Occlusion-robust Deformable Object Tracking without Physics Simulation
This work is significant for robotics researchers and practitioners working on deformable object manipulation, offering a solution to the problem of robust tracking under occlusion without requiring complex physical models.
This paper addresses the challenge of tracking deformable objects under partial occlusion without relying on physics simulations. The proposed method, based on Coherent Point Drift (CPD), achieves improved accuracy compared to physics-based CPD methods in occluded scenarios while maintaining adequate run-time.
Estimating the state of a deformable object is crucial for robotic manipulation, yet accurate tracking is challenging when the object is partially-occluded. To address this problem, we propose an occlusion-robust RGBD sequence tracking framework based on Coherent Point Drift (CPD). To mitigate the effects of occlusion, our method 1) Uses a combination of locally linear embedding and constrained optimization to regularize the output of CPD, thus enforcing topological consistency when occlusions create disconnected pieces of the object; 2) Reasons about the free-space visible by an RGBD sensor to better estimate the prior on point location and to detect tracking failures during occlusion; and 3) Uses shape descriptors to find the most relevant previous state of the object to use for tracking after a severe occlusion. Our method does not rely on physics simulation or a physical model of the object, which can be difficult to obtain in unstructured environments. Despite having no physical model, our experiments demonstrate that our method achieves improved accuracy in the presence of occlusion as compared to a physics-based CPD method while maintaining adequate run-time.