factor-graph optimization requirements—a design that ensures every keyframe potentially involved in the next optimization cycle resides in cache. Second, it presents a four-level memory bounding architecture—SLAM-engine keyframe trimming with transparent on-demand reloading, a DB-level least recently used (LRU) cache with a spatial active window, Scan Context descriptor pool bounding, and iSAM2 sliding window compaction with a sparse global anchor graph—that collectively bounds the dominant memory consumers to O (C) . Third, the DB-based persistent storage enables a localization mode that can reload previously built maps—including full point clouds, six-degree-of-freedom poses, timestamps, and inter-keyframe relationships—and perform pose estimation using the stored map, which is particularly valuable for agricultural robots and other autonomous systems requiring map reuse. Experiments on a custom orchard dataset demonstrate an 81.9% reduction in memory usage compared with that of the in-memory baseline (2888 MB → 524 MB), while preserving equivalent trajectory accuracy (absolute trajectory error (ATE) root mean square error (RMSE) 0.305 ± 0.001 m vs. 0.296 m). Validation on the KITTI odometry benchmark confirms that the proposed localization mode generalizes across different LiDAR types (Livox Mid360, Velodyne HDL-64E) and environments (orchard, urban driving). [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asx&AN=194140164 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/s26103061 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 3061 Subjects: – SubjectFull: SLAM (Robotics) Type: general – SubjectFull: Computer memory management Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Data management Type: general Titles: – TitleFull: DB-LIO: Database-Driven LiDAR–Inertial Odometry for Memory-Bounded Persistent Mapping. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kim, Hun-Hee – PersonEntity: Name: NameFull: Kang, Ho-Hyun – PersonEntity: Name: NameFull: Noh, Dong-Hee – PersonEntity: Name: NameFull: Lee, Hea-Min IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 14248220 Numbering: – Type: volume Value: 26 – Type: issue Value: 10 Titles: – TitleFull: Sensors (14248220) Type: main |
| ResultId | 1 |