形状補完に基づく果実マッピングと再構成のための次善視点計画
NBV-SC: Next Best View Planning based on Shape Completion for Fruit Mapping and Reconstruction
果実の予測形状を利用して未観測部分を狙う視点を計画し、視点の非類似性でサンプリングを削減する手法を提案。シミュレーションと実機でサイズ推定・再構成の精度向上と計画時間短縮を実証した。
著者: Rohit Menon, Tobias Zaenker, Nils Dengler, Maren Bennewitz
分類: cs.RO, cs.CV
原文アブストラクト
Active perception for fruit mapping and harvesting is a difficult task since occlusions occur frequently and the location as well as size of fruits change over time. State-of-the-art viewpoint planning approaches utilize computationally expensive ray casting operations to find good viewpoints aiming at maximizing information gain and covering the fruits in the scene. In this paper, we present a novel viewpoint planning approach that explicitly uses information about the predicted fruit shapes to compute targeted viewpoints that observe as yet unobserved parts of the fruits. Furthermore, we formulate the concept of viewpoint dissimilarity to reduce the sampling space for more efficient selection of useful, dissimilar viewpoints. Our simulation experiments with a UR5e arm equipped with an RGB-D sensor provide a quantitative demonstration of the efficacy of our iterative next best view planning method based on shape completion. In comparative experiments with a state-of-the-art viewpoint planner, we demonstrate improvement not only in the estimation of the fruit sizes, but also in their reconstruction, while significantly reducing the planning time. Finally, we show the viability of our approach for mapping sweet peppers plants with a real robotic system in a commercial glasshouse.
