发表机构
University of Nevada, Reno; Missouri University of Science and Technology（内华达大学雷诺分校; 密苏里科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对自主叶片表面重建的规划预算与计算资源限制，提出滚动时域次最佳视点规划器，通过基于质心的信息增益函数优化视点效用，在草莓数据集上使重建精度较基线最高提升10%。
AI 中文摘要
准确的植物叶片建模是植物生长监测、用于产量估算的表型分析等下游任务的基础。面向大规模田间部署的自主机器人重建必须解决机器人规划预算和计算资源的限制，同时优化叶片表面重建的视点效用。现有方法要么聚焦于刚性物体、点云覆盖，要么进行植物重建，但未完全解决系统限制或利用任务驱动的点云效用。本研究针对存在行程约束的叶片表面重建问题，研究次最佳视点（NBV）规划，开发了一种新颖的基于质心的信息增益（CIG）函数，该函数测量观测点相对于现有点云质心的空间分布，以计算视点效用；还开发了一种滚动时域变体，可对未来视点进行推理。为进行基准测试，使用包含不同生长阶段草莓植物点云的公开数据集LAST-STRAW [1]，将所提方法与采用基于可见性的信息增益方法的注意力驱动NBV [2]进行对比。所提出的滚动时域方法在多个生长阶段持续降低表面重建误差、提高几何保真度，尤其在叶片间遮挡增加时效果显著；结果表明，与基线相比，该方法访问的视点可降低表面重建误差，使重建精度提升最高达10%。
英文摘要
Accurate plant leaf modeling is fundamental to downstream tasks such as plant growth monitoring, and phenotyping for yield estimation. Autonomous robotic reconstruction for large-scale field deployment must address limitations on robot planning budget and computation resources while optimizing viewpoint utility for leaf surface reconstruction. Existing approaches either focus on rigid objects, point-cloud coverage or plant reconstruction without fully addressing the system limitations or exploiting task-driven point cloud utility. In this work, we study next-best-view (NBV) planning for leaf surface reconstruction under travel constraints. We develop a novel Centroid-based Information Gain (CIG) function that measures the spatial distribution of observed points relative to the centroid of the existing point cloud to compute viewpoint utility. We also develop a receding-horizon variant that reasons over future viewpoints. To benchmark our work, we use the LAST-STRAW [1] public dataset that includes point clouds of strawberry plants over different growth stages and compare our method with attention-driven NBV [2] that uses a visibility-based information gain approach. The proposed receding-horizon approach consistently reduces surface reconstruction error and improves geometric fidelity across multiple growth stages, especially under increased inter-leaf occlusion. Results demonstrate that our approach is able to visit viewpoints that reduce surface reconstruction error and improves reconstruc-tion accuracy as compared to the baseline by upto 10%.
CommentsAccepted at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
