Efficient MSPSO Sampling for Object Detection and 6D Pose Estimation in 3D Scenes

Xuejun Xing, Jianwei Guo, Liangliang Nan, Qingyi Gu, Xiaopeng Zhang, Dong Ming Yan

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Abstract

The point pair feature (PPF) is widely used in industrial applications for estimating 6D poses of known objects from unrecognized point clouds. The key to the success of PPF matching is to establish correct 3D correspondences between the object and the scene, i.e., finding as many valid similar point pairs as possible. Thus, a set of reference points in the scene should be sampled and paired with other points in the scene to create point pair features. However, efficient sampling of scene point pairs has been overlooked in existing frameworks. The novelty of our approach is a new sampling algorithm for selecting scene reference points based on the multi-subpopulation particle swarm optimization (MSPSO) guided by a probability map. We also introduce an effective pose clustering and hypotheses verification method to obtain the optimal pose. Moreover, we optimize the progressive sampling for multi-frame point clouds to improve processing efficiency. The experimental results show that our method outperforms previous methods by 6.6%, 3.9% in terms of accuracy on the public DTU and LineMOD datasets, respectively. We further validate our approach by applying it in a real robot grasping task.

Original languageEnglish
Article number10281
Pages (from-to)10281-10291
Number of pages11
JournalIEEE Transactions on Industrial Electronics
Volume69
Issue number10
DOIs
Publication statusPublished - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • 3D point cloud
  • 6D pose estimation
  • Clustering algorithms
  • Deep learning
  • Image segmentation
  • Multi-subpopulation particle swarm optimization
  • Point pair features
  • Pose estimation
  • Robot kinematics
  • Robustness
  • Three-dimensional displays

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