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. |
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| 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 |
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