Robotic perception of transparent objects: A review

Jiaqi Jiang, Guanqun Cao, Jiankang Deng, Thanh Toan Do, Shan Luo

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)

Abstract

Transparent object perception is a rapidly developing research problem in artificial intelligence. The ability to perceive transparent objects enables robots to achieve higher levels of autonomy, unlocking new applications in various industries such as healthcare, services and manufacturing. Despite numerous datasets and perception methods being proposed in recent years, there is still a lack of in-depth understanding of these methods and the challenges in this field. To address this gap, this article provides a comprehensive survey of the platforms and recent advances for robotic perception of transparent objects. We highlight the main challenges and propose future directions of various transparent object perception tasks, i.e., segmentation, reconstruction, and pose estimation. We also discuss the limitations of existing datasets in diversity and complexity, and the benefits of employing multi-modal sensors, such as RGB-D cameras, thermal cameras, and polarised imaging, for transparent object perception. Furthermore, we identify perception challenges in complex and dynamic environments, as well as for objects with changeable geometries. Finally, we provide an interactive online platform to navigate each reference: <uri>https://sites.google.com/view/transperception</uri>.

Original languageEnglish
Pages (from-to)2547-2567
Number of pages21
JournalIEEE Transactions on Artificial Intelligence
Volume5
Issue number6
DOIs
Publication statusPublished - 21 Jun 2024

Keywords

  • Artificial intelligence
  • Cameras
  • Deep learning
  • depth reconstruction
  • object segmentation
  • Robot vision systems
  • robotic perception
  • Robots
  • Sensors
  • Task analysis
  • Three-dimensional displays
  • transparent objects

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