Core Viewpoint - The article discusses the development of Gaussian-LIC2, a novel LiDAR-Inertial-Camera 3D Gaussian splatting SLAM system that emphasizes visual quality, geometric accuracy, and real-time performance, addressing challenges in existing systems [52]. Group 1: SLAM Technology Overview - Simultaneous Localization and Mapping (SLAM) is a foundational technology for mixed reality systems and robotic applications, with recent advancements in neural radiance fields (NeRF) and 3D Gaussian splatting (3DGS) leading to a new paradigm in SLAM [3]. - The introduction of 3DGS has improved rendering speed and visual quality, making it more suitable for real-time applications compared to NeRF systems, although challenges remain in outdoor environments [4][6]. Group 2: Challenges in Existing Systems - Current methods often rely on high-density LiDAR data, which can lead to reconstruction issues in LiDAR blind spots or with sparse LiDAR [7]. - There is a tendency to prioritize visual quality over geometric accuracy, which limits the application of SLAM systems in tasks requiring precise geometry, such as obstacle avoidance [7]. - Existing systems primarily focus on rendering quality from trained viewpoints, neglecting the evaluation of new viewpoint synthesis capabilities [7]. Group 3: Gaussian-LIC2 System Contributions - Gaussian-LIC2 is designed to achieve robust and accurate pose estimation while constructing high-fidelity, geometrically accurate 3D Gaussian maps in real-time [8]. - The system consists of two main modules: a tightly coupled LiDAR-Inertial-Camera odometry and a progressive realistic mapping backend based on 3D Gaussian splatting [9]. - It effectively integrates LiDAR, IMU, and camera measurements to enhance odometry stability and accuracy in degraded scenarios [52]. Group 4: Depth Completion and Initialization - To address reconstruction blind spots caused by sparse LiDAR, Gaussian-LIC2 employs an efficient depth completion model that enhances Gaussian initialization coverage [12]. - The system utilizes a sparse depth completion network (SPNet) to predict dense depth maps from sparse LiDAR data and RGB images, achieving robust depth recovery in large-scale environments [31][32]. Group 5: Performance and Evaluation - Extensive experiments on public and self-collected datasets demonstrate the system's superior performance in localization accuracy, novel viewpoint synthesis quality, and real-time capabilities across various LiDAR types [52]. - The system achieves a significant reduction in drift error and maintains high rendering quality, showcasing its potential for practical applications in robotics and augmented reality [47][52].