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Multi-view object pose distribution tracking for pre-grasp planning on mobile robots.

Lakshadeep Naik1, Thorbjørn Mosekjær Iversen1, Jakob Wilm1

  • 1SDU Robotics, Mærsk Mc-Kinney Møller Institute (MMMI), Faculty of Engineering, University of Southern Denmark, Odense, Denmark.

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Summary
This summary is machine-generated.

This study introduces a multi-view particle filter to track object pose distributions for mobile manipulators. It improves grasp planning and robot manipulation robustness by fusing camera views, overcoming single-camera limitations.

Keywords:
mobile manipulationmulti-camera fusionpose distribution trackingpose estimationuncertainty quantification

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Accurate 6D object pose tracking is crucial for mobile manipulation tasks like grasp planning.
  • Limited camera views on approaching mobile manipulators pose a significant challenge for precise pose estimation.
  • Existing single-view tracking methods struggle with occlusions and incomplete object visibility.

Purpose of the Study:

  • To develop a robust framework for tracking the 6D pose distribution of objects during mobile manipulator approach.
  • To enhance grasp planning efficiency and reliability by providing accurate pose information.
  • To overcome the limitations of single-camera views by fusing multi-view observations.

Main Methods:

  • A particle filter-based multi-view framework is proposed, extending the PoseRBPF.
  • Fusion of observations from the mobile manipulator's camera and external stationary cameras.
  • Modeling the object pose posterior as a multi-modal distribution.
  • Techniques for data fusion, re-sampling, and pose estimation from the tracked distribution are introduced.

Main Results:

  • The proposed framework achieves more accurate quantification of object pose and associated uncertainty compared to prior methods.
  • Effective handling of noisy and conflicting observations from multiple cameras.
  • Demonstrated improvement in pre-grasp planning for mobile robots.

Conclusions:

  • The multi-view approach significantly enhances the accuracy and robustness of 6D object pose distribution tracking.
  • Fusion of external camera data is vital for compensating for limited views during manipulator approach.
  • The framework offers practical utility for real-world mobile manipulation applications.