融合惯性数据提升3D高斯溅射SLAM的精度与鲁棒性。
GI-SLAM: Gaussian-Inertial SLAM
- 引入惯性损失函数，增强相机跟踪性能。
- 在EuRoC和TUM-RGBD数据集上达到顶尖实时表现。
- 支持单目、双目、RGBD等多种传感器配置。
3D高斯溅射（3DGS）近期成为密集式同步定位与地图构建（SLAM）中几何与外观的强大表示方法。通过快速、可微分的3D高斯投影，许多3DGS SLAM方法实现了近实时渲染和加速训练。然而，这些方法大多忽略了来自惯性测量单元（IMU）的关键惯性数据。本文提出GI-SLAM，一种新型高斯-惯性SLAM系统，包含增强型相机跟踪模块和基于真实3D高斯的场景表示。该方法引入一个可无缝融入3D高斯溅射深度学习框架的IMU损失，显著提升相机跟踪的准确性、鲁棒性和效率。此外，本系统支持多种传感器配置，包括带或不带IMU的单目、双目及RGBD相机。在EuRoC和TUM-RGBD数据集上，其性能与现有最先进实时方法相当。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) has recently emerged as a powerful representation of geometry and appearance for dense Simultaneous Localization and Mapping (SLAM). Through rapid, differentiable rasterization of 3D Gaussians, many 3DGS SLAM methods achieve near real-time rendering and accelerated training. However, these methods largely overlook inertial data, witch is a critical piece of information collected from the inertial measurement unit (IMU). In this paper, we present GI-SLAM, a novel gaussian-inertial SLAM system which consists of an IMU-enhanced camera tracking module and a realistic 3D Gaussian-based scene representation for mapping. Our method introduces an IMU loss that seamlessly integrates into the deep learning framework underpinning 3D Gaussian Splatting SLAM, effectively enhancing the accuracy, robustness and efficiency of camera tracking. Moreover, our SLAM system supports a wide range of sensor configurations, including monocular, stereo, and RGBD cameras, both with and without IMU integration. Our method achieves competitive performance compared with existing state-of-the-art real-time methods on the EuRoC and TUM-RGBD datasets.
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