Robust 6D Object Pose Estimation by Learning RGB-D Features

2020/02/29 by Meng Tian, Liang Pan, Tian, Meng +5 · 3 citations
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #cs.CV

paper · pdf · doi:10.48550/arxiv.2003.00188

Accepted at ICRA 2020

arxiv created 2020/03/09 · arxiv updated 2020/03/10

Abstract

Accurate 6D object pose estimation is fundamental to robotic manipulation and grasping. Previous methods follow a local optimization approach which minimizes the distance between closest point pairs to handle the rotation ambiguity of symmetric objects. In this work, we propose a novel discrete-continuous formulation for rotation regression to resolve this local-optimum problem. We uniformly sample rotation anchors in SO(3), and predict a constrained deviation from each anchor to the target, as well as uncertainty scores for selecting the best prediction. Additionally, the object location is detected by aggregating point-wise vectors pointing to the 3D center. Experiments on two benchmarks: LINEMOD and YCB-Video, show that the proposed method outperforms state-of-the-art approaches. Our code is available at https://github.com/mentian/object-posenet.

Cited by

• Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds
• Detecting Pose Estimation Failures via Keypoint Self-Consistency
• Instance-Adaptive Keypoint Learning with Local-to-Global Geometric Aggregation for Category-Level Object Pose Estimation

Related

• PrimA6D: Rotational Primitive Reconstruction for Enhanced and Robust 6D Pose Estimation
• 6D Pose Estimation for Textureless Objects on RGB Frames using Multi-View Optimization
• Occlusion Resistant Object Rotation Regression from Point Cloud Segments
• 6D Object Pose Estimation without PnP
• 6IMPOSE: Bridging the Reality Gap in 6D Pose Estimation for Robotic Grasping