果実のサイズと位置推定のための視点計画
Viewpoint Planning for Fruit Size and Position Estimation
植物の果実領域をオクツリーで管理し、情報利得に基づく視点候補の評価と探索・注視の自動切替により、葉の遮蔽下でも果実の位置とサイズを推定する手法を提案し、シミュレーションと実機で有効性を示した。
著者: Tobias Zaenker, Claus Smitt, Chris McCool, Maren Bennewitz
分類: cs.RO
原文アブストラクト
Modern agricultural applications require knowledge about the position and size of fruits on plants. However, occlusions from leaves typically make obtaining this information difficult. We present a novel viewpoint planning approach that builds up an octree of plants with labeled regions of interest (ROIs), i.e., fruits. Our method uses this octree to sample viewpoint candidates that increase the information around the fruit regions and evaluates them using a heuristic utility function that takes into account the expected information gain. Our system automatically switches between ROI targeted sampling and exploration sampling, which considers general frontier voxels, depending on the estimated utility. When the plants have been sufficiently covered with the RGB-D sensor, our system clusters the ROI voxels and estimates the position and size of the detected fruits. We evaluated our approach in simulated scenarios and compared the resulting fruit estimations with the ground truth. The results demonstrate that our combined approach outperforms a sampling method that does not explicitly consider the ROIs to generate viewpoints in terms of the number of discovered ROI cells. Furthermore, we show the real-world applicability by testing our framework on a robotic arm equipped with an RGB-D camera installed on an automated pipe-rail trolley in a capsicum glasshouse.
