葉表面再構成のための後退水平型次善視点プランナー
Receding-Horizon Next-Best-View Planner for Autonomous Leaf Surface Reconstruction
移動制約下で葉表面の3次元再構成精度を高めるため、点群の重心に対する情報利得を基に視点の有用性を評価し、将来視点を考慮した後退水平型の次善視点計画手法を提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Arif Ahmed, Sajal K. Das, Parikshit Maini
分類: cs.RO
原文アブストラクト
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%.
