Nothing But Geometric Constraints: A Model-Free Method for Articulated Object Pose Estimation

• URL: http://arxiv.org/abs/2012.00088v1
• Date: Mon, 30 Nov 2020 20:46:48 GMT
• Title: Nothing But Geometric Constraints: A Model-Free Method for Articulated Object Pose Estimation
• Authors: Qihao Liu, Weichao Qiu, Weiyao Wang, Gregory D. Hager, Alan L. Yuille
• Abstract summary: We propose an unsupervised vision-based system to estimate the joint configurations of the robot arm from a sequence of RGB or RGB-D images without knowing the model a priori. We combine a classical geometric formulation with deep learning and extend the use of epipolar multi-rigid-body constraints to solve this task.
• Score: 89.82169646672872
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an unsupervised vision-based system to estimate the joint configurations of the robot arm from a sequence of RGB or RGB-D images without knowing the model a priori, and then adapt it to the task of category-independent articulated object pose estimation. We combine a classical geometric formulation with deep learning and extend the use of epipolar constraint to multi-rigid-body systems to solve this task. Given a video sequence, the optical flow is estimated to get the pixel-wise dense correspondences. After that, the 6D pose is computed by a modified PnP algorithm. The key idea is to leverage the geometric constraints and the constraint between multiple frames. Furthermore, we build a synthetic dataset with different kinds of robots and multi-joint articulated objects for the research of vision-based robot control and robotic vision. We demonstrate the effectiveness of our method on three benchmark datasets and show that our method achieves higher accuracy than the state-of-the-art supervised methods in estimating joint angles of robot arms and articulated objects.

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