  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.
