Simultaneous Localization and Mapping (SLAM) with dense representation plays a key role in robotics, Virtual Reality (VR), and Augmented Reality (AR) applications. Recent advancements in dense representation SLAM have highlighted the potential of leveraging neural scene representation and 3D Gaussian representation for high-fidelity spatial representation. In this paper, we propose a novel dense representation SLAM approach with a fusion of Generalized Iterative Closest Point (G-ICP) and 3D Gaussian Splatting (3DGS). In contrast to existing methods, we utilize a single Gaussian map for both tracking and mapping, resulting in mutual benefits. Through the exchange of covariances between tracking and mapping processes with scale alignment techniques, we minimize redundant computations and achieve an efficient system. Additionally, we enhance tracking accuracy and mapping quality through our keyframe selection methods. Experimental results demonstrate the effectiveness of our approach, showing an incredibly fast speed up to 107 FPS (for the entire system) and superior quality of the reconstructed map.
在机器人、虚拟现实（VR）和增强现实（AR）应用中，具有密集表示的同时定位与建图（SLAM）起着关键作用。最近在密集表示SLAM的进展突显了利用神经场景表示和3D高斯表示进行高保真空间表示的潜力。在本文中，我们提出了一种新颖的密集表示SLAM方法，该方法融合了广义迭代最近点（G-ICP）和3D高斯喷溅（3DGS）。与现有方法不同，我们利用单一的高斯地图同时进行跟踪和映射，从而获得相互利益。通过在跟踪和映射过程中交换协方差，并使用比例对齐技术，我们最小化了冗余计算并实现了一个高效的系统。此外，我们通过我们的关键帧选择方法提高了跟踪精度和映射质量。实验结果证明了我们方法的有效性，显示出高达107 FPS（整个系统）的令人难以置信的快速速度和重建地图的优越质量。