Recently, map representations based on radiance fields such as 3D Gaussian Splatting and NeRF, which excellent for realistic depiction, have attracted considerable attention, leading to attempts to combine them with SLAM. While these approaches can build highly realistic maps, large-scale SLAM still remains a challenge because they require a large number of Gaussian images for mapping and adjacent images as keyframes for tracking. We propose a novel 3D Gaussian Splatting SLAM method, VIGS SLAM, that utilizes sensor fusion of RGB-D and IMU sensors for large-scale indoor environments. To reduce the computational load of 3DGS-based tracking, we adopt an ICP-based tracking framework that combines IMU preintegration to provide a good initial guess for accurate pose estimation. Our proposed method is the first to propose that Gaussian Splatting-based SLAM can be effectively performed in large-scale environments by integrating IMU sensor measurements. This proposal not only enhances the performance of Gaussian Splatting SLAM beyond room-scale scenarios but also achieves SLAM performance comparable to state-of-the-art methods in large-scale indoor environments.
最近，基于辐射场的地图表示方法，如3D高斯点云（3D Gaussian Splatting）和NeRF，以其出色的写实表现吸引了大量关注，并引发了将它们与SLAM（同步定位与地图构建）结合的尝试。虽然这些方法能够构建高写实度的地图，但由于它们需要大量的高斯图像进行建图，并且需要相邻图像作为关键帧进行跟踪，因此大规模SLAM仍然面临挑战。我们提出了一种新颖的3D高斯点云SLAM方法——VIGS SLAM，利用RGB-D和IMU传感器的传感器融合，应用于大规模室内环境。为了减少基于3DGS的跟踪计算负担，我们采用了一种基于ICP的跟踪框架，并结合IMU预积分，为准确的姿态估计提供一个良好的初始猜测。我们提出的方法首次提出通过集成IMU传感器测量，基于高斯点云的SLAM可以在大规模环境中有效执行。该提案不仅增强了基于高斯点云的SLAM在超越房间规模场景中的性能，而且在大规模室内环境中实现了与最先进方法相当的SLAM性能。