MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusion
This addresses the need for efficient object-based scene representations in robotics for tasks like manipulation, but it is incremental as it builds on existing pose estimation methods with multi-view fusion.
The paper tackles the problem of estimating accurate 6D poses for multiple known objects in cluttered scenes with contact and occlusion from real-time, multi-view RGB-D vision, achieving real-time performance and enabling a robot arm to precisely disassemble piles of objects using on-board vision.
Robots and other smart devices need efficient object-based scene representations from their on-board vision systems to reason about contact, physics and occlusion. Recognized precise object models will play an important role alongside non-parametric reconstructions of unrecognized structures. We present a system which can estimate the accurate poses of multiple known objects in contact and occlusion from real-time, embodied multi-view vision. Our approach makes 3D object pose proposals from single RGB-D views, accumulates pose estimates and non-parametric occupancy information from multiple views as the camera moves, and performs joint optimization to estimate consistent, non-intersecting poses for multiple objects in contact. We verify the accuracy and robustness of our approach experimentally on 2 object datasets: YCB-Video, and our own challenging Cluttered YCB-Video. We demonstrate a real-time robotics application where a robot arm precisely and orderly disassembles complicated piles of objects, using only on-board RGB-D vision.