Real-time pose estimation of deformable objects using a volumetric approach

Abstract

Pose estimation of deformable objects is a fundamental and challenging problem in robotics. We present a novel solution to this problem by first reconstructing a 3D model of the object from a low-cost depth sensor such as Kinect, and then searching a database of simulated models in different poses to predict the pose. Given noisy depth images from 360-degree views of the target object acquired from the Kinect sensor, we reconstruct a smooth 3D model of the object using depth image segmentation and volumetric fusion. Then with an efficient feature extraction and matching scheme, we search the database, which contains a large number of deformable objects in different poses, to obtain the most similar model, whose pose is then adopted as the prediction. Extensive experiments demonstrate better accuracy and orders of magnitude speed-up compared to our previous work. An additional benefit of our method is that it produces a high-quality mesh model and camera pose, which is necessary for other tasks such as regrasping and object manipulation.