  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.
