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.
本文提出了一种新的系统，旨在使用3D高斯平滑进行3D地图制作和视觉重定位。我们的方法利用激光雷达和相机数据，创建环境的准确且视觉上可信的表示。通过利用激光雷达数据启动3D高斯平滑地图的训练，我们的系统构建了既详细又几何上准确的地图。为了减轻过度的GPU内存使用并促进快速的空间查询，我们采用了2D体素图和KD树的组合。这一准备使我们的方法非常适合视觉定位任务，能够通过归一化互相关（NCC）高效识别查询图像与高斯平滑地图渲染图像之间的对应关系。此外，我们使用基于特征的匹配和透视n点（PnP）技术，对查询图像的相机姿态进行了精细化。我们的系统的有效性、适应性和精度通过在KITTI360数据集上的广泛评估得到了证明。