  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.
