Adaptive Viewpoint Selection for Tomato Truss Localization via Polytope Hypotheses

Robotization is considered a key solution to labor shortages in the agri-food industry. However, deploying robots in natural environments is challenging due to unpredictable factors such as plant variances and occlusions. This paper focuses on the localization of tomato trusses for autonomous harvesting by servoing a robot-mounted camera to different viewpoints. We build on previous work where the robot is provided with prior knowledge of the tomato plant. Specifically, the geometric relations between the trusses are modeled as ranges, which reflect uncertainty. Our main contribution is an approach that represents this uncertainty as polytope volumes. Polytopes enable scalable reasoning that facilitates likelihood estimation for viewpoint selection. Our method first constructs polytope hypotheses regarding the truss locations based on prior plant knowledge. It then refines the polytope shapes using Bayesian updates based on camera observations. Finally, the polytopes are used to select the next viewpoint that maximizes the chance of observing a new tomato truss. Experiments show that polytope-based viewpoint selection speeds up truss localization compared to earlier methods, advancing robotic harvesting.