Dense 3D representations of the environment have been a long-term goal in the robotics field. While previous Neural Radiance Fields (NeRF) representation have been prevalent for its implicit, coordinate-based model, the recent emergence of 3D Gaussian Splatting (3DGS) has demonstrated remarkable potential in its explicit radiance field representation. By leveraging 3D Gaussian primitives for explicit scene representation and enabling differentiable rendering, 3DGS has shown significant advantages over other radiance fields in real-time rendering and photo-realistic performance, which is beneficial for robotic applications. In this survey, we provide a comprehensive understanding of 3DGS in the field of robotics. We divide our discussion of the related works into two main categories: the application of 3DGS and the advancements in 3DGS techniques. In the application section, we explore how 3DGS has been utilized in various robotics tasks from scene understanding and interaction perspectives. The advance of 3DGS section focuses on the improvements of 3DGS own properties in its adaptability and efficiency, aiming to enhance its performance in robotics. We then summarize the most commonly used datasets and evaluation metrics in robotics. Finally, we identify the challenges and limitations of current 3DGS methods and discuss the future development of 3DGS in robotics.
密集的3D环境表示一直是机器人领域的长期目标。尽管先前的神经辐射场（NeRF）因其基于坐标的隐式模型而广受欢迎，但近期3D高斯散射（3DGS）的出现展示了其显式辐射场表示的巨大潜力。通过利用3D高斯基元进行显式场景表示并实现可微渲染，3DGS在实时渲染和照片级真实感性能方面表现出显著优势，这对机器人应用非常有利。在本综述中，我们对3DGS在机器人领域的应用进行了全面的探讨。我们将相关工作分为两个主要类别：3DGS的应用和3DGS技术的进展。在应用部分，我们探讨了3DGS如何从场景理解和交互的角度应用于各种机器人任务。在3DGS技术进展部分，我们聚焦于其自适应性和效率的提升，旨在增强其在机器人领域的性能。随后，我们总结了机器人领域中最常用的数据集和评估指标。最后，我们指出了当前3DGS方法的挑战和局限性，并讨论了3DGS在机器人领域未来的发展方向。