Abstract:

The paper proposes a method for learning compact representations of the configuration (joint positions) of articulated mechanisms consisting of interconnected rigid bodies, from collected sequences of keypoint positions observed and tracked in camera images. The method analyzes the variations in pairwise distances between keypoints over time to deduce which of the keypoints must belong to the same rigid body and then computes the relative pose of all rigid bodies with respect to a reference image representing an initial or target configuration of the mechanism. By analyzing the rank of data matrices representing the translational and rotational components of the relative poses between the rigid bodies over time, the algorithm infers the order of the kinematic chain of the mechanism and the type of joints used in it, allowing the construction of a configuration vector as compact as the true joint positions of the mechanism.