Interactive Segmentation, Tracking, and Kinematic Modeling of Unknown Articulated Objects

Abstract

We present an interactive perceptual skill for segmenting, tracking, and kinematic modeling of 3D articulated objects. This skill is a prerequisite for general manipu- lation in unstructured environments. Robot-environment interaction is used to move an unknown object, creating a perceptual signal that reveals the kinematic properties of the object. The resulting perceptual information can then inform and facilitate fur- ther manipulation. The algorithm is computationally efficient, handles occlusion, and depends on little object motion; it only requires sufficient texture for visual feature tracking. We conducted experiments with everyday objects on a mobile manipula- tion platform equipped with an RGB-D sensor. The results demonstrate the robustness of the proposed method to lighting conditions, object appearance, size, structure, and configuration.