In recent years, 3D Gaussian splatting (3D-GS) has emerged as a novel scene representation approach. However, existing vision-only 3D-GS methods often rely on hand-crafted heuristics for point-cloud densification and face challenges in handling occlusions and high GPU memory and computation consumption. LiDAR-Inertial-Visual (LIV) sensor configuration has demonstrated superior performance in localization and dense mapping by leveraging complementary sensing characteristics: rich texture information from cameras, precise geometric measurements from LiDAR, and high-frequency motion data from IMU. Inspired by this, we propose a novel real-time Gaussian-based simultaneous localization and mapping (SLAM) system. Our map system comprises a global Gaussian map and a sliding window of Gaussians, along with an IESKF-based odometry. The global Gaussian map consists of hash-indexed voxels organized in a recursive octree, effectively covering sparse spatial volumes while adapting to different levels of detail and scales. The Gaussian map is initialized through multi-sensor fusion and optimized with photometric gradients. Our system incrementally maintains a sliding window of Gaussians, significantly reducing GPU computation and memory consumption by only optimizing the map within the sliding window. Moreover, we implement a tightly coupled multi-sensor fusion odometry with an iterative error state Kalman filter (IESKF), leveraging real-time updating and rendering of the Gaussian map. Our system represents the first real-time Gaussian-based SLAM framework deployable on resource-constrained embedded systems, demonstrated on the NVIDIA Jetson Orin NX platform. The framework achieves real-time performance while maintaining robust multi-sensor fusion capabilities.
近年来，三维高斯散点（3D Gaussian Splatting, 3D-GS）作为一种新颖的场景表示方法迅速兴起。然而，现有基于视觉的 3D-GS 方法通常依赖人工设计的启发式规则来实现点云加密，并且在处理遮挡、高 GPU 内存和计算资源消耗方面面临挑战。LiDAR-惯性-视觉（LIV）传感器配置通过结合相机的丰富纹理信息、LiDAR 的精确几何测量以及 IMU 的高频运动数据，已在定位和密集建图方面展现出卓越性能。 受此启发，我们提出了一种基于高斯的实时同步定位与建图（SLAM）系统。我们的地图系统由一个全局高斯地图和一个高斯滑动窗口组成，并结合了基于迭代误差状态卡尔曼滤波器（IESKF）的里程计模块。全局高斯地图通过递归八叉树结构将哈希索引的体素组织起来，有效覆盖稀疏空间体积，同时适应不同的细节层次和尺度。高斯地图通过多传感器融合初始化，并通过光度梯度进行优化。 我们的系统增量维护一个高斯滑动窗口，通过仅优化滑动窗口内的地图，显著降低了 GPU 的计算和内存消耗。此外，我们实现了一种紧耦合的多传感器融合里程计，结合 IESKF，实现了高斯地图的实时更新与渲染。 该系统是第一个可在资源受限嵌入式系统上部署的基于高斯的实时 SLAM 框架，并在 NVIDIA Jetson Orin NX 平台上得以验证。该框架在保持鲁棒多传感器融合能力的同时，实现了实时性能。