密集した clutter 環境での高精度物体姿勢推定のための能動的視触覚対話型ロボット知覚
Active Visuo-Tactile Interactive Robotic Perception for Accurate Object Pose Estimation in Dense Clutter
密集した clutter 環境で物体姿勢を高精度に推定するため、declutter graph による片付け計画と、視覚・触覚の情報利得最大化に基づく能動的姿勢推定を組み合わせたフレームワークを提案した。
著者: Prajval Kumar Murali, Anirvan Dutta, Michael Gentner, Etienne Burdet, Ravinder Dahiya, Mohsen Kaboli
分類: cs.RO
原文アブストラクト
This work presents a novel active visuo-tactile based framework for robotic systems to accurately estimate pose of objects in dense cluttered environments. The scene representation is derived using a novel declutter graph (DG) which describes the relationship among objects in the scene for decluttering by leveraging semantic segmentation and grasp affordances networks. The graph formulation allows robots to efficiently declutter the workspace by autonomously selecting the next best object to remove and the optimal action (prehensile or non-prehensile) to perform. Furthermore, we propose a novel translation-invariant Quaternion filter (TIQF) for active vision and active tactile based pose estimation. Both active visual and active tactile points are selected by maximizing the expected information gain. We evaluate our proposed framework on a system with two robots coordinating on randomized scenes of dense cluttered objects and perform ablation studies with static vision and active vision based estimation prior and post decluttering as baselines. Our proposed active visuo-tactile interactive perception framework shows upto 36% improvement in pose accuracy compared to the active vision baseline.
