局所と大域の視点計画を組み合わせた果実被覆のための視点生成
Combining Local and Global Viewpoint Planning for Fruit Coverage
局所的な勾配ベースの視点計画と大域的な被覆計画を組み合わせ、果実の遮蔽を避けつつ広範囲を撮影できる視点列を生成する手法を提案し、ロボットアーム実験で被覆率の向上を示した。
著者: Tobias Zaenker, Chris Lehnert, Chris McCool, Maren Bennewitz
分類: cs.RO
原文アブストラクト
Obtaining 3D sensor data of complete plants or plant parts (e.g., the crop or fruit) is difficult due to their complex structure and a high degree of occlusion. However, especially for the estimation of the position and size of fruits, it is necessary to avoid occlusions as much as possible and acquire sensor information of the relevant parts. Global viewpoint planners exist that suggest a series of viewpoints to cover the regions of interest up to a certain degree, but they usually prioritize global coverage and do not emphasize the avoidance of local occlusions. On the other hand, there are approaches that aim at avoiding local occlusions, but they cannot be used in larger environments since they only reach a local maximum of coverage. In this paper, we therefore propose to combine a local, gradient-based method with global viewpoint planning to enable local occlusion avoidance while still being able to cover large areas. Our simulated experiments with a robotic arm equipped with a camera array as well as an RGB-D camera show that this combination leads to a significantly increased coverage of the regions of interest compared to just applying global coverage planning.
