VIGS SLAM：基于IMU的大规模3D高斯溅射SLAM
VIGS SLAM: IMU-based Large-Scale 3D Gaussian Splatting SLAM
AI总结：
VIGS SLAM提出一种融合RGB-D与IMU的3D高斯溅射SLAM方法，利用ICP跟踪与IMU预积分，实现大规模室内环境下的高效建图与定位，性能媲美现有最先进方法。
摘要：
近年来，基于辐射场的地图表示方法，如3D高斯溅射和NeRF，因其出色的真实感描绘能力而受到广泛关注，并促使人们尝试将其与SLAM相结合。虽然这些方法能够构建高度逼真的地图，但大规模SLAM仍然是一个挑战，因为它们需要大量的高斯图像用于建图，以及相邻图像作为关键帧用于跟踪。我们提出了一种新颖的3D高斯溅射SLAM方法，VIGS SLAM，该方法利用RGB-D和IMU传感器的融合，适用于大规模室内环境。为了降低基于3DGS的跟踪的计算负担，我们采用了基于ICP的跟踪框架，该框架结合了IMU预积分，为精确的位姿估计提供了良好的初始猜测。我们的方法首次提出，通过集成IMU传感器测量，基于高斯溅射的SLAM可以在大规模环境中有效执行。这一提议不仅提升了高斯溅射SLAM在超越房间尺度场景中的性能，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。
英文摘要：
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.
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