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점진적 공간 스무딩을 이용한 그래프 최적성 인식 확률적 LiDAR 번들 조정
대규모 LiDAR 번들 조정(LBA)의 정확성, 효율성 및 확장성을 향상시키기 위해 점진적 공간 스무딩(PSS)과 그래프 최적성 인식 확률적 최적화(GOSO)를 결합한 새로운 방법인 PSS-GOSO를 제안합니다.
Graph Optimality-Aware Stochastic LiDAR Bundle Adjustment with Progressive Spatial Smoothing for Robust, Efficient, and Scalable 3D Mapping
PSS-GOSO is a novel LiDAR Bundle Adjustment (LBA) method that leverages spatial smoothing, graph optimization, and stochastic clustering to achieve robust, efficient, and scalable 3D mapping, especially in large-scale, complex environments.
LiDAR Bundle Adjustment with Progressive Spatial Smoothing for Accurate 3D Modeling
Proposing PSS-BA for accurate point cloud construction in complex environments.
LiDAR Bundle Adjustment with Progressive Spatial Smoothing: PSS-BA Study
提案されたPSS-BAは、複雑な環境でのLiDARデータからの正確なポイントクラウド構築を可能にし、高品質な3Dモデリングを実現します。
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