3DGS-ReLoc: 3D Gaussian Splatting for Map Representation and Visual
  ReLocalization
Peng Jiang Gaurav Pandey Srikanth Saripalli
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.