Abstract
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.
Abstract (translated)
同时定位与映射（SLAM）在机器人学、虚拟现实（VR）和增强现实（AR）应用中扮演着关键角色。最近，在密集表示SLAM方面的先进技术突出了利用神经场景表示和3D高斯表示进行高保真度空间表示的潜力。在本文中，我们提出了一个新的密集表示SLAM方法，结合了扩展迭代最近点（G-ICP）和3D高斯展平（3DGS）。与现有方法不同，我们使用单个高斯地图进行跟踪和映射，从而实现相互有益。通过跟踪和映射过程之间的协方差交换，我们最小化冗余计算并实现高效的系统。此外，我们还通过关键帧选择方法提高了跟踪准确性和映射质量。实验结果证明了我们的方法的有效性，显示了系统速度加快到107 FPS（整个系统）以及重建地图的质量优越。
URL
https://arxiv.org/abs/2403.12550
