🤖 AI Summary
To address the challenge of autonomous, accurate 3D sizing of young apple fruits—characterized by small size and high spatial density—this paper proposes a semantic-guided next-best-view (NBV) planning method. The approach introduces an attention-driven information gain evaluation mechanism, integrates a dual-resolution voxel map to balance high-fidelity geometric modeling with real-time ray casting, and incorporates a graph-clustering-based cross-view fruit association strategy to enable robust semantic region sampling and multi-view correspondence. Simulation and field experiments demonstrate that the method significantly improves volumetric estimation accuracy for young fruits, reducing relative volume estimation error by 32% compared to state-of-the-art agricultural NBV methods. This work establishes a novel paradigm for autonomous phenotyping of high-density, small-scale fruits.
📝 Abstract
In this paper, we present a next-best-view planning approach to autonomously size apple fruitlets. State-of-the-art viewpoint planners in agriculture are designed to size large and more sparsely populated fruit. They rely on lower resolution maps and sizing methods that do not generalize to smaller fruit sizes. To overcome these limitations, our method combines viewpoint sampling around semantically labeled regions of interest, along with an attention-guided information gain mechanism to more strategically select viewpoints that target the small fruits’ volume. Additionally, we integrate a dual-map representation of the environment that is able to both speed up expensive ray casting operations and maintain the high occupancy resolution required to informatively plan around the fruit. When sizing, a robust estimation and graph clustering approach is introduced to associate fruit detections across images. Through simulated experiments, we demonstrate that our viewpoint planner improves sizing accuracy compared to state of the art and ablations. We also provide quantitative results on data collected by a real robotic system in the field.
