ロボットビンピッキングのためのアクティブステレオ深度マップの確率的マルチビュー融合
Probabilistic Multi-View Fusion of Active Stereo Depth Maps for Robotic Bin-Picking
アクティブステレオカメラの深度測定の不確かさを明示的に推定し、それを確率的モデルに組み込んで多視点の深度マップを逐次融合する手法を提案。従来の融合法より再構成誤差を12.8%削減し、検出率を6.1%改善した。
著者: Jun Yang, Dong Li, Steven L. Waslander
分類: cs.RO
原文アブストラクト
The reliable fusion of depth maps from multiple viewpoints has become an important problem in many 3D reconstruction pipelines. In this work, we investigate its impact on robotic bin-picking tasks such as 6D object pose estimation. The performance of object pose estimation relies heavily on the quality of depth data. However, due to the prevalence of shiny surfaces and cluttered scenes, industrial grade depth cameras often fail to sense depth or generate unreliable measurements from a single viewpoint. To this end, we propose a novel probabilistic framework for scene reconstruction in robotic bin-picking. Based on active stereo camera data, we first explicitly estimate the uncertainty of depth measurements for mitigating the adverse effects of both noise and outliers. The uncertainty estimates are then incorporated into a probabilistic model for incrementally updating the scene. To extensively evaluate the traditional fusion approach alongside our own approach, we will release a novel representative dataset with multiple views for each bin and curated parts. Over the entire dataset, we demonstrate that our framework outperforms a traditional fusion approach by a 12.8% reduction in reconstruction error, and 6.1% improvement in detection rate. The dataset will be available at https://www.trailab.utias.utoronto.ca/robi.
