ArborSplat：面向果园的在线语义高斯溅射SLAM
ArborSplat: Online Semantic Gaussian Splatting SLAM for Orchards
- Politecnico di Milano（米兰理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
ArborSplat提出一种在线语义3DGS SLAM系统，利用LiDAR里程计和类别约束优化语义，在果园场景中实现高精度地图构建，优于现有方法。
AI中文摘要：
果园机器人需要保留细小但语义重要的结构（如树干、棚架和果实）的地图。3D高斯溅射（3DGS）SLAM实现了高光度保真度。然而，其优化仍由外观驱动，将图像语义转移到3D点对于细长结构不可靠，因为这些结构的像素可能从背景表面接收深度。我们提出ArborSplat，一种在线语义3DGS SLAM系统，该系统使用LiDAR里程计进行跟踪，并直接在Gaussian地图上优化语义，受限于每个关键帧立体点云拟合的地平面之上的类别特定高度带，同时在线将多视图证据融合到语义点云中，并拒绝与局部地面或单目深度不一致的标签。类别约束细化将Gaussian容量保留给代表性不足的结构，并在预算减少的情况下提高树类别的训练视图精度。我们在休眠期、开花期和收获期的苹果和梨园中评估了该方法。在完整路线上，它在所有12次遍历中保持ATE低于0.5米。在共享的301帧片段上，它比SGS-SLAM和GS3LAM超出0.23至0.50的训练视图mIoU和0.15至0.36的保留mIoU，同时运行速度快1.7至7.5倍，而SemGauss-SLAM在所有六个片段上都耗尽了GPU内存。
英文摘要：
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.
