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一个快速发展的研究问题。赋予机器人感知透明物体的能力将提升其自主性水平，为医疗、服务和制造等各行业释放新的应用潜力。尽管近年来已有大量数据集和感知方法被提出，但对此类方法及该领域挑战的深入理解仍显不足。为填补这一空白，本文对透明物体机器人感知平台及最新进展进行了全面综述。我们重点阐述了透明物体感知任务（即分割、重建与位姿估计）中的主要挑战，并提出未来研究方向。同时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。此外，我们识别了复杂动态环境中及几何可变形物体感知面临的挑战。最后，我们提供一个可供导航参考文献的交互式在线平台：\url{https://sites.google.com/view/transperception}。