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: \url{https://sites.google.com/view/transperception}.
翻译:透明物体感知是人工智能领域中正在快速发展的研究问题。感知透明物体的能力使得机器人能够实现更高水平的自主性,从而在医疗保健、服务和制造等各个行业中开辟了新的应用。尽管近年来涌现了许多数据集和感知方法,但该领域仍然缺乏对这些方法及挑战的深入了解。为了填补这一空白,本文全面调研了机器人感知透明物体的平台和最新进展。我们重点介绍了透明物体感知任务的主要挑战和未来方向,包括分割、重建和姿态估计。我们还讨论了现有数据集在多样性和复杂性方面的局限性,以及采用多模态传感器(如RGB-D相机、热成像相机和偏振成像)对透明物体进行感知的好处。此外,我们还确定了复杂和动态环境中的感知挑战,以及针对具有可变几何形态的物体的感知挑战。最后,我们提供了一个互动的在线平台,用于查阅每个参考文献:\url{https://sites.google.com/view/transperception}。