アンセンテッド変換を用いたロバストな花クラスタマッチング
Robust Flower Cluster Matching Using The Unscented Transform
RGB-Dデータから生成した記述子とアンセンテッド変換による不確かさ推定を組み合わせ、時間変化や遮蔽があっても花クラスタを高精度にマッチングする手法を提案。
著者: Andy Chu, Rashik Shrestha, Yu Gu, Jason N. Gross
分類: cs.RO, cs.CV
原文アブストラクト
Monitoring flowers over time is essential for precision robotic pollination in agriculture. To accomplish this, a continuous spatial-temporal observation of plant growth can be done using stationary RGB-D cameras. However, image registration becomes a serious challenge due to changes in the visual appearance of the plant caused by the pollination process and occlusions from growth and camera angles. Plants flower in a manner that produces distinct clusters on branches. This paper presents a method for matching flower clusters using descriptors generated from RGB-D data and considers allowing for spatial uncertainty within the cluster. The proposed approach leverages the Unscented Transform to efficiently estimate plant descriptor uncertainty tolerances, enabling a robust image-registration process despite temporal changes. The Unscented Transform is used to handle the nonlinear transformations by propagating the uncertainty of flower positions to determine the variations in the descriptor domain. A Monte Carlo simulation is used to validate the Unscented Transform results, confirming our method's effectiveness for flower cluster matching. Therefore, it can facilitate improved robotics pollination in dynamic environments.
