# RD-SLAM: Real-Time Dense SLAM Using Gaussian Splatting
## 1 Introduction  
Visual simultaneous localization and mapping (SLAM) is a technology in which robots rely on their vision sensors to enable real-time positioning and mapping in unknown environments [1; 2]. After decades of development, SLAM has been widely used in fields such as autonomous driving, virtual reality, and augmented reality (AR). Traditional SLAM methods have achieved precise tracking accuracy in a variety of environments while using point clouds [3; 4; 5; 6], meshes [7; 8], voxel [9; 10; 11], or truncated signed distance function (TSDF) [12; 13; 14] to represent the scene. However, to adapt to increasingly complex downstream tasks such as autonomous goal navigation and human-computer interaction, SLAM systems need to make breakthroughs in map quality and density so that the maps constructed by the system have real-world realism. Neural Radiance Fields (NeRF) [15] have been used in SLAM systems, demonstrating highly novel scene inference and high-fidelity map representation capabilities. NeRF is a novel view synthesis method with an implicit scene representation that offers the advantages of realism and continuity. SLAM systems using NeRF typically represent the scene as a multilayer perceptron (MLP), estimating the pose and optimizing the map parameters through differentiable volumetric render. However, NeRF-based SLAM requires using severely time-consuming ray-based volume rendering techniques to obtain high-resolution images. Concurrently, it hides the scene information in the MLP, precluding the expeditious undertaking of tasks such as scene editing [16; 17]. 3D Gaussian splatting (3 DGS) [18] has more potential for dense map representation due to its high-fidelity rendering quality and hundreds of times faster rendering than NeRF. In addition to faster rendering, 3 DGS-based scene representation enables the direct editing of the scene, which is essential for numerous downstream operations. 3D Gaussians are differentiable volumetric representations that are simultaneously unstructured andexplicit and can be rasterized very efficiently by projecting them to 2D. With these advantages, 3 DGS can be well applied to online SLAM systems with real-time requirements. Nevertheless, most extant 3D Gaussian-based SLAM systems [19; 20; 21; 22] employ photometric error techniques to ascertain the optimal pose through iterative minimization of the error between the rendered and input images. This approach has the consequence of significantly constraining the system's real-time performance. Based on this, we use the generalized iterative closest point (G-ICP) [23] to replace the photometric error technique in position estimation and use a simple and efficient 3D point cloud fast tracking method to improve the system's operation speed and tracking accuracy. In this paper, we present RD-SLAM, a dense RGB-D SLAM system using 3D Gaussian as the only scene representation. We actively utilize the point cloud information acquired by the depth camera to estimate the position using the G-ICP. A dual keyframe selection strategy and corresponding densification method are proposed for 3 DGS characteristics and system mapping requirements, and regularization loss is added to ensure geometric consistency, which achieves a good balance between speed and accuracy. An example of the high-fidelity rendering output of RD-SLAM is shown in Figure 1. Overall, our contributions include the following:  
1. We propose a 3 DGS-based dense RGB-D SLAM system that utilizes fast rendering techniques to improve the speed of map optimization and achieve realistic map construction. 2. The system uses G-ICP for pose estimation, significantly reducing the time required for tracking while improving positioning accuracy. 3. We propose a dual keyframe selection strategy and corresponding densification method, and add regularization loss to the loss term to ensure tracking accuracy while improving rendering quality. 4. The system was extensively evaluated on various RGB-D datasets, achieving competitive tracking, mapping, and run speed performance.