针对遮挡下的牛油果采摘，提出基于几何与语义的视角规划算法。
GS-NBV: a Geometry-based, Semantics-aware Viewpoint Planning Algorithm for Avocado Harvesting under Occlusions
- 通过几何约束将搜索空间缩小至1维圆环，高效采样四个视角点。
- 引入新拾取评分机制，在严重遮挡下实现100%成功采摘。
- 适合复杂果园环境中的自动化采摘系统研发人员使用。
高效识别采摘点对自动化果实采收至关重要。牛油果因其不规则形状、重量及无序生长环境带来独特挑战，需特定视角才能成功采收。本文提出一种基于几何与语义感知的视角规划算法，包含视角采样、评估与执行三步。从部分遮挡视图出发，系统先检测果实，利用几何信息将视角搜索空间限制在1维圆环上，并均匀采样四个点以平衡效率与探索。提出新的拾取评分指标评估视角适用性，引导相机获取下一个最优视角。通过仿真对比两种先进算法验证方法有效性。两组案例研究中均在显著遮挡下实现100%成功率，证明该方法高效且鲁棒。代码已开源：https://github.com/lineojcd/GSNBV。
原文摘要 · Abstract (English)
Efficient identification of picking points is critical for automated fruit harvesting. Avocados present unique challenges owing to their irregular shape, weight, and less-structured growing environments, which require specific viewpoints for successful harvesting. We propose a geometry-based, semantics-aware viewpoint-planning algorithm to address these challenges. The planning process involves three key steps: viewpoint sampling, evaluation, and execution. Starting from a partially occluded view, the system first detects the fruit, then leverages geometric information to constrain the viewpoint search space to a 1D circle, and uniformly samples four points to balance the efficiency and exploration. A new picking score metric is introduced to evaluate the viewpoint suitability and guide the camera to the next-best view. We validate our method through simulation against two state-of-the-art algorithms. Results show a 100% success rate in two case studies with significant occlusions, demonstrating the efficiency and robustness of our approach. Our code is available at https://github.com/lineojcd/GSNBV
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