This paper presents a novel system designed for 3D mapping and visual relocalization using 3D Gaussian Splatting. Our proposed method uses LiDAR and camera data to create accurate and visually plausible representations of the environment. By leveraging LiDAR data to initiate the training of the 3D Gaussian Splatting map, our system constructs maps that are both detailed and geometrically accurate. To mitigate excessive GPU memory usage and facilitate rapid spatial queries, we employ a combination of a 2D voxel map and a KD-tree. This preparation makes our method well-suited for visual localization tasks, enabling efficient identification of correspondences between the query image and the rendered image from the Gaussian Splatting map via normalized cross-correlation (NCC). Additionally, we refine the camera pose of the query image using feature-based matching and the Perspective-n-Point (PnP) technique. The effectiveness, adaptability, and precision of our system are demonstrated through extensive evaluation on the KITTI360 dataset.

翻译：本文提出了一种利用3D高斯溅射实现三维建图与视觉重定位的新颖系统。该方法融合激光雷达与相机数据，构建了兼顾视觉真实感与几何准确性的环境表征。通过使用激光雷达数据初始化3D高斯溅射地图的训练，系统所构建的地图兼具细节丰富性与几何精确性。为缓解GPU显存过度占用并支持快速空间查询，我们结合了二维体素地图与KD树数据结构。这一预处理使本方法特别适用于视觉定位任务——通过归一化互相关技术高效建立查询图像与高斯溅射地图渲染图像之间的对应关系。此外，我们还通过基于特征匹配的透视n点法对查询图像的相机位姿进行精化。通过在KITTI360数据集上的全面评估，验证了该系统的有效性、适应性与精度。