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中一个快速发展的研究问题。感知透明物体的能力使机器人能够达到更高的自主水平，从而在医疗、服务和制造业等各行各业中开启新的应用。尽管近年来提出了大量数据集和感知方法，但对于这些方法以及该领域面临的挑战仍缺乏深入理解。为填补这一空白，本文对机器人透明物体感知的平台及最新进展进行了全面综述。我们重点阐述了主要挑战，并针对透明物体感知的各类任务（即分割、重建和姿态估计）提出了未来研究方向。此外，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）对透明物体感知的益处。进一步地，我们指出了复杂动态环境及几何形态可变物体所带来的感知挑战。最后，我们提供了一个交互式在线平台以导航每篇参考文献：\url{https://sites.google.com/view/transperception}。