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Multi-View Metal Parts Pose Estimation Based on a Single Camera

  • Chen Chen
  • , Xin Jiang*
  • *Corresponding author for this work
  • Harbin Institute of Technology Shenzhen

Research output: Contribution to journalArticlepeer-review

Abstract

Pose estimation of metal parts plays a vital role in industrial grasping areas. It is challenging to obtain complete point clouds of metal parts because of their reflective properties. This study introduces an approach for recovering the 6D pose of CAD-known metal parts from images captured by a single RGB camera. The proposed strategy only requires RGB images without depth information. The core idea of the proposed method is to use multiple views to estimate the metal parts’ pose. First, the pose of metal parts is estimated in the first view. Second, ray casting is employed to simulate additional views with the corresponding status of the metal parts, enabling the calculation of the camera’s next best viewpoint. The camera, mounted on a robotic arm, is then moved to this calculated position. Third, this study integrates the known camera transformations with the poses estimated from different viewpoints to refine the final scene. The results of this work demonstrate that the proposed method effectively estimates the pose of shiny metal parts.

Original languageEnglish
Article number3408
JournalSensors
Volume24
Issue number11
DOIs
StatePublished - Jun 2024
Externally publishedYes

Keywords

  • RGB perception
  • deep learning for visual perception
  • pose estimation

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