6D Object Pose Estimation Using a Particle Filter With Better Initialization

Abstract

Estimation of 6D object poses is a key issue in robotic grasping tasks. Recently, many high-performance learning-based methods have been introduced using robust deep learning techniques; however, applying these methods to real robot environments requires many ground truth 6D pose annotations for training. To address this problem, we propose a template matching-based particle filter approach for 6D pose estimation; the proposed method does not require ground truth 6D poses. Although particle filter approaches can stochastically avoid local optima, they require adequate initial pose hypotheses for estimating an accurate 6D object pose. Therefore, we estimated an initial translation of the target object for accurately initializing a particle filter by developing a new deep network. Once the proposed centroid prediction network (CPN) is trained with a specific dataset, no additional training is required for new objects not in the dataset. We evaluated the performance of the CPN and the proposed 6D pose estimation method on benchmark datasets, which demonstrated that the CPN can predict the centroid for any object, including those not in the training data, and that our 6D pose estimation method outperforms existing methods for partially occluded objects. Finally, we tested a grasping task based on our proposed method using a real robot platform to demonstrate an application of our method to a downstream task. This experiment shows that our method can be applied to part assembly, bin picking, and object manipulation without large training datasets with 6D pose annotations.