AgriGS-SLAM：基于多视图高斯泼溅SLAM的跨季节果园建图
AgriGS-SLAM: Orchard Mapping Across Seasons via Multi-View Gaussian Splatting SLAM
AI总结：
提出AgriGS-SLAM框架，结合激光雷达里程计与多相机3D高斯泼溅，解决果园跨季节实时3D建图问题，实现更清晰稳定的重建与轨迹。
摘要：
果园中的自主机器人需要实时的3D场景理解，尽管存在重复的行几何结构、季节性外观变化和风驱动的树叶运动。我们提出了AgriGS-SLAM，这是一个视觉-激光雷达SLAM框架，将直接激光雷达里程计和闭环与多相机3D高斯泼溅（3DGS）渲染相结合。跨互补视角的批量光栅化在遮挡下恢复果园结构，而在关键帧之间执行的统一梯度驱动地图生命周期则保留了精细细节并限制了内存。位姿细化由基于概率激光雷达的深度一致性项引导，通过相机投影反向传播以加强几何-外观耦合。我们在处于休眠、开花和收获期的苹果和梨果园的现场平台上部署了该系统，使用标准化的轨迹协议评估训练视图和新视图合成，以减少评估中3DGS的过拟合。跨季节和地点，AgriGS-SLAM比最近最先进的3DGS-SLAM基线提供了更清晰、更稳定的重建和更平稳的轨迹，同时在拖拉机上保持实时性能。虽然在果园监测中得到演示，该方法可应用于需要稳健多模态感知的其他户外领域。
英文摘要：
Autonomous robots in orchards require real-time 3D scene understanding despite repetitive row geometry, seasonal appearance changes, and wind-driven foliage motion. We present AgriGS-SLAM, a Visual--LiDAR SLAM framework that couples direct LiDAR odometry and loop closures with multi-camera 3D Gaussian Splatting (3DGS) rendering. Batch rasterization across complementary viewpoints recovers orchard structure under occlusions, while a unified gradient-driven map lifecycle executed between keyframes preserves fine details and bounds memory. Pose refinement is guided by a probabilistic LiDAR-based depth consistency term, back-propagated through the camera projection to tighten geometry-appearance coupling. We deploy the system on a field platform in apple and pear orchards across dormancy, flowering, and harvesting, using a standardized trajectory protocol that evaluates both training-view and novel-view synthesis to reduce 3DGS overfitting in evaluation. Across seasons and sites, AgriGS-SLAM delivers sharper, more stable reconstructions and steadier trajectories than recent state-of-the-art 3DGS-SLAM baselines while maintaining real-time performance on-tractor. While demonstrated in orchard monitoring, the approach can be applied to other outdoor domains requiring robust multimodal perception.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
