ArborSplat: 果樹園のためのオンライン意味論的ガウシアンスプラッティングSLAM
ArborSplat: Online Semantic Gaussian Splatting SLAM for Orchards
LiDARオドメトリで追跡しながら3Dガウシアンマップ上で直接意味論を最適化し、果樹園の幹や棚、果実などの細い構造を保つオンライン意味論SLAMを提案。
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著者: Alessandro Masini, Matteo Frosi, Mirko Usuelli, Matteo Matteucci
分類: cs.RO
原文アブストラクト
Orchard robots need maps that preserve small but semantically important structures such as trunks, trellises, and fruit. 3D Gaussian Splatting (3DGS) SLAM achieves high photometric fidelity. However, its optimization remains appearance-driven, and transferring image semantics to 3D points is unreliable for thin structures, whose pixels may receive depth from background surfaces. We present ArborSplat, an online semantic 3DGS SLAM system that tracks with LiDAR odometry and optimizes semantics directly on the Gaussian map, constrained by class-specific height bands above a ground plane fitted to each keyframe's stereo point cloud, and fuses multi-view evidence into a semantic point cloud online while rejecting labels inconsistent with the local ground surface or with monocular depth. Class-constrained refinement reserves Gaussian capacity for underrepresented structures and, under reduced budgets, increases training-view accuracy on tree classes. We evaluate the approach on apple and pear orchards during dormancy, flowering, and harvesting. On full routes, it keeps ATE below 0.5 m on all 12 traversals. On shared 301-frame segments, it exceeds SGS-SLAM and GS3LAM by 0.23 to 0.50 training-view and 0.15 to 0.36 held-out mIoU while running 1.7 to 7.5 times faster, whereas SemGauss-SLAM runs out of GPU memory on all six.