GI-SLAM: 高斯-惯性SLAM
GI-SLAM: Gaussian-Inertial SLAM
AI总结：
针对现有3DGS SLAM方法忽略惯性数据的问题，提出GI-SLAM系统，通过引入IMU损失集成到3DGS SLAM深度学习框架，提升跟踪性能，支持多传感器配置，在主流数据集上取得有竞争力的实时SLAM效果。
摘要：
3D Gaussian Splatting（3DGS，三维高斯溅射）近期已成为稠密同时定位与建图（SLAM）中一种强大的几何与外观表征方式。通过对三维高斯进行快速、可微的光栅化，许多3DGS SLAM方法实现了近实时渲染与加速训练。然而，这些方法大多忽略了惯性数据——这是从惯性测量单元（IMU）采集的关键信息。本文提出GI-SLAM，这是一种新型的高斯-惯性SLAM系统，由IMU增强的相机跟踪模块和用于建图的、基于真实感三维高斯的场景表征组成。我们的方法引入了一种IMU损失，可无缝集成到支撑3D Gaussian Splatting SLAM的深度学习框架中，有效提升了相机跟踪的准确性、鲁棒性与效率。此外，我们的SLAM系统支持多种传感器配置，包括单目、双目和RGBD相机，均可选择是否集成IMU。在EuRoC和TUM-RGBD数据集上，我们的方法与现有最先进的实时方法相比取得了具有竞争力的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
